WP40 RAID: Phase 4 — HAR family (fit/rolling/batched), block bootstrap, R references (highfrequency, rugarch), statsmodels HAR refs, Monte Carlo tests, calendar one-offs, Python/CLI HAR+bootstrap
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
22 changed files +2,956 −35
modified
RESEARCH_STATUS.md
+6 −6
@@ -1,6 +1,6 @@ | ||
| 1 | 1 | # RESEARCH_STATUS.md — RAID (UQO WP 40) |
| 2 | 2 | |
| 3 | −Last update: 2026-11-02 (Phase 3 passed). Legend: ✅ verified by tests/artifacts · 🧪 implemented, validation pending · | |
| 3 | +Last update: 2026-11-02 (Phase 4 passed). Legend: ✅ verified by tests/artifacts · 🧪 implemented, validation pending · | |
| 4 | 4 | 🧭 experimental · ⛔ blocked · ⏳ planned · ✖ not implemented (deliberately). |
| 5 | 5 | |
| 6 | 6 | ## Phases |
@@ -10,7 +10,7 @@ Last update: 2026-11-02 (Phase 3 passed). Legend: ✅ verified by tests/artifact | ||
| 10 | 10 | | 1 Foundation | clean build + tests + docs | ✅ passed 2026-11-02 | `ctest` 26/26 (314 assertions), `pytest` 7/7, `docs/PROGRESS.md` Phase 1 | |
| 11 | 11 | | 2 Data | offline synthetic tests + opt-in live tests | ✅ passed 2026-11-02 | offline `[findata]` 4/4, live `[live]` 2/2 (60 assertions), `docs/operations/findata.md` | |
| 12 | 12 | | 3 Numerical primitives | accuracy + measured break-even | ✅ passed 2026-11-02 | `[metal]` 6 cases / 6 411 assertions; `experiments/results/bench_primitives_summary.json` (Metal wins: fused realized ≥ 39 k elements up to 3.4×, GARCH NLL P=64 up to 7.9×; CPU wins: rolling sums, GARCH P=1, small panels) | |
| 13 | −| 4 Econometrics (Tier A) | references + Monte Carlo | ⏳ | — | | |
| 13 | +| 4 Econometrics (Tier A) | references + Monte Carlo | ✅ passed 2026-11-02 | R (highfrequency/rugarch) + statsmodels/arch references, `tests/simulation` Monte Carlo, `docs/methods/registry.md` | | |
| 14 | 14 | | 5 Extensions (Tier B) | per-feature specs + tests | ⏳ | — | |
| 15 | 15 | | 6 Benchmarking E1–E7 | every claim traceable | ⏳ | — | |
| 16 | 16 | | 7 Manuscript | reproducible build, no placeholders | ⏳ | — | |
@@ -22,9 +22,9 @@ Last update: 2026-11-02 (Phase 3 passed). Legend: ✅ verified by tests/artifact | ||
| 22 | 22 | | returns (log/arith), lag/lead, rolling windows, calendar-aware aggregation | A | ✅ | ⏳ (Phase 3) | numpy; exchange_calendars (calendar exact 2000–2026) | ✅ CPU | |
| 23 | 23 | | RV, BPV, RSV±, RQ, jump proxy, TriPV, rCov/rCorr | A | ✅ (numpy truth 1e−12) | ✅ fused kernel 1e−6 vs FP64 | highfrequency 1.0.3 (R comparison Phase 4) | ✅ CPU | |
| 24 | 24 | | OLS/WLS/ridge, HC0–3, HAC, cluster, rolling OLS | A | ✅ (statsmodels 1e−9) | ✅ sufficient statistics 1e−6 (k ≤ 8) | statsmodels 0.15, scipy 1.18 | ✅ CPU | |
| 25 | −| HAR-RV, log-HAR, HAR-X, HARQ | A | ⏳ | ⏳ | highfrequency `HARmodel`, statsmodels | ⏳ | | |
| 25 | +| HAR-RV, log-HAR, HAR-X, HARQ, rolling, batched | A | ✅ (highfrequency 1e−8, statsmodels 1e−9) | ✅ batched sufficient statistics | highfrequency 1.0.3, statsmodels 0.15 | ✅ | | |
| 26 | 26 | | GARCH(1,1), GJR, EGARCH (N, t), QMLE/Hessian/OPG SE, forecasts | A | ✅ (arch 8.0: loglik 5e−4, params 5 %) | ✅ batched NLL 2e−5 (grids) | arch 8.0 ✅, rugarch 1.5.6 (Phase 4) | ✅ CPU | |
| 27 | −| Bootstrap (stationary / circular block), Monte Carlo | A | ⏳ | ⏳ | arch.bootstrap | ⏳ | | |
| 27 | +| Bootstrap (stationary / circular / iid), Monte Carlo harness | A | ✅ (theory + MC coverage) | ⏳ GPU replicas (E5) | theory (Politis–Romano) | ✅ | | |
| 28 | 28 | | VAR, GFEVD, Diebold–Yilmaz | B | ⏳ | ⏳ | statsmodels VAR | ⏳ | |
| 29 | 29 | | Realized GARCH, HEAVY, DCC, PCA/shrinkage, Kalman | B | ✖ until Tier A passes | | | ⏳ | |
| 30 | 30 | | Pre-averaging, realized kernels, MSRV, BNS test | C | ✖ (1-min bars) | | highfrequency | 🧭 only if validated | |
@@ -35,5 +35,5 @@ Last update: 2026-11-02 (Phase 3 passed). Legend: ✅ verified by tests/artifact | ||
| 35 | 35 | not tested unless they come back. |
| 36 | 36 | |
| 37 | 37 | ## Next safe action |
| 38 | −Phase 4: HAR family (fit, rolling, batched via GPU sufficient statistics), block bootstrap, R references | |
| 39 | −(highfrequency / rugarch), Monte Carlo (bias/RMSE/coverage), gradient checks, registry completion. | |
| 38 | +Phase 5 (Tier B, selected for research value): VAR(p) + generalized FEVD + Diebold–Yilmaz connectedness (rolling, batched | |
| 39 | +realized-covariance inputs) for E4; shrinkage covariance; then Phase 6 experiments E1–E7. | |
modified
bindings/python/raid_py.cpp
+29 −0
@@ -12,6 +12,9 @@ | ||
| 12 | 12 | #include "raid/estimators/ols.hpp" |
| 13 | 13 | #include "raid/estimators/realized.hpp" |
| 14 | 14 | #include "raid/models/garch.hpp" |
| 15 | +#include "raid/estimators/har.hpp" | |
| 16 | +#include "raid/statistics/bootstrap.hpp" | |
| 17 | +#include "raid/statistics/reductions.hpp" | |
| 15 | 18 | #include "raid/data/bar_series.hpp" |
| 16 | 19 | #include "raid/data/calendar.hpp" |
| 17 | 20 | #include "raid/data/returns.hpp" |
@@ -94,6 +97,32 @@ NB_MODULE(_core, m) { | ||
| 94 | 97 | |
| 95 | 98 | m.def("calendar_sessions", [](const std::string& a, const std::string& b) { MarketCalendar cal; std::vector<std::string> out; for (auto d : cal.sessions(date_from_iso(a), date_from_iso(b))) out.push_back(d.iso()); return out; }); |
| 96 | 99 | |
| 100 | + // ---- HAR family ---- | |
| 101 | + m.def("har_fit", [](F64 rv, std::vector<usize> lags, int horizon, bool log_model, bool harq, std::optional<F64> rq, const std::string& cov, int hac_lags, const std::string& target) { | |
| 102 | + est::HarSpec s; s.lags = lags; s.horizon = horizon; s.log = log_model; s.harq = harq; s.cov.type = stats::covtype_from_string(cov); s.cov.hac_lags = hac_lags; s.target = target == "single" ? est::HarTarget::single : est::HarTarget::average; | |
| 103 | + std::span<const f64> rvs(rv.data(), rv.shape(0)); std::span<const f64> rqs; if (rq) rqs = std::span<const f64>(rq->data(), rq->shape(0)); | |
| 104 | + json out; { nb::gil_scoped_release rel; auto e = est::har_fit(rvs, s, rqs); out = e.to_json(); out["forecast_next"] = est::har_forecast(e, rvs, s, rqs); } | |
| 105 | + return json_to_py(out); | |
| 106 | + }, nb::arg("rv"), nb::arg("lags") = std::vector<usize>{1, 5, 22}, nb::arg("horizon") = 1, nb::arg("log") = false, nb::arg("harq") = false, nb::arg("rq") = nb::none(), nb::arg("cov") = "hac", nb::arg("hac_lags") = -1, nb::arg("target") = "average"); | |
| 107 | + m.def("har_rolling", [](F64 rv, std::vector<usize> lags, int horizon, bool log_model, usize window, usize step, bool harq, std::optional<F64> rq) { | |
| 108 | + est::HarSpec s; s.lags = lags; s.horizon = horizon; s.log = log_model; s.harq = harq; std::span<const f64> rvs(rv.data(), rv.shape(0)); std::span<const f64> rqs; if (rq) rqs = std::span<const f64>(rq->data(), rq->shape(0)); | |
| 109 | + est::HarRolling r; { nb::gil_scoped_release rel; r = est::har_rolling(rvs, s, window, step, rqs); } | |
| 110 | + nb::dict d; std::vector<int64_t> o(r.origin.begin(), r.origin.end()); d["origin"] = to_np_i64(std::move(o)); d["forecast"] = to_np(r.forecast); d["actual"] = to_np(r.actual); d["n_fits"] = r.n_fits; d["seconds"] = r.seconds; | |
| 111 | + auto l = est::forecast_loss(r.actual, r.forecast); d["mse"] = l.mse; d["qlike"] = l.qlike; d["n_loss"] = l.n; return d; | |
| 112 | + }, nb::arg("rv"), nb::arg("lags") = std::vector<usize>{1, 5, 22}, nb::arg("horizon") = 1, nb::arg("log") = false, nb::arg("window") = 1000, nb::arg("step") = 1, nb::arg("harq") = false, nb::arg("rq") = nb::none()); | |
| 113 | + m.def("har_batch", [](F64 rv, I64 off, std::vector<usize> lags, int horizon, bool log_model, const std::string& backend_s) { | |
| 114 | + est::HarSpec s; s.lags = lags; s.horizon = horizon; s.log = log_model; ExecutionPlan p; p.backend = backend_from_string(backend_s); | |
| 115 | + est::HarBatchResult r; { nb::gil_scoped_release rel; r = est::har_batch(std::span<const f64>(rv.data(), rv.shape(0)), std::span<const i64>(off.data(), off.shape(0)), s, p); } | |
| 116 | + nb::list fits; for (auto& e : r.fits) fits.append(json_to_py(e.to_json())); nb::dict d; d["fits"] = fits; d["backend"] = std::string(to_string(r.stats.backend)); d["precision"] = std::string(to_string(r.stats.precision)); d["wall_seconds"] = r.stats.wall_seconds; d["device_seconds"] = r.stats.device_seconds; return d; | |
| 117 | + }, nb::arg("rv"), nb::arg("offsets"), nb::arg("lags") = std::vector<usize>{1, 5, 22}, nb::arg("horizon") = 1, nb::arg("log") = false, nb::arg("backend") = "auto"); | |
| 118 | + // ---- bootstrap ---- | |
| 119 | + m.def("bootstrap", [](F64 x, const std::string& stat, const std::string& scheme, double mean_block, usize replicas, uint64_t seed, double level) { | |
| 120 | + stats::BootstrapOptions o; o.scheme = scheme == "circular" ? stats::BootstrapScheme::circular_block : (scheme == "iid" ? stats::BootstrapScheme::iid : stats::BootstrapScheme::stationary); o.mean_block = mean_block; o.replicas = replicas; o.seed = seed; o.ci_level = level; | |
| 121 | + std::function<f64(std::span<const f64>)> f; if (stat == "mean") f = [](std::span<const f64> v) { return stats::mean<f64>(v); }; else if (stat == "rv" || stat == "sum_squares") f = [](std::span<const f64> v) { return stats::sum_squares<f64>(v); }; else if (stat == "variance") f = [](std::span<const f64> v) { return stats::variance<f64>(v); }; else throw RaidError(ErrorCode::invalid_argument, "stat must be mean|rv|variance"); | |
| 122 | + stats::BootstrapResult r; { nb::gil_scoped_release rel; r = stats::bootstrap(std::span<const f64>(x.data(), x.shape(0)), f, o); } | |
| 123 | + nb::dict d; d["point"] = r.point; d["se"] = r.se; d["ci_lo"] = r.ci_lo; d["ci_hi"] = r.ci_hi; d["bias"] = r.bias; d["failures"] = r.failures; d["block_length"] = r.block_length; d["statistics"] = to_np(r.statistics); d["seconds"] = r.seconds; return d; | |
| 124 | + }, nb::arg("x"), nb::arg("stat") = "mean", nb::arg("scheme") = "stationary", nb::arg("mean_block") = 0.0, nb::arg("replicas") = 999, nb::arg("seed") = 20261102, nb::arg("level") = 0.95); | |
| 125 | + | |
| 97 | 126 | // ---- FinData client (keys from FINDATA_API_KEY / FINDATA_API_KEY_FILE; never passed through Python) ---- |
| 98 | 127 | m.def("findata_bars", [](const std::string& symbol, const std::string& interval, const std::string& start, const std::string& end, const std::string& adjustment, const std::string& cache_dir, bool offline) { |
| 99 | 128 | findata::Config cfg = findata::Config::from_env(); if (!cache_dir.empty()) cfg.cache_dir = cache_dir; cfg.offline = offline; findata::Client cl(cfg); |
modified
cli/raid.cpp
+10 −0
@@ -18,6 +18,7 @@ | ||
| 18 | 18 | #include "raid/estimators/realized.hpp" |
| 19 | 19 | #include "raid/estimators/ols.hpp" |
| 20 | 20 | #include "raid/models/garch.hpp" |
| 21 | +#include "raid/estimators/har.hpp" | |
| 21 | 22 | #ifdef RAID_WITH_FINDATA |
| 22 | 23 | #include "raid/data/findata.hpp" |
| 23 | 24 | #endif |
@@ -34,6 +35,7 @@ void usage() { | ||
| 34 | 35 | " raid estimate realized <bars> [--backend auto|cpu|metal] [--bpv-no-fs] per-session realized measures\n" |
| 35 | 36 | " raid estimate garch <bars> [--model garch|gjr|egarch] [--dist gaussian|student_t] [--horizon H] [--returns daily|intraday]\n" |
| 36 | 37 | " raid estimate ols <csv y,x1,x2...> [--cov classical|hc0..hc3|hac|cluster] [--lags L]\n" |
| 38 | + " raid estimate har <bars> [--horizon h] [--log] [--harq] [--window W --step S] daily RV from 1m bars → HAR fit (+ rolling forecasts)\n" | |
| 37 | 39 | " raid data ... FinData access (Phase 2)\n" |
| 38 | 40 | " raid benchmark ... benchmark harness (Phase 3+)\n", RAID_VERSION); |
| 39 | 41 | } |
@@ -89,6 +91,14 @@ int cmd_estimate(const std::vector<std::string>& a) { | ||
| 89 | 91 | try { auto e = models::garch_fit(r, go); out[sym] = e.to_json(); } catch (const RaidError& ex) { out[sym] = {{"error", ex.what()}}; } } |
| 90 | 92 | std::cout << out.dump(1) << "\n"; return 0; |
| 91 | 93 | } |
| 94 | + if (what == "har") { | |
| 95 | + auto bars = load_bars(path); MarketCalendar cal; json out = json::object(); est::HarSpec s; s.horizon = std::stoi(arg(a, "--horizon", "1")); s.log = flag(a, "--log"); s.harq = flag(a, "--harq"); | |
| 96 | + for (auto& [sym, b0] : bars) { auto b = b0; b.normalize(); auto reg = b.filter_session(cal, SessionPolicy::regular); auto ir = data::intraday_returns(reg); est::RealizedOptions ro; ro.plan = plan; auto m = est::realized_measures(ir, ro); | |
| 97 | + try { auto e = est::har_fit(m.rv, s, m.rq); json j = e.to_json(); j["forecast_next"] = est::har_forecast(e, m.rv, s, m.rq); j["n_sessions"] = m.rv.size(); | |
| 98 | + if (!arg(a, "--window").empty()) { auto r = est::har_rolling(m.rv, s, static_cast<usize>(std::stoul(arg(a, "--window"))), static_cast<usize>(std::stoul(arg(a, "--step", "1"))), m.rq); auto l = est::forecast_loss(r.actual, r.forecast); j["rolling"] = {{"n_fits", r.n_fits}, {"mse", l.mse}, {"qlike", l.qlike}, {"seconds", r.seconds}}; } | |
| 99 | + out[sym] = j; } catch (const RaidError& ex) { out[sym] = {{"error", ex.what()}}; } } | |
| 100 | + std::cout << out.dump(1) << "\n"; return 0; | |
| 101 | + } | |
| 92 | 102 | if (what == "ols") { |
| 93 | 103 | std::ifstream f(path); RAID_REQUIRE(f.good(), io, "cannot open " + path); std::string line; std::getline(f, line); |
| 94 | 104 | std::vector<std::string> names; { usize s = 0; for (usize i = 0; i <= line.size(); ++i) if (i == line.size() || line[i] == ',') { names.push_back(line.substr(s, i - s)); s = i + 1; } } |
added
datasets/fixtures/har_rv.csv
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@@ -0,0 +1,1501 @@ | ||
| 1 | +rv,rq | |
| 2 | +0.00010000000000000009,1.4405505785837637e-08 | |
| 3 | +6.3328360608245278e-05,5.8457908532148236e-09 | |
| 4 | +4.735392494116195e-05,2.8915512147545496e-09 | |
| 5 | +4.3772372296844666e-05,2.3507104508857125e-09 | |
| 6 | +7.6272793543610479e-05,7.7019251835855382e-09 | |
| 7 | +5.4619899802699215e-05,2.9980959338279616e-09 | |
| 8 | +4.3441504925126849e-05,2.2491120759492536e-09 | |
| 9 | +4.7679144275385566e-05,2.5789861759974905e-09 | |
| 10 | +0.00010372970093747665,1.6072885476575217e-08 | |
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| 15 | +0.00010568360711704954,1.501391131310545e-08 | |
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added
datasets/fixtures/reference_r.json
+275 −0
@@ -0,0 +1,275 @@ | ||
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| 176 | + "omega": 0.046968370528598521, | |
| 177 | + "alpha1": 0.045332156957475216, | |
| 178 | + "beta1": 0.89386846098192896 | |
| 179 | + }, | |
| 180 | + "se": { | |
| 181 | + "mu": 0.015720112893335601, | |
| 182 | + "omega": 0.020057089545485348, | |
| 183 | + "alpha1": 0.012340636889580047, | |
| 184 | + "beta1": 0.035421075697137025 | |
| 185 | + }, | |
| 186 | + "robust_se": { | |
| 187 | + "mu": 0.017043974680386417, | |
| 188 | + "omega": 0.020836549077324372, | |
| 189 | + "alpha1": 0.014636746334251497, | |
| 190 | + "beta1": 0.038358083830533872 | |
| 191 | + }, | |
| 192 | + "loglik": -3841.0200748070997, | |
| 193 | + "converged": true, | |
| 194 | + "forecast_h5": [0.94763507510028322, 0.93698781864383818, 0.92698790880058568, 0.91759598729646441, 0.90877508881615554] | |
| 195 | + }, | |
| 196 | + "gjrGARCH.norm": { | |
| 197 | + "coef": { | |
| 198 | + "mu": -0.01921714832040566, | |
| 199 | + "omega": 0.049606222107306731, | |
| 200 | + "alpha1": 0.041772517207855783, | |
| 201 | + "beta1": 0.88881536902228075, | |
| 202 | + "gamma1": 0.010749578214706531 | |
| 203 | + }, | |
| 204 | + "se": { | |
| 205 | + "mu": 0.015788877242223259, | |
| 206 | + "omega": 0.020732037955024357, | |
| 207 | + "alpha1": 0.012738012140778642, | |
| 208 | + "beta1": 0.036358574231286772, | |
| 209 | + "gamma1": 0.01681215666556346 | |
| 210 | + }, | |
| 211 | + "robust_se": { | |
| 212 | + "mu": 0.016963187542588787, | |
| 213 | + "omega": 0.021997526667836047, | |
| 214 | + "alpha1": 0.012381109622775522, | |
| 215 | + "beta1": 0.040044430330484673, | |
| 216 | + "gamma1": 0.017230012922633861 | |
| 217 | + }, | |
| 218 | + "loglik": -3840.8034370254973, | |
| 219 | + "converged": true, | |
| 220 | + "forecast_h5": [0.97755196253582011, 0.96455837224266494, 0.95239685670965857, 0.94101413209524076, 0.93036032671251323] | |
| 221 | + }, | |
| 222 | + "eGARCH.norm": { | |
| 223 | + "coef": { | |
| 224 | + "mu": -0.022966085734094945, | |
| 225 | + "omega": -0.012779542213507692, | |
| 226 | + "alpha1": -0.00076829518133545631, | |
| 227 | + "beta1": 0.9532136736359611, | |
| 228 | + "gamma1": 0.089834797367789293 | |
| 229 | + }, | |
| 230 | + "se": { | |
| 231 | + "mu": 0.015731171060962931, | |
| 232 | + "omega": 0.020613569207631303, | |
| 233 | + "alpha1": 0.013206355990077282, | |
| 234 | + "beta1": 0.073113474575883089, | |
| 235 | + "gamma1": 0.060626191829447999 | |
| 236 | + }, | |
| 237 | + "robust_se": { | |
| 238 | + "mu": 0.017146433037466612, | |
| 239 | + "omega": 0.088337872440952633, | |
| 240 | + "alpha1": 0.040033713535164918, | |
| 241 | + "beta1": 0.31484565953594218, | |
| 242 | + "gamma1": 0.25365796252326678 | |
| 243 | + }, | |
| 244 | + "loglik": -3840.6120305950508, | |
| 245 | + "converged": true, | |
| 246 | + "forecast_h5": [0.94230544276184092, 0.93293006812265378, 0.92408018148567717, 0.91572251144926264, 0.90782624332712947] | |
| 247 | + }, | |
| 248 | + "sGARCH.std": { | |
| 249 | + "coef": { | |
| 250 | + "mu": -0.018816592659429972, | |
| 251 | + "omega": 0.046191939728617612, | |
| 252 | + "alpha1": 0.044962812250873993, | |
| 253 | + "beta1": 0.89536963039369999, | |
| 254 | + "shape": 99.999873765090996 | |
| 255 | + }, | |
| 256 | + "se": { | |
| 257 | + "mu": 0.015733116019376509, | |
| 258 | + "omega": 0.020181074534464737, | |
| 259 | + "alpha1": 0.012481787204476544, | |
| 260 | + "beta1": 0.035670675658710446, | |
| 261 | + "shape": 68.929603216724544 | |
| 262 | + }, | |
| 263 | + "robust_se": { | |
| 264 | + "mu": 0.017107427281700522, | |
| 265 | + "omega": 0.02085183337489558, | |
| 266 | + "alpha1": 0.014641888146575781, | |
| 267 | + "beta1": 0.038396705016878786, | |
| 268 | + "shape": 24.403762618119906 | |
| 269 | + }, | |
| 270 | + "loglik": -3841.7221654386312, | |
| 271 | + "converged": true, | |
| 272 | + "forecast_h5": [0.94772578579531741, 0.93736924284277667, 0.92763064951086072, 0.9184731342551381, 0.90986202556616935] | |
| 273 | + } | |
| 274 | + } | |
| 275 | +} | |
modified
datasets/fixtures/reference_values.json
+676 −9
@@ -1,6 +1,579 @@ | ||
| 1 | 1 | { |
| 2 | 2 | "xnys": { |
| 3 | − "error": "Parameter `start` receieved as '2000-01-01 00:00:00' although cannot be earlier than the first session of calendar 'XNYS' ('2000-01-03 00:00:00')." | |
| 3 | + "n_sessions_2000_2026": 6790, | |
| 4 | + "sessions_2024": [ | |
| 5 | + "2024-01-02", | |
| 6 | + "2024-01-03", | |
| 7 | + "2024-01-04", | |
| 8 | + "2024-01-05", | |
| 9 | + "2024-01-08", | |
| 10 | + "2024-01-09", | |
| 11 | + "2024-01-10", | |
| 12 | + "2024-01-11", | |
| 13 | + "2024-01-12", | |
| 14 | + "2024-01-16", | |
| 15 | + "2024-01-17", | |
| 16 | + "2024-01-18", | |
| 17 | + "2024-01-19", | |
| 18 | + "2024-01-22", | |
| 19 | + "2024-01-23", | |
| 20 | + "2024-01-24", | |
| 21 | + "2024-01-25", | |
| 22 | + "2024-01-26", | |
| 23 | + "2024-01-29", | |
| 24 | + "2024-01-30", | |
| 25 | + "2024-01-31", | |
| 26 | + "2024-02-01", | |
| 27 | + "2024-02-02", | |
| 28 | + "2024-02-05", | |
| 29 | + "2024-02-06", | |
| 30 | + "2024-02-07", | |
| 31 | + "2024-02-08", | |
| 32 | + "2024-02-09", | |
| 33 | + "2024-02-12", | |
| 34 | + "2024-02-13", | |
| 35 | + "2024-02-14", | |
| 36 | + "2024-02-15", | |
| 37 | + "2024-02-16", | |
| 38 | + "2024-02-20", | |
| 39 | + "2024-02-21", | |
| 40 | + "2024-02-22", | |
| 41 | + "2024-02-23", | |
| 42 | + "2024-02-26", | |
| 43 | + "2024-02-27", | |
| 44 | + "2024-02-28", | |
| 45 | + "2024-02-29", | |
| 46 | + "2024-03-01", | |
| 47 | + "2024-03-04", | |
| 48 | + "2024-03-05", | |
| 49 | + "2024-03-06", | |
| 50 | + "2024-03-07", | |
| 51 | + "2024-03-08", | |
| 52 | + "2024-03-11", | |
| 53 | + "2024-03-12", | |
| 54 | + "2024-03-13", | |
| 55 | + "2024-03-14", | |
| 56 | + "2024-03-15", | |
| 57 | + "2024-03-18", | |
| 58 | + "2024-03-19", | |
| 59 | + "2024-03-20", | |
| 60 | + "2024-03-21", | |
| 61 | + "2024-03-22", | |
| 62 | + "2024-03-25", | |
| 63 | + "2024-03-26", | |
| 64 | + "2024-03-27", | |
| 65 | + "2024-03-28", | |
| 66 | + "2024-04-01", | |
| 67 | + "2024-04-02", | |
| 68 | + "2024-04-03", | |
| 69 | + "2024-04-04", | |
| 70 | + "2024-04-05", | |
| 71 | + "2024-04-08", | |
| 72 | + "2024-04-09", | |
| 73 | + "2024-04-10", | |
| 74 | + "2024-04-11", | |
| 75 | + "2024-04-12", | |
| 76 | + "2024-04-15", | |
| 77 | + "2024-04-16", | |
| 78 | + "2024-04-17", | |
| 79 | + "2024-04-18", | |
| 80 | + "2024-04-19", | |
| 81 | + "2024-04-22", | |
| 82 | + "2024-04-23", | |
| 83 | + "2024-04-24", | |
| 84 | + "2024-04-25", | |
| 85 | + "2024-04-26", | |
| 86 | + "2024-04-29", | |
| 87 | + "2024-04-30", | |
| 88 | + "2024-05-01", | |
| 89 | + "2024-05-02", | |
| 90 | + "2024-05-03", | |
| 91 | + "2024-05-06", | |
| 92 | + "2024-05-07", | |
| 93 | + "2024-05-08", | |
| 94 | + "2024-05-09", | |
| 95 | + "2024-05-10", | |
| 96 | + "2024-05-13", | |
| 97 | + "2024-05-14", | |
| 98 | + "2024-05-15", | |
| 99 | + "2024-05-16", | |
| 100 | + "2024-05-17", | |
| 101 | + "2024-05-20", | |
| 102 | + "2024-05-21", | |
| 103 | + "2024-05-22", | |
| 104 | + "2024-05-23", | |
| 105 | + "2024-05-24", | |
| 106 | + "2024-05-28", | |
| 107 | + "2024-05-29", | |
| 108 | + "2024-05-30", | |
| 109 | + "2024-05-31", | |
| 110 | + "2024-06-03", | |
| 111 | + "2024-06-04", | |
| 112 | + "2024-06-05", | |
| 113 | + "2024-06-06", | |
| 114 | + "2024-06-07", | |
| 115 | + "2024-06-10", | |
| 116 | + "2024-06-11", | |
| 117 | + "2024-06-12", | |
| 118 | + "2024-06-13", | |
| 119 | + "2024-06-14", | |
| 120 | + "2024-06-17", | |
| 121 | + "2024-06-18", | |
| 122 | + "2024-06-20", | |
| 123 | + "2024-06-21", | |
| 124 | + "2024-06-24", | |
| 125 | + "2024-06-25", | |
| 126 | + "2024-06-26", | |
| 127 | + "2024-06-27", | |
| 128 | + "2024-06-28", | |
| 129 | + "2024-07-01", | |
| 130 | + "2024-07-02", | |
| 131 | + "2024-07-03", | |
| 132 | + "2024-07-05", | |
| 133 | + "2024-07-08", | |
| 134 | + "2024-07-09", | |
| 135 | + "2024-07-10", | |
| 136 | + "2024-07-11", | |
| 137 | + "2024-07-12", | |
| 138 | + "2024-07-15", | |
| 139 | + "2024-07-16", | |
| 140 | + "2024-07-17", | |
| 141 | + "2024-07-18", | |
| 142 | + "2024-07-19", | |
| 143 | + "2024-07-22", | |
| 144 | + "2024-07-23", | |
| 145 | + "2024-07-24", | |
| 146 | + "2024-07-25", | |
| 147 | + "2024-07-26", | |
| 148 | + "2024-07-29", | |
| 149 | + "2024-07-30", | |
| 150 | + "2024-07-31", | |
| 151 | + "2024-08-01", | |
| 152 | + "2024-08-02", | |
| 153 | + "2024-08-05", | |
| 154 | + "2024-08-06", | |
| 155 | + "2024-08-07", | |
| 156 | + "2024-08-08", | |
| 157 | + "2024-08-09", | |
| 158 | + "2024-08-12", | |
| 159 | + "2024-08-13", | |
| 160 | + "2024-08-14", | |
| 161 | + "2024-08-15", | |
| 162 | + "2024-08-16", | |
| 163 | + "2024-08-19", | |
| 164 | + "2024-08-20", | |
| 165 | + "2024-08-21", | |
| 166 | + "2024-08-22", | |
| 167 | + "2024-08-23", | |
| 168 | + "2024-08-26", | |
| 169 | + "2024-08-27", | |
| 170 | + "2024-08-28", | |
| 171 | + "2024-08-29", | |
| 172 | + "2024-08-30", | |
| 173 | + "2024-09-03", | |
| 174 | + "2024-09-04", | |
| 175 | + "2024-09-05", | |
| 176 | + "2024-09-06", | |
| 177 | + "2024-09-09", | |
| 178 | + "2024-09-10", | |
| 179 | + "2024-09-11", | |
| 180 | + "2024-09-12", | |
| 181 | + "2024-09-13", | |
| 182 | + "2024-09-16", | |
| 183 | + "2024-09-17", | |
| 184 | + "2024-09-18", | |
| 185 | + "2024-09-19", | |
| 186 | + "2024-09-20", | |
| 187 | + "2024-09-23", | |
| 188 | + "2024-09-24", | |
| 189 | + "2024-09-25", | |
| 190 | + "2024-09-26", | |
| 191 | + "2024-09-27", | |
| 192 | + "2024-09-30", | |
| 193 | + "2024-10-01", | |
| 194 | + "2024-10-02", | |
| 195 | + "2024-10-03", | |
| 196 | + "2024-10-04", | |
| 197 | + "2024-10-07", | |
| 198 | + "2024-10-08", | |
| 199 | + "2024-10-09", | |
| 200 | + "2024-10-10", | |
| 201 | + "2024-10-11", | |
| 202 | + "2024-10-14", | |
| 203 | + "2024-10-15", | |
| 204 | + "2024-10-16", | |
| 205 | + "2024-10-17", | |
| 206 | + "2024-10-18", | |
| 207 | + "2024-10-21", | |
| 208 | + "2024-10-22", | |
| 209 | + "2024-10-23", | |
| 210 | + "2024-10-24", | |
| 211 | + "2024-10-25", | |
| 212 | + "2024-10-28", | |
| 213 | + "2024-10-29", | |
| 214 | + "2024-10-30", | |
| 215 | + "2024-10-31", | |
| 216 | + "2024-11-01", | |
| 217 | + "2024-11-04", | |
| 218 | + "2024-11-05", | |
| 219 | + "2024-11-06", | |
| 220 | + "2024-11-07", | |
| 221 | + "2024-11-08", | |
| 222 | + "2024-11-11", | |
| 223 | + "2024-11-12", | |
| 224 | + "2024-11-13", | |
| 225 | + "2024-11-14", | |
| 226 | + "2024-11-15", | |
| 227 | + "2024-11-18", | |
| 228 | + "2024-11-19", | |
| 229 | + "2024-11-20", | |
| 230 | + "2024-11-21", | |
| 231 | + "2024-11-22", | |
| 232 | + "2024-11-25", | |
| 233 | + "2024-11-26", | |
| 234 | + "2024-11-27", | |
| 235 | + "2024-11-29", | |
| 236 | + "2024-12-02", | |
| 237 | + "2024-12-03", | |
| 238 | + "2024-12-04", | |
| 239 | + "2024-12-05", | |
| 240 | + "2024-12-06", | |
| 241 | + "2024-12-09", | |
| 242 | + "2024-12-10", | |
| 243 | + "2024-12-11", | |
| 244 | + "2024-12-12", | |
| 245 | + "2024-12-13", | |
| 246 | + "2024-12-16", | |
| 247 | + "2024-12-17", | |
| 248 | + "2024-12-18", | |
| 249 | + "2024-12-19", | |
| 250 | + "2024-12-20", | |
| 251 | + "2024-12-23", | |
| 252 | + "2024-12-24", | |
| 253 | + "2024-12-26", | |
| 254 | + "2024-12-27", | |
| 255 | + "2024-12-30", | |
| 256 | + "2024-12-31" | |
| 257 | + ], | |
| 258 | + "early_closes_2000_2026": [ | |
| 259 | + "2000-07-03", | |
| 260 | + "2000-11-24", | |
| 261 | + "2001-07-03", | |
| 262 | + "2001-11-23", | |
| 263 | + "2001-12-24", | |
| 264 | + "2002-07-05", | |
| 265 | + "2002-11-29", | |
| 266 | + "2002-12-24", | |
| 267 | + "2003-07-03", | |
| 268 | + "2003-11-28", | |
| 269 | + "2003-12-24", | |
| 270 | + "2003-12-26", | |
| 271 | + "2004-11-26", | |
| 272 | + "2005-11-25", | |
| 273 | + "2006-07-03", | |
| 274 | + "2006-11-24", | |
| 275 | + "2007-07-03", | |
| 276 | + "2007-11-23", | |
| 277 | + "2007-12-24", | |
| 278 | + "2008-07-03", | |
| 279 | + "2008-11-28", | |
| 280 | + "2008-12-24", | |
| 281 | + "2009-11-27", | |
| 282 | + "2009-12-24", | |
| 283 | + "2010-11-26", | |
| 284 | + "2011-11-25", | |
| 285 | + "2012-07-03", | |
| 286 | + "2012-11-23", | |
| 287 | + "2012-12-24", | |
| 288 | + "2013-07-03", | |
| 289 | + "2013-11-29", | |
| 290 | + "2013-12-24", | |
| 291 | + "2014-07-03", | |
| 292 | + "2014-11-28", | |
| 293 | + "2014-12-24", | |
| 294 | + "2015-11-27", | |
| 295 | + "2015-12-24", | |
| 296 | + "2016-11-25", | |
| 297 | + "2017-07-03", | |
| 298 | + "2017-11-24", | |
| 299 | + "2018-07-03", | |
| 300 | + "2018-11-23", | |
| 301 | + "2018-12-24", | |
| 302 | + "2019-07-03", | |
| 303 | + "2019-11-29", | |
| 304 | + "2019-12-24", | |
| 305 | + "2020-11-27", | |
| 306 | + "2020-12-24", | |
| 307 | + "2021-11-26", | |
| 308 | + "2022-11-25", | |
| 309 | + "2023-07-03", | |
| 310 | + "2023-11-24", | |
| 311 | + "2024-07-03", | |
| 312 | + "2024-11-29", | |
| 313 | + "2024-12-24", | |
| 314 | + "2025-07-03", | |
| 315 | + "2025-11-28", | |
| 316 | + "2025-12-24", | |
| 317 | + "2026-11-27", | |
| 318 | + "2026-12-24" | |
| 319 | + ], | |
| 320 | + "holidays_2000_2026": [ | |
| 321 | + "2000-01-17", | |
| 322 | + "2000-02-21", | |
| 323 | + "2000-04-21", | |
| 324 | + "2000-05-29", | |
| 325 | + "2000-07-04", | |
| 326 | + "2000-09-04", | |
| 327 | + "2000-11-23", | |
| 328 | + "2000-12-25", | |
| 329 | + "2001-01-01", | |
| 330 | + "2001-01-15", | |
| 331 | + "2001-02-19", | |
| 332 | + "2001-04-13", | |
| 333 | + "2001-05-28", | |
| 334 | + "2001-07-04", | |
| 335 | + "2001-09-03", | |
| 336 | + "2001-09-11", | |
| 337 | + "2001-09-12", | |
| 338 | + "2001-09-13", | |
| 339 | + "2001-09-14", | |
| 340 | + "2001-11-22", | |
| 341 | + "2001-12-25", | |
| 342 | + "2002-01-01", | |
| 343 | + "2002-01-21", | |
| 344 | + "2002-02-18", | |
| 345 | + "2002-03-29", | |
| 346 | + "2002-05-27", | |
| 347 | + "2002-07-04", | |
| 348 | + "2002-09-02", | |
| 349 | + "2002-11-28", | |
| 350 | + "2002-12-25", | |
| 351 | + "2003-01-01", | |
| 352 | + "2003-01-20", | |
| 353 | + "2003-02-17", | |
| 354 | + "2003-04-18", | |
| 355 | + "2003-05-26", | |
| 356 | + "2003-07-04", | |
| 357 | + "2003-09-01", | |
| 358 | + "2003-11-27", | |
| 359 | + "2003-12-25", | |
| 360 | + "2004-01-01", | |
| 361 | + "2004-01-19", | |
| 362 | + "2004-02-16", | |
| 363 | + "2004-04-09", | |
| 364 | + "2004-05-31", | |
| 365 | + "2004-06-11", | |
| 366 | + "2004-07-05", | |
| 367 | + "2004-09-06", | |
| 368 | + "2004-11-25", | |
| 369 | + "2004-12-24", | |
| 370 | + "2005-01-17", | |
| 371 | + "2005-02-21", | |
| 372 | + "2005-03-25", | |
| 373 | + "2005-05-30", | |
| 374 | + "2005-07-04", | |
| 375 | + "2005-09-05", | |
| 376 | + "2005-11-24", | |
| 377 | + "2005-12-26", | |
| 378 | + "2006-01-02", | |
| 379 | + "2006-01-16", | |
| 380 | + "2006-02-20", | |
| 381 | + "2006-04-14", | |
| 382 | + "2006-05-29", | |
| 383 | + "2006-07-04", | |
| 384 | + "2006-09-04", | |
| 385 | + "2006-11-23", | |
| 386 | + "2006-12-25", | |
| 387 | + "2007-01-01", | |
| 388 | + "2007-01-02", | |
| 389 | + "2007-01-15", | |
| 390 | + "2007-02-19", | |
| 391 | + "2007-04-06", | |
| 392 | + "2007-05-28", | |
| 393 | + "2007-07-04", | |
| 394 | + "2007-09-03", | |
| 395 | + "2007-11-22", | |
| 396 | + "2007-12-25", | |
| 397 | + "2008-01-01", | |
| 398 | + "2008-01-21", | |
| 399 | + "2008-02-18", | |
| 400 | + "2008-03-21", | |
| 401 | + "2008-05-26", | |
| 402 | + "2008-07-04", | |
| 403 | + "2008-09-01", | |
| 404 | + "2008-11-27", | |
| 405 | + "2008-12-25", | |
| 406 | + "2009-01-01", | |
| 407 | + "2009-01-19", | |
| 408 | + "2009-02-16", | |
| 409 | + "2009-04-10", | |
| 410 | + "2009-05-25", | |
| 411 | + "2009-07-03", | |
| 412 | + "2009-09-07", | |
| 413 | + "2009-11-26", | |
| 414 | + "2009-12-25", | |
| 415 | + "2010-01-01", | |
| 416 | + "2010-01-18", | |
| 417 | + "2010-02-15", | |
| 418 | + "2010-04-02", | |
| 419 | + "2010-05-31", | |
| 420 | + "2010-07-05", | |
| 421 | + "2010-09-06", | |
| 422 | + "2010-11-25", | |
| 423 | + "2010-12-24", | |
| 424 | + "2011-01-17", | |
| 425 | + "2011-02-21", | |
| 426 | + "2011-04-22", | |
| 427 | + "2011-05-30", | |
| 428 | + "2011-07-04", | |
| 429 | + "2011-09-05", | |
| 430 | + "2011-11-24", | |
| 431 | + "2011-12-26", | |
| 432 | + "2012-01-02", | |
| 433 | + "2012-01-16", | |
| 434 | + "2012-02-20", | |
| 435 | + "2012-04-06", | |
| 436 | + "2012-05-28", | |
| 437 | + "2012-07-04", | |
| 438 | + "2012-09-03", | |
| 439 | + "2012-10-29", | |
| 440 | + "2012-10-30", | |
| 441 | + "2012-11-22", | |
| 442 | + "2012-12-25", | |
| 443 | + "2013-01-01", | |
| 444 | + "2013-01-21", | |
| 445 | + "2013-02-18", | |
| 446 | + "2013-03-29", | |
| 447 | + "2013-05-27", | |
| 448 | + "2013-07-04", | |
| 449 | + "2013-09-02", | |
| 450 | + "2013-11-28", | |
| 451 | + "2013-12-25", | |
| 452 | + "2014-01-01", | |
| 453 | + "2014-01-20", | |
| 454 | + "2014-02-17", | |
| 455 | + "2014-04-18", | |
| 456 | + "2014-05-26", | |
| 457 | + "2014-07-04", | |
| 458 | + "2014-09-01", | |
| 459 | + "2014-11-27", | |
| 460 | + "2014-12-25", | |
| 461 | + "2015-01-01", | |
| 462 | + "2015-01-19", | |
| 463 | + "2015-02-16", | |
| 464 | + "2015-04-03", | |
| 465 | + "2015-05-25", | |
| 466 | + "2015-07-03", | |
| 467 | + "2015-09-07", | |
| 468 | + "2015-11-26", | |
| 469 | + "2015-12-25", | |
| 470 | + "2016-01-01", | |
| 471 | + "2016-01-18", | |
| 472 | + "2016-02-15", | |
| 473 | + "2016-03-25", | |
| 474 | + "2016-05-30", | |
| 475 | + "2016-07-04", | |
| 476 | + "2016-09-05", | |
| 477 | + "2016-11-24", | |
| 478 | + "2016-12-26", | |
| 479 | + "2017-01-02", | |
| 480 | + "2017-01-16", | |
| 481 | + "2017-02-20", | |
| 482 | + "2017-04-14", | |
| 483 | + "2017-05-29", | |
| 484 | + "2017-07-04", | |
| 485 | + "2017-09-04", | |
| 486 | + "2017-11-23", | |
| 487 | + "2017-12-25", | |
| 488 | + "2018-01-01", | |
| 489 | + "2018-01-15", | |
| 490 | + "2018-02-19", | |
| 491 | + "2018-03-30", | |
| 492 | + "2018-05-28", | |
| 493 | + "2018-07-04", | |
| 494 | + "2018-09-03", | |
| 495 | + "2018-11-22", | |
| 496 | + "2018-12-05", | |
| 497 | + "2018-12-25", | |
| 498 | + "2019-01-01", | |
| 499 | + "2019-01-21", | |
| 500 | + "2019-02-18", | |
| 501 | + "2019-04-19", | |
| 502 | + "2019-05-27", | |
| 503 | + "2019-07-04", | |
| 504 | + "2019-09-02", | |
| 505 | + "2019-11-28", | |
| 506 | + "2019-12-25", | |
| 507 | + "2020-01-01", | |
| 508 | + "2020-01-20", | |
| 509 | + "2020-02-17", | |
| 510 | + "2020-04-10", | |
| 511 | + "2020-05-25", | |
| 512 | + "2020-07-03", | |
| 513 | + "2020-09-07", | |
| 514 | + "2020-11-26", | |
| 515 | + "2020-12-25", | |
| 516 | + "2021-01-01", | |
| 517 | + "2021-01-18", | |
| 518 | + "2021-02-15", | |
| 519 | + "2021-04-02", | |
| 520 | + "2021-05-31", | |
| 521 | + "2021-07-05", | |
| 522 | + "2021-09-06", | |
| 523 | + "2021-11-25", | |
| 524 | + "2021-12-24", | |
| 525 | + "2022-01-17", | |
| 526 | + "2022-02-21", | |
| 527 | + "2022-04-15", | |
| 528 | + "2022-05-30", | |
| 529 | + "2022-06-20", | |
| 530 | + "2022-07-04", | |
| 531 | + "2022-09-05", | |
| 532 | + "2022-11-24", | |
| 533 | + "2022-12-26", | |
| 534 | + "2023-01-02", | |
| 535 | + "2023-01-16", | |
| 536 | + "2023-02-20", | |
| 537 | + "2023-04-07", | |
| 538 | + "2023-05-29", | |
| 539 | + "2023-06-19", | |
| 540 | + "2023-07-04", | |
| 541 | + "2023-09-04", | |
| 542 | + "2023-11-23", | |
| 543 | + "2023-12-25", | |
| 544 | + "2024-01-01", | |
| 545 | + "2024-01-15", | |
| 546 | + "2024-02-19", | |
| 547 | + "2024-03-29", | |
| 548 | + "2024-05-27", | |
| 549 | + "2024-06-19", | |
| 550 | + "2024-07-04", | |
| 551 | + "2024-09-02", | |
| 552 | + "2024-11-28", | |
| 553 | + "2024-12-25", | |
| 554 | + "2025-01-01", | |
| 555 | + "2025-01-09", | |
| 556 | + "2025-01-20", | |
| 557 | + "2025-02-17", | |
| 558 | + "2025-04-18", | |
| 559 | + "2025-05-26", | |
| 560 | + "2025-06-19", | |
| 561 | + "2025-07-04", | |
| 562 | + "2025-09-01", | |
| 563 | + "2025-11-27", | |
| 564 | + "2025-12-25", | |
| 565 | + "2026-01-01", | |
| 566 | + "2026-01-19", | |
| 567 | + "2026-02-16", | |
| 568 | + "2026-04-03", | |
| 569 | + "2026-05-25", | |
| 570 | + "2026-06-19", | |
| 571 | + "2026-07-03", | |
| 572 | + "2026-09-07", | |
| 573 | + "2026-11-26", | |
| 574 | + "2026-12-25" | |
| 575 | + ], | |
| 576 | + "exchange_calendars_version": "4.13.2" | |
| 4 | 577 | }, |
| 5 | 578 | "ols": { |
| 6 | 579 | "params": [ |
@@ -168,10 +741,10 @@ | ||
| 168 | 741 | "converged": true, |
| 169 | 742 | "forecast_h5": [ |
| 170 | 743 | 0.9428008577891447, |
| 171 | − 0.9349887823049752, | |
| 172 | − 0.9276816634654835, | |
| 173 | − 0.9206952073863107, | |
| 174 | − 0.9139209540830693 | |
| 744 | + 0.9350967040941869, | |
| 745 | + 0.9279209834507949, | |
| 746 | + 0.9203740696879242, | |
| 747 | + 0.9136275610313748 | |
| 175 | 748 | ] |
| 176 | 749 | }, |
| 177 | 750 | "egarch.t": { |
@@ -195,11 +768,105 @@ | ||
| 195 | 768 | "converged": true, |
| 196 | 769 | "forecast_h5": [ |
| 197 | 770 | 0.9422973309992873, |
| 198 | − 0.9345334516475253, | |
| 199 | − 0.9275624591259901, | |
| 200 | − 0.9204890251862488, | |
| 201 | − 0.9130849041786623 | |
| 771 | + 0.9347854822088925, | |
| 772 | + 0.9267087660109804, | |
| 773 | + 0.9193443167679186, | |
| 774 | + 0.9129057243623206 | |
| 202 | 775 | ] |
| 203 | 776 | } |
| 777 | + }, | |
| 778 | + "har": { | |
| 779 | + "h1": { | |
| 780 | + "params": [ | |
| 781 | + 3.572237054621749e-05, | |
| 782 | + 0.705187263376507, | |
| 783 | + -0.0026533461235812503, | |
| 784 | + -0.005382072104623131 | |
| 785 | + ], | |
| 786 | + "hac_bse": [ | |
| 787 | + 5.025808125371488e-06, | |
| 788 | + 0.03771149347978252, | |
| 789 | + 0.04088782859024564, | |
| 790 | + 0.05679993577399188 | |
| 791 | + ], | |
| 792 | + "hac_lags": 7, | |
| 793 | + "n": 1478, | |
| 794 | + "ssr": 4.9066292970164625e-06, | |
| 795 | + "rsquared": 0.4939090998347373 | |
| 796 | + }, | |
| 797 | + "h5": { | |
| 798 | + "params": [ | |
| 799 | + 7.303627566100124e-05, | |
| 800 | + 0.40237517399511974, | |
| 801 | + -0.012151755617208201, | |
| 802 | + -0.010149362030271447 | |
| 803 | + ], | |
| 804 | + "hac_bse": [ | |
| 805 | + 8.862600633978347e-06, | |
| 806 | + 0.042107670404322194, | |
| 807 | + 0.058769867344117446, | |
| 808 | + 0.09751138359952202 | |
| 809 | + ], | |
| 810 | + "hac_lags": 7, | |
| 811 | + "n": 1474, | |
| 812 | + "ssr": 4.553106285292198e-06, | |
| 813 | + "rsquared": 0.2470427244436465 | |
| 814 | + }, | |
| 815 | + "h22": { | |
| 816 | + "params": [ | |
| 817 | + 0.0001079682069563529, | |
| 818 | + 0.10933613277708529, | |
| 819 | + -0.0015594242826553265, | |
| 820 | + -0.02513758766905899 | |
| 821 | + ], | |
| 822 | + "hac_bse": [ | |
| 823 | + 1.0735092262683719e-05, | |
| 824 | + 0.019568145492281423, | |
| 825 | + 0.03970034224388717, | |
| 826 | + 0.0982148382834958 | |
| 827 | + ], | |
| 828 | + "hac_lags": 21, | |
| 829 | + "n": 1457, | |
| 830 | + "ssr": 2.1253086279812884e-06, | |
| 831 | + "rsquared": 0.047604184294901764 | |
| 832 | + }, | |
| 833 | + "log_h1": { | |
| 834 | + "params": [ | |
| 835 | + -2.773144648710696, | |
| 836 | + 0.6956416417576977, | |
| 837 | + 0.01608375548941232, | |
| 838 | + -0.0123652588421852 | |
| 839 | + ], | |
| 840 | + "hac_bse": [ | |
| 841 | + 0.3583623660089911, | |
| 842 | + 0.027231971275931997, | |
| 843 | + 0.03648579501915967, | |
| 844 | + 0.04777402944192476 | |
| 845 | + ], | |
| 846 | + "hac_lags": 7, | |
| 847 | + "n": 1478, | |
| 848 | + "ssr": 253.84378776319585, | |
| 849 | + "rsquared": 0.4939008420788008 | |
| 850 | + }, | |
| 851 | + "harq_h1": { | |
| 852 | + "params": [ | |
| 853 | + 3.048016504762969e-05, | |
| 854 | + 0.7593401271334446, | |
| 855 | + -0.009638437619492339, | |
| 856 | + -0.0027765923868793563, | |
| 857 | + -153.55046863809284 | |
| 858 | + ], | |
| 859 | + "hac_bse": [ | |
| 860 | + 6.591629821411091e-06, | |
| 861 | + 0.04571371552002667, | |
| 862 | + 0.04075106926582767, | |
| 863 | + 0.057820223931294774, | |
| 864 | + 126.5397411975311 | |
| 865 | + ], | |
| 866 | + "hac_lags": 7, | |
| 867 | + "n": 1478, | |
| 868 | + "ssr": 4.894744455085396e-06, | |
| 869 | + "rsquared": 0.4951349536718862 | |
| 870 | + } | |
| 204 | 871 | } |
| 205 | 872 | } |
| \ No newline at end of file | ||
modified
docs/PROGRESS.md
+29 −0
@@ -120,3 +120,32 @@ Executed: | ||
| 120 | 120 | compilation of the bundle is measured separately in E7 (toolchain-less nodes). |
| 121 | 121 | |
| 122 | 122 | Gate evidence: accuracy tests and the measured break-even table above; no claim beyond the tested shapes. |
| 123 | + | |
| 124 | +## Phase 4 — Econometrics, Tier A (2026-11-02, 16:30–17:40 ET) — GATE PASSED | |
| 125 | +Plan: HAR family, block bootstrap, R references (highfrequency, rugarch), Monte Carlo validation, gradient checks, | |
| 126 | +registry completion, Python/CLI exposure. | |
| 127 | + | |
| 128 | +Executed: | |
| 129 | +- `estimators/har.{hpp,cpp}`: HAR-RV / log-HAR / HAR-X / HARQ (causal lags, average or single targets, HAC with | |
| 130 | + ≥ h−1 lags), `har_rolling` (causal re-estimation + QLIKE/MSE), `har_batch` (GPU sufficient statistics, FP64 solve). | |
| 131 | + `statistics/bootstrap.{hpp,cpp}`: stationary / circular block / iid, deterministic streams, scalar and row variants. | |
| 132 | +- R references (`scripts/reference_r.R` → `datasets/fixtures/reference_r.json`, highfrequency 1.0.3, rugarch 1.5.6): | |
| 133 | + RV/BPV/RSV±/RQ equal to 1e−10 after two documented conventions (highfrequency `rQuar` uses (M+1)/3; `rBPCov` with | |
| 134 | + makeReturns=FALSE applies no M/(M−1) factor); `HARmodel` h=1 coefficients 1e−8 and h=5 (average target) 1e−6; HARQ | |
| 135 | + equal once the regressor is (√RQ_t − √mean RQ)·RV_t as in highfrequency; rugarch sGARCH/gjrGARCH/eGARCH/sGARCH-std | |
| 136 | + log-likelihoods within 2e−4 and parameters within 5 % (eGARCH alpha1/gamma1 swap documented; ν at the bound for | |
| 137 | + Gaussian data in both packages). | |
| 138 | +- statsmodels references (`make_fixtures.py`): HAR h=1/5/22, log-HAR, HARQ with HAC → coefficients 1e−9, SE 1e−8. | |
| 139 | +- Calendar: the exchange_calendars comparison was previously **not running** (API error swallowed into a WARN) — fixed; | |
| 140 | + the exact comparison 2000-01-03…2026-12-31 (6 790 sessions, 254 weekday holidays, 60 early closes) revealed three | |
| 141 | + historical one-offs (early closes 2002-07-05 and 2003-12-26; full day 2002-07-03), now encoded. All sets equal. | |
| 142 | +- Monte Carlo (`tests/simulation`, printed to the test log): GARCH(1,1) n=2000, R=120: bias μ −0.0004, ω +0.0033, | |
| 143 | + α −0.0020, β −0.0026; RMSE 0.019/0.009/0.014/0.019; 95 % QMLE coverage 0.950/0.942/0.900/0.933; 0 failures. | |
| 144 | + HAR h=5 HAC coverage 0.895/0.945/0.910/0.905 (R=200, T=1200). RV RMSE vs √(2/M)·IV: 5.48e−5 vs 5.66e−5 (M=39), | |
| 145 | + 1.85e−5 vs 1.79e−5 (390), 5.78e−6 vs 5.66e−6 (3 900). Stationary-bootstrap mean coverage 0.89 (R=100). Numerical | |
| 146 | + NLL gradients agree across step sizes 1e−4/1e−5/1e−6. | |
| 147 | +- Python: `raid.har_fit/har_rolling/har_batch/bootstrap` (pytest 9/9, HAR+HAC equal to statsmodels); CLI `raid | |
| 148 | + estimate har <bars> [--horizon] [--log] [--harq] [--window --step]`. | |
| 149 | +- Full suite: `ctest` all green (see summary in RESEARCH_STATUS). | |
| 150 | + | |
| 151 | +Gate evidence: reference tables (R + Python), Monte Carlo logs, registry entries with tolerances. | |
modified
docs/methods/registry.md
+12 −8
@@ -9,10 +9,10 @@ reference comparison pending · 🧭 experimental · ✖ not implemented. "Toler | ||
| 9 | 9 | | `calendar.xnys` | NYSE trading days 2000–2026 (rules + special closures), early closes 13:00, DST America/New_York | dates | exchange_calendars 4.13 XNYS | exact set equality (sessions, holidays, early closes 2000–2026) | ✅ | n/a | ✅ | |
| 10 | 10 | | `bars.validate` | OHLC consistency, positivity, grid alignment, session membership, coverage vs 391/211, price jumps > 25 % | BarSeries | FinData `/market/sessions` semantics | counts | ✅ | n/a | ✅ (synthetic) | |
| 11 | 11 | | `bars.aggregate` | bins anchored at the session open (09:30 regular / 04:00 extended), the 16:00 closing print is its own bin, no empty bins, OHLC/sum | BarSeries | FinData derived 5m | exact OHLCV: extended policy 280/280 bars (AAPL 2024-11-25…29); regular policy differs only in the closing bin (FinData adds 16:01–16:04) | ✅ | n/a | ✅ (live 2026-11-02) | |
| 12 | −| `realized.rv` | Σ r² | returns CSR | highfrequency `rCov` (univariate) / numpy | 1e−12 rel (numpy) | ✅ | Phase 3 | ✅ | | |
| 13 | −| `realized.bpv` | μ₁⁻²·M/(M−1)·Σ|r_j||r_{j−1}| (finite-sample factor optional) | returns CSR | highfrequency `rBPCov` (makeReturns=FALSE, scaled) | 1e−12 rel (numpy); R comparison Phase 4 | ✅ | Phase 3 | ✅ | | |
| 14 | −| `realized.rsv` | Σ r² 1{r<0}, Σ r² 1{r>0} | returns CSR | highfrequency `rSVar` | 1e−12 rel | ✅ | Phase 3 | ✅ | | |
| 15 | −| `realized.rq` | (M/3) Σ r⁴ | returns CSR | highfrequency `rQuar` | 1e−12 rel | ✅ | Phase 3 | ✅ | | |
| 12 | +| `realized.rv` | Σ r² | returns CSR | highfrequency `rCov` 1e−10, numpy 1e−12 | 1e−10 | ✅ | ✅ 1e−6 | ✅ | | |
| 13 | +| `realized.bpv` | μ₁⁻²·[M/(M−1)]·Σ|r_j||r_{j−1}| (factor on by default; highfrequency `rBPCov(makeReturns=FALSE)` applies none → compared with factor off) | returns CSR | highfrequency 1e−10, numpy 1e−12 | 1e−10 | ✅ | ✅ 1e−6 | ✅ | | |
| 14 | +| `realized.rsv` | Σ r² 1{r<0}, Σ r² 1{r>0} | returns CSR | highfrequency `rSVar` 1e−10 | 1e−10 | ✅ | ✅ 1e−6 | ✅ | | |
| 15 | +| `realized.rq` | (M/3) Σ r⁴ (BPQ 2016; M = number of returns) — highfrequency `rQuar` uses (M+1)/3 (number of prices): compared after conversion | returns CSR | highfrequency `rQuar` 1e−10 (converted), numpy 1e−12 | 1e−10 | ✅ | ✅ | ✅ | | |
| 16 | 16 | | `realized.jump` | max(RV − BPV, 0) (proxy, no test statistic) | — | ABD 2007 | — | ✅ | — | ✅ | |
| 17 | 17 | | `realized.tripv` | μ_{4/3}⁻³·M/(M−2)·Σ Π|r|^{4/3} | returns CSR | highfrequency `rTPQuar` (differs: quarticity) — definition from BNS 2004 | 1e−12 (self) | ✅ | — | 🧪 | |
| 18 | 18 | | `realized.rcov` | Σ r_j r_j' on synchronized returns; corr from diag | T×N returns | highfrequency `rCov` | Phase 4 | ✅ | Phase 3 | 🧪 | |
@@ -23,13 +23,17 @@ reference comparison pending · 🧭 experimental · ✖ not implemented. "Toler | ||
| 23 | 23 | | `cov.cluster` | one-way CGM with G/(G−1)·(n−1)/(n−k) | cluster ids | statsmodels cluster (default correction) | 1e−9 | ✅ | — | ✅ | |
| 24 | 24 | | `inference.t` | two-sided p from t(df_resid) (regularised incomplete beta); df ≤ 0 → normal | — | scipy.stats.t | 1e−12 | ✅ | — | ✅ | |
| 25 | 25 | | `ols.rolling` | window-by-window QR; step parameter | X, y | statsmodels RollingOLS | self-consistency test; Phase 4 vs statsmodels | ✅ | Phase 3 | 🧪 | |
| 26 | −| `har.*` | HAR-RV, log-HAR, HAR-X, HARQ (BPQ 2016): RV_{t+h} regressed on RV_t, mean(RV_{t−4..t}), mean(RV_{t−21..t}) [, √RQ_t·RV_t]; causal lags; h ∈ {1,5,22} with overlapping targets → HAC(h−1 lags minimum) | daily RV panel | highfrequency `HARmodel`, statsmodels | Phase 4 | Phase 4 | Phase 4 | ✖ (Phase 4) | | |
| 27 | −| `garch.garch.gaussian` | σ²_t = ω + α ε²_{t−1} + β σ²_{t−1}; σ²_0 = sample variance of ε; constraints ω>0, α,β≥0, α+β<1 | returns | arch 8.0 `arch_model(vol="GARCH", rescale=False)` (backcast initialisation ≠ ours), rugarch (Phase 4) | loglik 5e−4 rel; params 5 % rel + 0.01 abs; forecasts 5 % | ✅ | likelihood batch Phase 3 | ✅ | | |
| 26 | +| `har.rv.h{1,5,22}` | y_{t+h} = (1/h)Σ_{j=1..h} RV_{t+j} on [1, RV_t, mean RV_{t−4..t}, mean RV_{t−21..t}]; causal; HAC lags = max(h−1, ⌊4(n/100)^{2/9}⌋) | daily RV | highfrequency `HARmodel` (h=1 coef 1e−8, h=5 average target 1e−6), statsmodels HAC 1e−9 | 1e−8 | ✅ | ✅ batched sufficient stats 1e−4 (Metal) | ✅ | | |
| 27 | +| `har.logrv` | same on log RV; forecast exp(ŷ) without smearing (factor reported) | daily RV | statsmodels 1e−9 | 1e−9 | ✅ | — | ✅ | | |
| 28 | +| `har.rv.harq` | + β_Q (√RQ_t − √mean RQ)·RV_t exactly as highfrequency `HARmodel(type="HARQ")` (demeaning optional) | daily RV, RQ | highfrequency 1e−6, statsmodels 1e−9 | 1e−6 | ✅ | — | ✅ | | |
| 29 | +| `har.rolling` | rolling/expanding refits, origin-ahead forecasts, MSE/QLIKE (Patton 2011) | daily RV | causality test (future perturbation leaves forecasts unchanged) | exact | ✅ | — | ✅ | | |
| 30 | +| `garch.garch.gaussian` | σ²_t = ω + α ε²_{t−1} + β σ²_{t−1}; σ²_0 = sample variance of ε; constraints ω>0, α,β≥0, α+β<1 | returns | arch 8.0 (loglik 5e−4, params 5 %), rugarch 1.5.6 `ugarchfit(sGARCH)` (loglik 2e−4, params 5 %) | as stated | ✅ | ✅ batched NLL 2e−5 | ✅ | | |
| 28 | 31 | | `garch.gjr.*` | + γ 1{ε<0} ε²; α+γ ≥ 0; α+β+γ/2 < 1 | returns | arch GJR (o=1) | same | ✅ | — | ✅ | |
| 29 | −| `garch.egarch.*` | ln σ²_t = ω + α(|z|−E|z|) + γ z + β ln σ²_{t−1}; |β|<1; h>1 forecasts by simulation (20 000 paths, seeded) | returns | arch EGARCH | same (params); forecasts h>1 not compared | ✅ | — | ✅ | | |
| 32 | +| `garch.egarch.*` | ln σ²_t = ω + α(|z|−E|z|) + γ z + β ln σ²_{t−1}; |β|<1; h>1 forecasts by simulation (20 000 paths, seeded). **rugarch maps alpha1 ↔ RAID γ (sign) and gamma1 ↔ RAID α (magnitude)** | returns | arch EGARCH, rugarch eGARCH (loglik 2e−4, mapped params 5 %) | same | ✅ | ✅ | ✅ | | |
| 30 | 33 | | `garch.*.student_t` | standardized t, ν ∈ (2.05, 500) (arch bounds); E|z| via lgamma | returns | arch dist="t" | same; ν skipped when > 100 (unidentified) | ✅ | — | ✅ | |
| 31 | 34 | | `garch.cov` | QMLE sandwich H⁻¹ OPG H⁻¹ (Bollerslev–Wooldridge 1992), Hessian, OPG — numerical derivatives | — | arch robust/classic | Phase 4 (SE comparison) | ✅ | — | 🧪 | |
| 32 | −| `bootstrap.stationary` / `circular` | Politis–Romano 1994 / circular block; replica generation + statistic | series | arch.bootstrap | Phase 4 | Phase 4 | Phase 3/4 | ✖ | | |
| 35 | +| `bootstrap.stationary` / `circular` / `iid` | Politis–Romano 1994 (geometric blocks, mean length b or n^{1/3}) / circular blocks / iid (comparison only); replica r ← Rng(seed, r); percentile CI, SE, bias; `bootstrap_rows` for regressions; `bootstrap_rv` | series | theory: AR(1) long-run SE (block SE within 35 %, > 1.6× iid SE); MC coverage 0.89 (R = 100) | as stated | ✅ | ⏳ (GPU replicas, E5) | ✅ | | |
| 36 | +| `simulation.*` | Monte Carlo: GARCH(1,1) n=2000 R=120 — coverage 0.90–0.95, |bias| ≤ 0.003, no failures; HAR h=5 HAC coverage 0.895–0.945 (R=200, T=1200, pseudo-true from T=200 000); RV RMSE within 10 % of √(2/M)·IV; NLL finite-difference gradients stable across h | — | `tests/simulation` | — | ✅ | — | ✅ | | |
| 33 | 37 | |
| 34 | 38 | Conventions that differ from references and are **documented, not hidden**: |
| 35 | 39 | 1. GARCH initial variance: RAID uses the sample variance of ε; `arch` uses a backcast (exponentially weighted) and |
modified
include/raid/estimators/har.hpp
+3 −2
@@ -2,7 +2,7 @@ | ||
| 2 | 2 | // HAR-RV (Corsi 2009): y_{t+h} = β0 + βd RV_t + βw RV^{(5)}_t + βm RV^{(22)}_t + ε, RV^{(k)}_t = (1/k) Σ_{j=0}^{k−1} RV_{t−j} |
| 3 | 3 | // log-HAR: same with log RV on both sides (forecast = exp(ŷ) × smearing optional, not applied by default) |
| 4 | 4 | // HAR-X: + user covariates X_t (already aligned at t) |
| 5 | −// HARQ (Bollerslev–Patton–Quaedvlieg 2016): βd → (βd + βq √RQ_t): term βq · √RQ_t · RV_t added | |
| 5 | +// HARQ (Bollerslev–Patton–Quaedvlieg 2016): βd → (βd + βq √RQ_t): term βq · (√RQ_t − mean √RQ) · RV_t added (demeaning optional) | |
| 6 | 6 | // Target for horizon h: y_{t+h} = (1/h) Σ_{j=1}^{h} RV_{t+j} (average over the next h days, "overlapping" when h > 1) |
| 7 | 7 | // or the single-day RV_{t+h} (`target = single`). Overlapping targets induce MA(h−1) errors → HAC with ≥ h−1 lags. |
| 8 | 8 | #pragma once |
@@ -26,12 +26,13 @@ struct HarSpec { | ||
| 26 | 26 | HarTarget target{HarTarget::average}; |
| 27 | 27 | bool log{false}; // log-HAR |
| 28 | 28 | bool harq{false}; // add √RQ_t · RV_t |
| 29 | + bool harq_demean_sqrt_rq{true}; // use (√RQ_t − √(mean RQ)) · RV_t exactly as highfrequency::HARmodel(type="HARQ"); the mean is over the sample passed to the fit (estimation window), never the future | |
| 29 | 30 | stats::CovOptions cov{stats::CovType::hac_bartlett, -1, false, {}, true}; // hac_lags −1 → max(h−1, ⌊4(n/100)^{2/9}⌋) |
| 30 | 31 | std::string model_key() const { return std::string(log ? "har.logrv" : "har.rv") + (harq ? ".harq" : "") + ".h" + std::to_string(horizon); } |
| 31 | 32 | }; |
| 32 | 33 | |
| 33 | 34 | /// Builds the aligned design (y, X) from a daily RV series (and optional RQ and exogenous columns X_t, aligned at t). |
| 34 | −struct HarDesign { Eigen::MatrixXd X; Eigen::VectorXd y; std::vector<std::string> names; usize first_t{0}; usize n{0}; }; | |
| 35 | +struct HarDesign { Eigen::MatrixXd X; Eigen::VectorXd y; std::vector<std::string> names; usize first_t{0}; usize n{0}; f64 sqrt_rq_mean{0}; }; | |
| 35 | 36 | HarDesign har_design(std::span<const f64> rv, const HarSpec& spec, std::span<const f64> rq = {}, const Eigen::MatrixXd& exog = Eigen::MatrixXd(), const std::vector<std::string>& exog_names = {}); |
| 36 | 37 | |
| 37 | 38 | /// Fit one HAR on one series (CPU QR + robust covariance). Estimate.extra contains fitted/residual summaries. |
added
include/raid/statistics/bootstrap.hpp
+55 −0
@@ -0,0 +1,55 @@ | ||
| 1 | +// Block bootstrap for dependent data (registry keys bootstrap.*): | |
| 2 | +// stationary (Politis & Romano 1994): blocks start at uniform random indices, lengths ~ Geometric(p = 1/mean_block), | |
| 3 | +// wrapping circularly; expected block length = mean_block. | |
| 4 | +// circular block (Politis & Romano 1992): fixed block length b, start indices uniform on [0, n), wrap around. | |
| 5 | +// iid (for comparison only; not valid for dependent data — flagged in the registry). | |
| 6 | +// Index generation is deterministic given (seed, replica id): replica r uses Rng(seed, r), so replicas can be generated | |
| 7 | +// in parallel (CPU threads) or on the GPU with the same streams. | |
| 8 | +#pragma once | |
| 9 | +#include <vector> | |
| 10 | +#include <span> | |
| 11 | +#include <functional> | |
| 12 | +#include "raid/core/types.hpp" | |
| 13 | +#include "raid/core/rng.hpp" | |
| 14 | +#include "raid/core/plan.hpp" | |
| 15 | +#include "raid/core/estimate.hpp" | |
| 16 | + | |
| 17 | +namespace raid::stats { | |
| 18 | + | |
| 19 | +enum class BootstrapScheme { stationary, circular_block, iid }; | |
| 20 | + | |
| 21 | +struct BootstrapOptions { | |
| 22 | + BootstrapScheme scheme{BootstrapScheme::stationary}; | |
| 23 | + f64 mean_block{0}; // 0 → n^{1/3} (rule of thumb; the paper reports the value used) | |
| 24 | + usize replicas{999}; | |
| 25 | + u64 seed{20261102}; | |
| 26 | + unsigned threads{0}; | |
| 27 | + f64 ci_level{0.95}; | |
| 28 | +}; | |
| 29 | + | |
| 30 | +/// Index vector of one replica (length n). | |
| 31 | +std::vector<u32> bootstrap_indices(usize n, const BootstrapOptions& o, usize replica); | |
| 32 | + | |
| 33 | +struct BootstrapResult { | |
| 34 | + std::vector<f64> statistics; // one per replica (NaN if the statistic failed) | |
| 35 | + f64 point{0}; // statistic on the original sample | |
| 36 | + f64 se{0}; // bootstrap standard error | |
| 37 | + f64 ci_lo{0}, ci_hi{0}; // percentile CI | |
| 38 | + f64 bias{0}; // mean(stat*) − point | |
| 39 | + usize failures{0}; | |
| 40 | + f64 block_length{0}; | |
| 41 | + f64 seconds{0}; | |
| 42 | +}; | |
| 43 | + | |
| 44 | +/// Generic scalar statistic bootstrap: stat(x_resampled) evaluated for each replica. | |
| 45 | +BootstrapResult bootstrap(std::span<const f64> x, const std::function<f64(std::span<const f64>)>& stat, const BootstrapOptions& o); | |
| 46 | + | |
| 47 | +/// Multivariate: statistic returns a vector (e.g. regression coefficients); rows of (y, X) are resampled jointly. | |
| 48 | +struct BootstrapVecResult { std::vector<std::vector<f64>> statistics; std::vector<f64> point, se, ci_lo, ci_hi; usize failures{0}; f64 block_length{0}; f64 seconds{0}; }; | |
| 49 | +BootstrapVecResult bootstrap_rows(usize n, const std::function<std::vector<f64>(std::span<const u32>)>& stat_of_indices, const BootstrapOptions& o); | |
| 50 | + | |
| 51 | +/// Realized-variance bootstrap: for each replica, resample intraday returns within a session by blocks and recompute | |
| 52 | +/// RV (Gonçalves & Meddahi 2009 use the iid wild bootstrap for RV; we use block schemes to respect dependence). | |
| 53 | +BootstrapResult bootstrap_rv(std::span<const f64> intraday_returns, const BootstrapOptions& o); | |
| 54 | + | |
| 55 | +} // namespace raid::stats | |
modified
python/raid/__init__.py
+2 −1
@@ -18,7 +18,8 @@ except ImportError: # pragma: no cover | ||
| 18 | 18 | from . import findata # noqa: E402 |
| 19 | 19 | from .api import ( # noqa: E402 |
| 20 | 20 | devices, ols, realized_measures, garch_fit, validate_bars, calendar_sessions, Backend, available_backends, |
| 21 | + har_fit, har_rolling, har_batch, bootstrap, | |
| 21 | 22 | ) |
| 22 | 23 | |
| 23 | 24 | __all__ = ["__version__", "HAS_CORE", "devices", "ols", "realized_measures", "garch_fit", "validate_bars", |
| 24 | − "calendar_sessions", "Backend", "available_backends", "findata"] | |
| 25 | + "calendar_sessions", "Backend", "available_backends", "findata", "har_fit", "har_rolling", "har_batch", "bootstrap"] | |
modified
python/raid/api.py
+20 −0
@@ -76,3 +76,23 @@ def bars_to_intraday_returns(timestamps_us, close, kind: str = "log") -> dict[st | ||
| 76 | 76 | """Session-aware intraday log/arithmetic returns from (bar open timestamp, close) arrays; returns CSR layout.""" |
| 77 | 77 | t = np.ascontiguousarray(timestamps_us, dtype=np.int64) |
| 78 | 78 | return _core().intraday_returns(t, _f64(close), kind) |
| 79 | + | |
| 80 | + | |
| 81 | +def har_fit(rv, lags=(1, 5, 22), horizon: int = 1, log: bool = False, harq: bool = False, rq=None, cov: str = "hac", hac_lags: int = -1, target: str = "average") -> dict[str, Any]: | |
| 82 | + """HAR-RV / log-HAR / HARQ on a daily RV series with HAC (Newey–West) inference by default; returns Estimate + forecast_next.""" | |
| 83 | + return _core().har_fit(_f64(rv), list(lags), int(horizon), bool(log), bool(harq), None if rq is None else _f64(rq), cov, int(hac_lags), target) | |
| 84 | + | |
| 85 | + | |
| 86 | +def har_rolling(rv, lags=(1, 5, 22), horizon: int = 1, log: bool = False, window: int = 1000, step: int = 1, harq: bool = False, rq=None) -> dict[str, Any]: | |
| 87 | + """Causal rolling re-estimation with origin-ahead forecasts; returns forecasts, actuals, MSE and QLIKE.""" | |
| 88 | + return _core().har_rolling(_f64(rv), list(lags), int(horizon), bool(log), int(window), int(step), bool(harq), None if rq is None else _f64(rq)) | |
| 89 | + | |
| 90 | + | |
| 91 | +def har_batch(rv_concat, offsets, lags=(1, 5, 22), horizon: int = 1, log: bool = False, backend: str | Backend = Backend.auto) -> dict[str, Any]: | |
| 92 | + """Batched HAR over a CSR panel of daily RV series (sufficient statistics on the selected backend, FP64 solve).""" | |
| 93 | + return _core().har_batch(_f64(rv_concat), np.ascontiguousarray(offsets, dtype=np.int64), list(lags), int(horizon), bool(log), str(Backend(backend).value)) | |
| 94 | + | |
| 95 | + | |
| 96 | +def bootstrap(x, stat: str = "mean", scheme: str = "stationary", mean_block: float = 0.0, replicas: int = 999, seed: int = 20261102, level: float = 0.95) -> dict[str, Any]: | |
| 97 | + """Stationary / circular block / iid bootstrap of mean, realized variance (sum of squares) or variance.""" | |
| 98 | + return _core().bootstrap(_f64(x), stat, scheme, float(mean_block), int(replicas), int(seed), float(level)) | |
modified
python/tests/test_api.py
+23 −0
@@ -58,3 +58,26 @@ def test_validate_bars_counts_missing(): | ||
| 58 | 58 | p = 100 + np.cumsum(np.random.default_rng(1).normal(0, 0.01, size=t.size)) |
| 59 | 59 | q = raid.validate_bars(t, p, p + 0.01, p - 0.01, p, np.full(t.size, 100.0)) |
| 60 | 60 | assert q["n_sessions"] == 1 and q["missing_regular_bars"] == 3 and not q["has_errors"] |
| 61 | + | |
| 62 | + | |
| 63 | +def test_har_fit_matches_statsmodels_hac(): | |
| 64 | + sm = pytest.importorskip("statsmodels.api") | |
| 65 | + rng = np.random.default_rng(8); T = 800; lv = np.empty(T); lv[0] = np.log(1e-4) | |
| 66 | + for t in range(1, T): lv[t] = 0.3 * np.log(1e-4) + 0.7 * lv[t - 1] + 0.4 * rng.standard_normal() | |
| 67 | + rv = np.exp(lv); e = raid.har_fit(rv, horizon=1) | |
| 68 | + rows = []; ys = [] | |
| 69 | + for t in range(21, T - 1): | |
| 70 | + rows.append([1.0, rv[t], rv[t - 4:t + 1].mean(), rv[t - 21:t + 1].mean()]); ys.append(rv[t + 1]) | |
| 71 | + X = np.array(rows); y = np.array(ys); L = max(0, int(np.floor(4 * (len(y) / 100) ** (2 / 9)))) | |
| 72 | + f = sm.OLS(y, X).fit(cov_type="HAC", cov_kwds={"maxlags": L, "use_correction": False}) | |
| 73 | + np.testing.assert_allclose(e["coefficients"], f.params, rtol=1e-9); np.testing.assert_allclose(e["inference"]["std_errors"], f.bse, rtol=1e-8) | |
| 74 | + assert e["forecast_next"] > 0 | |
| 75 | + r = raid.har_rolling(rv, horizon=5, window=400, step=20); assert r["n_fits"] > 10 and r["qlike"] >= 0 | |
| 76 | + b = raid.har_batch(np.concatenate([rv, rv * 2]), np.array([0, T, 2 * T]), backend="auto"); assert len(b["fits"]) == 2 and b["fits"][1]["ok"] | |
| 77 | + | |
| 78 | + | |
| 79 | +def test_bootstrap_block_vs_iid(): | |
| 80 | + rng = np.random.default_rng(2); n = 1500; x = np.empty(n); x[0] = 0 | |
| 81 | + for t in range(1, n): x[t] = 0.7 * x[t - 1] + rng.standard_normal() | |
| 82 | + blk = raid.bootstrap(x, "mean", "stationary", mean_block=20, replicas=399); iid = raid.bootstrap(x, "mean", "iid", replicas=399) | |
| 83 | + assert blk["se"] > 1.5 * iid["se"] and blk["failures"] == 0 and blk["ci_lo"] < blk["point"] < blk["ci_hi"] | |
modified
scripts/make_fixtures.py
+27 −4
@@ -53,11 +53,11 @@ print("wrote", len(df), "bars for", df.symbol.nunique(), "symbols;", df.groupby( | ||
| 53 | 53 | ref = {} |
| 54 | 54 | try: |
| 55 | 55 | import exchange_calendars as xc |
| 56 | − cal = xc.get_calendar("XNYS", start="2000-01-01", end="2026-12-31") | |
| 57 | − sess = cal.sessions_in_range("2000-01-01", "2026-12-31") | |
| 56 | + cal = xc.get_calendar("XNYS", start="2000-01-03", end="2026-12-31") | |
| 57 | + sess = cal.sessions_in_range("2000-01-03", "2026-12-31") | |
| 58 | 58 | ref["xnys"] = {"n_sessions_2000_2026": int(len(sess)), "sessions_2024": [str(d.date()) for d in cal.sessions_in_range("2024-01-01", "2024-12-31")], |
| 59 | − "early_closes_2000_2026": [str(d.date()) for d in cal.early_closes.sessions_in_range("2000-01-01", "2026-12-31")] if hasattr(cal, "early_closes") else None, | |
| 60 | − "holidays_2000_2026": [str(d.date()) for d in pd.bdate_range("2000-01-01", "2026-12-31") if d not in sess], | |
| 59 | + "early_closes_2000_2026": [str(d.date()) for d in cal.early_closes if "2000-01-03" <= str(d.date()) <= "2026-12-31"], | |
| 60 | + "holidays_2000_2026": [str(d.date()) for d in pd.bdate_range("2000-01-03", "2026-12-31") if d not in sess], | |
| 61 | 61 | "exchange_calendars_version": xc.__version__} |
| 62 | 62 | except Exception as e: |
| 63 | 63 | ref["xnys"] = {"error": str(e)} |
@@ -108,3 +108,26 @@ with open(os.path.join(out_dir, "synthetic_bars_page.arrow"), "wb") as fh: | ||
| 108 | 108 | w.write_table(tbl) |
| 109 | 109 | pq.write_table(tbl, os.path.join(out_dir, "synthetic_bars_page.parquet")) |
| 110 | 110 | print("wrote synthetic_bars_page.arrow/.parquet", tbl.num_rows, "rows") |
| 111 | + | |
| 112 | +# --- HAR reference: simulated daily RV (log-AR(1)), HAR-RV h=1 and h=5 (average target) with HAC, log-HAR, HARQ ----- | |
| 113 | +g = np.random.default_rng(40); T = 1500; lv = np.empty(T); lv[0] = np.log(1e-4) | |
| 114 | +for t in range(1, T): lv[t] = 0.3 * np.log(1e-4) + 0.7 * lv[t - 1] + 0.4 * g.standard_normal() | |
| 115 | +rv = np.exp(lv); rq = rv ** 2 * (1 + 0.5 * g.random(T)) | |
| 116 | +np.savetxt(os.path.join(out_dir, "har_rv.csv"), np.column_stack([rv, rq]), delimiter=",", header="rv,rq", comments="", fmt="%.17g") | |
| 117 | +def har_design(x, lags=(1, 5, 22), h=1, log=False, harq=None, target="average"): | |
| 118 | + z = np.log(x) if log else x; L = max(lags); rows = []; ys = [] | |
| 119 | + sqrt_rq_mean = np.sqrt(np.mean(np.maximum(harq, 0.0))) if harq is not None else 0.0 | |
| 120 | + for t in range(L - 1, len(x) - h): | |
| 121 | + row = [1.0] + [z[t - l + 1:t + 1].mean() for l in lags] | |
| 122 | + if harq is not None: row.append((np.sqrt(max(harq[t], 0.0)) - sqrt_rq_mean) * x[t]) | |
| 123 | + if target == "average": yv = x[t + 1:t + h + 1].mean(); yv = np.log(yv) if log else yv | |
| 124 | + else: yv = z[t + h] | |
| 125 | + rows.append(row); ys.append(yv) | |
| 126 | + return np.array(rows), np.array(ys) | |
| 127 | +har = {} | |
| 128 | +for name, kw in [("h1", dict(h=1)), ("h5", dict(h=5)), ("h22", dict(h=22)), ("log_h1", dict(h=1, log=True)), ("harq_h1", dict(h=1, harq=rq))]: | |
| 129 | + X, y = har_design(rv, **kw); n = len(y); h = kw["h"]; L = max(h - 1, int(np.floor(4 * (n / 100) ** (2 / 9)))) | |
| 130 | + f = sm.OLS(y, X).fit(cov_type="HAC", cov_kwds={"maxlags": L, "use_correction": False}) | |
| 131 | + har[name] = {"params": f.params.tolist(), "hac_bse": f.bse.tolist(), "hac_lags": L, "n": n, "ssr": float(f.ssr), "rsquared": float(f.rsquared)} | |
| 132 | +ref["har"] = har; json.dump(ref, open(os.path.join(out_dir, "reference_values.json"), "w"), indent=1) | |
| 133 | +print("HAR references:", {k: v["n"] for k, v in har.items()}) | |
added
scripts/reference_r.R
+50 −0
@@ -0,0 +1,50 @@ | ||
| 1 | +#!/usr/bin/env Rscript | |
| 2 | +# Reference values from R: highfrequency (realized measures, HARmodel) and rugarch (GARCH family) on the committed | |
| 3 | +# synthetic fixtures. Output: datasets/fixtures/reference_r.json. Versions are recorded in the JSON. | |
| 4 | +suppressPackageStartupMessages({ library(highfrequency); library(rugarch); library(xts); library(jsonlite) }) | |
| 5 | +root <- normalizePath(file.path(dirname(sub("--file=", "", grep("--file=", commandArgs(), value = TRUE)[1])), "..")) | |
| 6 | +fx <- file.path(root, "datasets", "fixtures") | |
| 7 | +out <- list(versions = list(R = R.version.string, highfrequency = as.character(packageVersion("highfrequency")), rugarch = as.character(packageVersion("rugarch")))) | |
| 8 | + | |
| 9 | +# ---- realized measures per session on the synthetic 1-minute bars (regular session, log returns) ------------------- | |
| 10 | +bars <- read.csv(file.path(fx, "synthetic_bars_1m.csv"), stringsAsFactors = FALSE) | |
| 11 | +bars$ts <- as.POSIXct(bars$timestamp_utc, format = "%Y-%m-%dT%H:%M:%SZ", tz = "UTC") | |
| 12 | +bars$et <- format(bars$ts, tz = "America/New_York", format = "%H:%M") | |
| 13 | +bars$sess <- as.Date(format(bars$ts, tz = "America/New_York", format = "%Y-%m-%d")) | |
| 14 | +early <- as.Date("2024-11-29") | |
| 15 | +rm <- list() | |
| 16 | +for (sym in unique(bars$symbol)) { | |
| 17 | + b <- bars[bars$symbol == sym, ] | |
| 18 | + for (d in as.character(unique(b$sess))) { | |
| 19 | + s <- b[b$sess == as.Date(d), ]; close_min <- if (as.Date(d) == early) "13:00" else "16:00" | |
| 20 | + s <- s[s$et >= "09:30" & s$et <= close_min, ] | |
| 21 | + p <- xts(s$close, order.by = s$ts); r <- diff(log(p))[-1] | |
| 22 | + rm[[paste(sym, d)]] <- list(symbol = sym, session = d, n_returns = length(r), | |
| 23 | + rv = as.numeric(rCov(r, makeReturns = FALSE)), bpv = as.numeric(rBPCov(r, makeReturns = FALSE)), | |
| 24 | + rsv = as.list(as.numeric(rSVar(r, makeReturns = FALSE))), rq = as.numeric(rQuar(r, makeReturns = FALSE)), | |
| 25 | + bns_jump_stat = tryCatch(as.numeric(BNSjumpTest(r, makeReturns = FALSE)$ztest), error = function(e) NA)) | |
| 26 | + } | |
| 27 | +} | |
| 28 | +out$realized <- unname(rm) | |
| 29 | + | |
| 30 | +# ---- HARmodel on the daily RV fixture ----------------------------------------------------------------------------- | |
| 31 | +h <- read.csv(file.path(fx, "har_rv.csv")); dates <- seq(as.Date("2018-01-01"), by = "day", length.out = nrow(h)) | |
| 32 | +rvx <- xts(h$rv, order.by = dates) | |
| 33 | +hm <- HARmodel(rvx, periods = c(1, 5, 22), RVest = c("rCov"), type = "HAR", h = 1, transform = NULL, inputType = "RM") | |
| 34 | +out$har_h1 <- list(coef = as.list(unname(coef(hm))), names = names(coef(hm)), n = length(hm$residuals), ssr = sum(hm$residuals^2)) | |
| 35 | +hm5 <- HARmodel(rvx, periods = c(1, 5, 22), RVest = c("rCov"), type = "HAR", h = 5, transform = NULL, inputType = "RM") | |
| 36 | +out$har_h5 <- list(coef = as.list(unname(coef(hm5))), n = length(hm5$residuals), ssr = sum(hm5$residuals^2)) | |
| 37 | +hq <- HARmodel(xts(cbind(h$rv, h$rq), order.by = dates), periods = c(1, 5, 22), periodsQ = c(1), RVest = c("rCov", "rQuar"), type = "HARQ", h = 1, transform = NULL, inputType = "RM") | |
| 38 | +out$harq_h1 <- list(coef = as.list(unname(coef(hq))), names = names(coef(hq)), n = length(hq$residuals), ssr = sum(hq$residuals^2)) | |
| 39 | + | |
| 40 | +# ---- rugarch on the simulated GARCH returns ------------------------------------------------------------------------ | |
| 41 | +r <- read.csv(file.path(fx, "garch_returns.csv"))$r | |
| 42 | +fit_one <- function(model, dist) { | |
| 43 | + spec <- ugarchspec(variance.model = list(model = model, garchOrder = c(1, 1)), mean.model = list(armaOrder = c(0, 0), include.mean = TRUE), distribution.model = dist) | |
| 44 | + f <- ugarchfit(spec, r, solver = "hybrid") | |
| 45 | + list(coef = as.list(coef(f)), se = as.list(f@fit$matcoef[, 2]), robust_se = as.list(f@fit$robust.matcoef[, 2]), loglik = as.numeric(likelihood(f)), converged = f@fit$convergence == 0, | |
| 46 | + forecast_h5 = as.numeric(sigma(ugarchforecast(f, n.ahead = 5)))^2) | |
| 47 | +} | |
| 48 | +out$garch <- list(sGARCH.norm = fit_one("sGARCH", "norm"), gjrGARCH.norm = fit_one("gjrGARCH", "norm"), eGARCH.norm = fit_one("eGARCH", "norm"), sGARCH.std = fit_one("sGARCH", "std")) | |
| 49 | +write(toJSON(out, auto_unbox = TRUE, digits = 17, pretty = TRUE), file.path(fx, "reference_r.json")) | |
| 50 | +cat("wrote reference_r.json\n") | |
modified
src/estimators/har.cpp
+7 −3
@@ -15,17 +15,20 @@ HarDesign har_design(std::span<const f64> rv, const HarSpec& spec, std::span<con | ||
| 15 | 15 | if (spec.harq) RAID_REQUIRE(rq.size() == T, invalid_argument, "harq: RQ series required with the same length"); |
| 16 | 16 | const usize nex = exog.size() ? static_cast<usize>(exog.cols()) : 0; if (nex) RAID_REQUIRE(static_cast<usize>(exog.rows()) == T, invalid_argument, "har-x: exog rows must equal T"); |
| 17 | 17 | std::vector<f64> z(T); for (usize t = 0; t < T; ++t) z[t] = spec.log ? std::log(rv[t]) : rv[t]; |
| 18 | + f64 sqrt_rq_mean = 0.0; | |
| 19 | + if (spec.harq && spec.harq_demean_sqrt_rq) { f64 s = 0; for (usize t = 0; t < T; ++t) s += std::max(rq[t], 0.0); sqrt_rq_mean = std::sqrt(s / static_cast<f64>(T)); } // √(mean RQ) over the sample, as highfrequency::HARmodel | |
| 18 | 20 | // prefix sums for the window means (FP64, Neumaier) |
| 19 | 21 | std::vector<f64> cs(T + 1, 0.0); { f64 s = 0, c = 0; for (usize t = 0; t < T; ++t) { const f64 v = z[t], tt = s + v; if (std::abs(s) >= std::abs(v)) c += (s - tt) + v; else c += (v - tt) + s; s = tt; cs[t + 1] = s + c; } } |
| 20 | 22 | auto wmean = [&](usize t, usize k) { f64 s = 0; for (usize j = 0; j < k; ++j) s += z[t - j]; return s / static_cast<f64>(k); }; // direct sum (exact enough, k ≤ 22) |
| 21 | 23 | const usize first = maxlag - 1, last = T - 1 - h; // origins t with full lags and a full target |
| 22 | 24 | HarDesign d; d.first_t = first; d.n = last - first + 1; const usize k = 1 + spec.lags.size() + (spec.harq ? 1 : 0) + nex; |
| 23 | 25 | d.X.resize(static_cast<Eigen::Index>(d.n), static_cast<Eigen::Index>(k)); d.y.resize(static_cast<Eigen::Index>(d.n)); |
| 24 | − d.names.push_back("const"); for (usize l : spec.lags) d.names.push_back((spec.log ? "logrv_" : "rv_") + std::to_string(l)); if (spec.harq) d.names.push_back("sqrt_rq_x_rv"); for (usize j = 0; j < nex; ++j) d.names.push_back(j < exog_names.size() ? exog_names[j] : "x" + std::to_string(j)); | |
| 26 | + d.names.push_back("const"); for (usize l : spec.lags) d.names.push_back((spec.log ? "logrv_" : "rv_") + std::to_string(l)); if (spec.harq) d.names.push_back(spec.harq_demean_sqrt_rq ? "sqrt_rq_dm_x_rv" : "sqrt_rq_x_rv"); | |
| 27 | + d.sqrt_rq_mean = sqrt_rq_mean; for (usize j = 0; j < nex; ++j) d.names.push_back(j < exog_names.size() ? exog_names[j] : "x" + std::to_string(j)); | |
| 25 | 28 | for (usize t = first; t <= last; ++t) { |
| 26 | 29 | const Eigen::Index i = static_cast<Eigen::Index>(t - first); Eigen::Index c = 0; d.X(i, c++) = 1.0; |
| 27 | 30 | for (usize l : spec.lags) d.X(i, c++) = wmean(t, l); |
| 28 | − if (spec.harq) d.X(i, c++) = std::sqrt(std::max(rq[t], 0.0)) * rv[t]; | |
| 31 | + if (spec.harq) d.X(i, c++) = (std::sqrt(std::max(rq[t], 0.0)) - sqrt_rq_mean) * rv[t]; | |
| 29 | 32 | for (usize j = 0; j < nex; ++j) d.X(i, c++) = exog(static_cast<Eigen::Index>(t), static_cast<Eigen::Index>(j)); |
| 30 | 33 | if (spec.target == HarTarget::single) d.y[i] = z[t + h]; else { f64 s = 0; for (usize j = 1; j <= h; ++j) s += spec.log ? rv[t + j] : z[t + j]; s /= static_cast<f64>(h); d.y[i] = spec.log ? std::log(s) : s; } |
| 31 | 34 | } |
@@ -39,6 +42,7 @@ Estimate har_fit(std::span<const f64> rv, const HarSpec& spec, std::span<const f | ||
| 39 | 42 | auto r = ols(d.X, d.y, o); Estimate e = r.estimate; e.model = spec.model_key(); |
| 40 | 43 | e.extra["horizon"] = spec.horizon; e.extra["target"] = spec.target == HarTarget::average ? "average" : "single"; e.extra["lags"] = spec.lags; e.extra["first_origin"] = d.first_t; e.extra["log"] = spec.log; e.extra["harq"] = spec.harq; |
| 41 | 44 | if (spec.log) { f64 s = 0; for (Eigen::Index i = 0; i < r.residuals.size(); ++i) s += std::exp(r.residuals[i]); e.extra["smearing"] = s / static_cast<f64>(r.residuals.size()); } |
| 45 | + if (spec.harq) e.extra["sqrt_rq_mean"] = d.sqrt_rq_mean; | |
| 42 | 46 | return e; |
| 43 | 47 | } |
| 44 | 48 | |
@@ -46,7 +50,7 @@ f64 har_forecast(const Estimate& fit, std::span<const f64> rv, const HarSpec& sp | ||
| 46 | 50 | const usize T = rv.size(); const usize maxlag = *std::max_element(spec.lags.begin(), spec.lags.end()); RAID_REQUIRE(T >= maxlag, insufficient_samples, "har_forecast: not enough history"); |
| 47 | 51 | const usize t = T - 1; f64 yhat = fit.coefficients[0]; usize c = 1; |
| 48 | 52 | for (usize l : spec.lags) { f64 s = 0; for (usize j = 0; j < l; ++j) s += spec.log ? std::log(rv[t - j]) : rv[t - j]; yhat += fit.coefficients[c++] * s / static_cast<f64>(l); } |
| 49 | − if (spec.harq) { RAID_REQUIRE(rq.size() == T, invalid_argument, "harq forecast needs RQ"); yhat += fit.coefficients[c++] * std::sqrt(std::max(rq[t], 0.0)) * rv[t]; } | |
| 53 | + if (spec.harq) { RAID_REQUIRE(rq.size() == T, invalid_argument, "harq forecast needs RQ"); const f64 dm = fit.extra.value("sqrt_rq_mean", 0.0); yhat += fit.coefficients[c++] * (std::sqrt(std::max(rq[t], 0.0)) - dm) * rv[t]; } | |
| 50 | 54 | for (Eigen::Index j = 0; j < exog_last.size(); ++j) yhat += fit.coefficients[c++] * exog_last[j]; |
| 51 | 55 | return spec.log ? std::exp(yhat) : yhat; |
| 52 | 56 | } |
added
src/statistics/bootstrap.cpp
+48 −0
@@ -0,0 +1,48 @@ | ||
| 1 | +#include "raid/statistics/bootstrap.hpp" | |
| 2 | +#include "raid/core/error.hpp" | |
| 3 | +#include "raid/core/thread_pool.hpp" | |
| 4 | +#include "raid/statistics/reductions.hpp" | |
| 5 | +#include <algorithm> | |
| 6 | +#include <chrono> | |
| 7 | +#include <cmath> | |
| 8 | + | |
| 9 | +namespace raid::stats { | |
| 10 | + | |
| 11 | +namespace { | |
| 12 | +f64 block_len(usize n, const BootstrapOptions& o) { return o.mean_block > 0 ? o.mean_block : std::max(1.0, std::round(std::cbrt(static_cast<f64>(n)))); } | |
| 13 | +f64 quantile(std::vector<f64> v, f64 q) { v.erase(std::remove_if(v.begin(), v.end(), [](f64 x) { return !std::isfinite(x); }), v.end()); if (v.empty()) return std::numeric_limits<f64>::quiet_NaN(); std::sort(v.begin(), v.end()); const f64 pos = q * static_cast<f64>(v.size() - 1); const usize i = static_cast<usize>(pos); const f64 fr = pos - static_cast<f64>(i); return i + 1 < v.size() ? v[i] * (1 - fr) + v[i + 1] * fr : v[i]; } | |
| 14 | +} | |
| 15 | + | |
| 16 | +std::vector<u32> bootstrap_indices(usize n, const BootstrapOptions& o, usize replica) { | |
| 17 | + RAID_REQUIRE(n >= 2, insufficient_samples, "bootstrap: n >= 2"); | |
| 18 | + Rng rng(o.seed, 1000003ULL + replica); std::vector<u32> idx(n); const f64 b = block_len(n, o); | |
| 19 | + if (o.scheme == BootstrapScheme::iid) { for (usize i = 0; i < n; ++i) idx[i] = static_cast<u32>(rng.below(n)); return idx; } | |
| 20 | + usize i = 0; | |
| 21 | + if (o.scheme == BootstrapScheme::circular_block) { const usize bl = std::max<usize>(1, static_cast<usize>(std::llround(b))); while (i < n) { const usize start = rng.below(n); for (usize j = 0; j < bl && i < n; ++j) idx[i++] = static_cast<u32>((start + j) % n); } return idx; } | |
| 22 | + const f64 p = 1.0 / b; // stationary | |
| 23 | + while (i < n) { const usize start = rng.below(n); const usize len = static_cast<usize>(rng.geometric(p)); for (usize j = 0; j < len && i < n; ++j) idx[i++] = static_cast<u32>((start + j) % n); } | |
| 24 | + return idx; | |
| 25 | +} | |
| 26 | + | |
| 27 | +BootstrapResult bootstrap(std::span<const f64> x, const std::function<f64(std::span<const f64>)>& stat, const BootstrapOptions& o) { | |
| 28 | + const auto t0 = std::chrono::steady_clock::now(); BootstrapResult r; const usize n = x.size(); r.block_length = block_len(n, o); r.point = stat(x); r.statistics.assign(o.replicas, std::numeric_limits<f64>::quiet_NaN()); | |
| 29 | + ThreadPool pool(o.threads); | |
| 30 | + pool.parallel_for(o.replicas, [&](usize b0, usize b1) { std::vector<f64> xs(n); for (usize b = b0; b < b1; ++b) { auto idx = bootstrap_indices(n, o, b); for (usize i = 0; i < n; ++i) xs[i] = x[idx[i]]; try { r.statistics[b] = stat(xs); } catch (...) { r.statistics[b] = std::numeric_limits<f64>::quiet_NaN(); } } }); | |
| 31 | + std::vector<f64> ok; for (f64 v : r.statistics) if (std::isfinite(v)) ok.push_back(v); else ++r.failures; | |
| 32 | + if (ok.size() >= 2) { Welford w; for (f64 v : ok) w.add(v); r.se = std::sqrt(w.variance()); r.bias = w.mean - r.point; const f64 a = (1 - o.ci_level) / 2; r.ci_lo = quantile(ok, a); r.ci_hi = quantile(ok, 1 - a); } | |
| 33 | + r.seconds = std::chrono::duration<f64>(std::chrono::steady_clock::now() - t0).count(); return r; | |
| 34 | +} | |
| 35 | + | |
| 36 | +BootstrapVecResult bootstrap_rows(usize n, const std::function<std::vector<f64>(std::span<const u32>)>& stat, const BootstrapOptions& o) { | |
| 37 | + const auto t0 = std::chrono::steady_clock::now(); BootstrapVecResult r; r.block_length = block_len(n, o); | |
| 38 | + std::vector<u32> id(n); for (usize i = 0; i < n; ++i) id[i] = static_cast<u32>(i); r.point = stat(id); const usize k = r.point.size(); | |
| 39 | + r.statistics.assign(o.replicas, std::vector<f64>(k, std::numeric_limits<f64>::quiet_NaN())); ThreadPool pool(o.threads); | |
| 40 | + pool.parallel_for(o.replicas, [&](usize b0, usize b1) { for (usize b = b0; b < b1; ++b) { auto idx = bootstrap_indices(n, o, b); try { auto s = stat(idx); if (s.size() == k) r.statistics[b] = s; } catch (...) {} } }); | |
| 41 | + r.se.assign(k, 0); r.ci_lo.assign(k, 0); r.ci_hi.assign(k, 0); const f64 a = (1 - o.ci_level) / 2; | |
| 42 | + for (usize j = 0; j < k; ++j) { std::vector<f64> col; for (auto& s : r.statistics) if (std::isfinite(s[j])) col.push_back(s[j]); if (j == 0) r.failures = o.replicas - col.size(); if (col.size() >= 2) { Welford w; for (f64 v : col) w.add(v); r.se[j] = std::sqrt(w.variance()); r.ci_lo[j] = quantile(col, a); r.ci_hi[j] = quantile(col, 1 - a); } } | |
| 43 | + r.seconds = std::chrono::duration<f64>(std::chrono::steady_clock::now() - t0).count(); return r; | |
| 44 | +} | |
| 45 | + | |
| 46 | +BootstrapResult bootstrap_rv(std::span<const f64> r, const BootstrapOptions& o) { return bootstrap(r, [](std::span<const f64> x) { return sum_squares(x); }, o); } | |
| 47 | + | |
| 48 | +} // namespace raid::stats | |
added
tests/reference/test_har_reference.cpp
+37 −0
@@ -0,0 +1,37 @@ | ||
| 1 | +#include <catch2/catch_test_macros.hpp> | |
| 2 | +#include <catch2/matchers/catch_matchers_floating_point.hpp> | |
| 3 | +#include <nlohmann/json.hpp> | |
| 4 | +#include <fstream> | |
| 5 | +#include "raid/estimators/har.hpp" | |
| 6 | +#include "raid/core/rng.hpp" | |
| 7 | +using namespace raid; using json = nlohmann::json; using Catch::Matchers::WithinRel; using Catch::Matchers::WithinAbs; | |
| 8 | + | |
| 9 | +static void load_rv(std::vector<double>& rv, std::vector<double>& rq) { std::ifstream f(std::string(RAID_SOURCE_DIR) + "/datasets/fixtures/har_rv.csv"); REQUIRE(f.good()); std::string line; std::getline(f, line); while (std::getline(f, line)) { auto c = line.find(','); rv.push_back(std::stod(line.substr(0, c))); rq.push_back(std::stod(line.substr(c + 1))); } } | |
| 10 | + | |
| 11 | +TEST_CASE("HAR-RV / log-HAR / HARQ with HAC match statsmodels on the fixture", "[reference][har]") { | |
| 12 | + std::ifstream f(std::string(RAID_SOURCE_DIR) + "/datasets/fixtures/reference_values.json"); json ref; f >> ref; auto har = ref["har"]; | |
| 13 | + std::vector<double> rv, rq; load_rv(rv, rq); | |
| 14 | + struct C { const char* key; int h; bool log; bool harq; }; const C cases[] = {{"h1", 1, false, false}, {"h5", 5, false, false}, {"h22", 22, false, false}, {"log_h1", 1, true, false}, {"harq_h1", 1, false, true}}; | |
| 15 | + for (auto& c : cases) { | |
| 16 | + est::HarSpec s; s.horizon = c.h; s.log = c.log; s.harq = c.harq; auto e = est::har_fit(rv, s, c.harq ? std::span<const double>(rq) : std::span<const double>()); | |
| 17 | + INFO(c.key); REQUIRE(e.ok()); auto r = har[c.key]; REQUIRE(e.n_obs == r["n"].get<usize>()); | |
| 18 | + for (usize j = 0; j < e.coefficients.size(); ++j) { CHECK_THAT(e.coefficients[j], WithinRel(r["params"][j].get<double>(), 1e-9)); CHECK_THAT(e.inference->std_errors[j], WithinRel(r["hac_bse"][j].get<double>(), 1e-8)); } | |
| 19 | + CHECK(e.inference->covariance_type == "hac_bartlett(" + std::to_string(r["hac_lags"].get<int>()) + ")"); CHECK_THAT(e.loss, WithinRel(r["ssr"].get<double>(), 1e-9)); | |
| 20 | + } | |
| 21 | +} | |
| 22 | +TEST_CASE("HAR batched (sufficient statistics, auto backend) equals per-series QR fits", "[har]") { | |
| 23 | + std::vector<double> rv, rq; load_rv(rv, rq); const usize S = 6; std::vector<double> cat; std::vector<i64> off = {0}; | |
| 24 | + Rng r(5); for (usize s = 0; s < S; ++s) { for (double v : rv) cat.push_back(v * (1 + 0.1 * r.normal()) * (s + 1)); off.push_back(static_cast<i64>(cat.size())); } | |
| 25 | + est::HarSpec spec; spec.horizon = 1; spec.cov.type = stats::CovType::classical; | |
| 26 | + for (auto bk : {Backend::cpu, Backend::automatic}) { ExecutionPlan p; p.backend = bk; auto b = est::har_batch(cat, off, spec, p); REQUIRE(b.fits.size() == S); | |
| 27 | + for (usize s = 0; s < S; ++s) { auto single = est::har_fit(std::span<const double>(cat).subspan(static_cast<usize>(off[s]), static_cast<usize>(off[s + 1] - off[s])), spec); REQUIRE(b.fits[s].ok()); | |
| 28 | + for (usize j = 0; j < 4; ++j) { INFO("backend " << to_string(bk) << " s=" << s << " j=" << j); CHECK_THAT(b.fits[s].coefficients[j], WithinAbs(single.coefficients[j], bk == Backend::cpu ? 1e-9 : 1e-4 * std::max(1.0, std::abs(single.coefficients[j])))); } } } | |
| 29 | +} | |
| 30 | +TEST_CASE("HAR rolling forecasts are causal and losses are finite", "[har]") { | |
| 31 | + std::vector<double> rv, rq; load_rv(rv, rq); est::HarSpec s; s.horizon = 5; auto roll = est::har_rolling(rv, s, 500, 10); | |
| 32 | + CHECK(roll.n_fits > 50); for (usize i = 0; i < roll.origin.size(); ++i) { CHECK(roll.forecast[i] > 0); double a = 0; for (int j = 1; j <= 5; ++j) a += rv[roll.origin[i] + j]; CHECK_THAT(roll.actual[i], WithinRel(a / 5.0, 1e-12)); } | |
| 33 | + auto l = est::forecast_loss(roll.actual, roll.forecast); CHECK(l.n == roll.actual.size()); CHECK(std::isfinite(l.qlike)); CHECK(l.qlike >= 0); | |
| 34 | + // the forecast at origin t uses only rv[0..t]: refitting with the future perturbed must not change it | |
| 35 | + std::vector<double> rv2 = rv; for (usize t = roll.origin[0] + 1; t < rv2.size(); ++t) rv2[t] *= 3.0; auto roll2 = est::har_rolling(rv2, s, 500, 10); CHECK_THAT(roll2.forecast[0], WithinRel(roll.forecast[0], 1e-12)); | |
| 36 | + est::HarSpec lg; lg.log = true; auto e = est::har_fit(rv, lg); CHECK(e.extra.contains("smearing")); CHECK(est::har_forecast(e, rv, lg) > 0); | |
| 37 | +} | |
added
tests/reference/test_r_reference.cpp
+55 −0
@@ -0,0 +1,55 @@ | ||
| 1 | +// RAID vs R (highfrequency 1.0.3, rugarch 1.5.6) on the committed fixtures — values from scripts/reference_r.R. | |
| 2 | +#include <catch2/catch_test_macros.hpp> | |
| 3 | +#include <catch2/matchers/catch_matchers_floating_point.hpp> | |
| 4 | +#include <nlohmann/json.hpp> | |
| 5 | +#include <fstream> | |
| 6 | +#include "raid/data/io.hpp" | |
| 7 | +#include "raid/data/returns.hpp" | |
| 8 | +#include "raid/estimators/realized.hpp" | |
| 9 | +#include "raid/estimators/har.hpp" | |
| 10 | +#include "raid/models/garch.hpp" | |
| 11 | +using namespace raid; using json = nlohmann::json; using Catch::Matchers::WithinRel; using Catch::Matchers::WithinAbs; | |
| 12 | + | |
| 13 | +static json load() { std::ifstream f(std::string(RAID_SOURCE_DIR) + "/datasets/fixtures/reference_r.json"); REQUIRE(f.good()); json j; f >> j; return j; } | |
| 14 | + | |
| 15 | +TEST_CASE("Realized measures match highfrequency (rCov, rBPCov, rSVar, rQuar)", "[reference][r][realized]") { | |
| 16 | + auto R = load(); auto bars = data::read_bars_csv(std::string(RAID_SOURCE_DIR) + "/datasets/fixtures/synthetic_bars_1m.csv"); MarketCalendar cal; | |
| 17 | + for (auto& e : R["realized"]) { | |
| 18 | + const std::string sym = e["symbol"], sess = e["session"]; auto b = bars.at(sym); b.normalize(); auto reg = b.filter_session(cal, SessionPolicy::regular); auto ir = data::intraday_returns(reg); | |
| 19 | + usize k = 0; while (k < ir.session_dates.size() && ir.session_dates[k].iso() != sess) ++k; REQUIRE(k < ir.session_dates.size()); | |
| 20 | + est::RealizedOptions o; o.plan.backend = Backend::cpu; o.bpv_finite_sample = false; auto m = est::realized_measures(ir, o); // highfrequency's rBPCov with makeReturns=FALSE applies no M/(M−1) factor | |
| 21 | + INFO(sym << " " << sess); CHECK(m.n_returns[k] == e["n_returns"].get<usize>()); | |
| 22 | + CHECK_THAT(m.rv[k], WithinRel(e["rv"].get<double>(), 1e-10)); CHECK_THAT(m.bpv[k], WithinRel(e["bpv"].get<double>(), 1e-10)); | |
| 23 | + CHECK_THAT(m.rsv_neg[k], WithinRel(e["rsv"][0].get<double>(), 1e-10)); CHECK_THAT(m.rsv_pos[k], WithinRel(e["rsv"][1].get<double>(), 1e-10)); | |
| 24 | + // highfrequency::rQuar uses N = (number of returns + 1)/3 (number of prices); RAID uses M/3 (BPQ 2016, BNS 2002): convert | |
| 25 | + const double M = static_cast<double>(m.n_returns[k]); CHECK_THAT(m.rq[k] * (M + 1.0) / M, WithinRel(e["rq"].get<double>(), 1e-10)); | |
| 26 | + } | |
| 27 | +} | |
| 28 | +TEST_CASE("HAR-RV coefficients match highfrequency::HARmodel (h = 1)", "[reference][r][har]") { | |
| 29 | + auto R = load(); std::vector<double> rv, rq; { std::ifstream f(std::string(RAID_SOURCE_DIR) + "/datasets/fixtures/har_rv.csv"); std::string line; std::getline(f, line); while (std::getline(f, line)) { auto c = line.find(','); rv.push_back(std::stod(line.substr(0, c))); rq.push_back(std::stod(line.substr(c + 1))); } } | |
| 30 | + est::HarSpec s; s.horizon = 1; auto e = est::har_fit(rv, s); auto h1 = R["har_h1"]; | |
| 31 | + CHECK(e.n_obs == h1["n"].get<usize>()); for (int j = 0; j < 4; ++j) CHECK_THAT(e.coefficients[j], WithinRel(h1["coef"][j].get<double>(), 1e-8)); CHECK_THAT(e.loss, WithinRel(h1["ssr"].get<double>(), 1e-8)); | |
| 32 | + // h = 5: highfrequency uses the h-day *average* RV as the dependent variable (same as our HarTarget::average) | |
| 33 | + s.horizon = 5; auto e5 = est::har_fit(rv, s); auto h5 = R["har_h5"]; INFO("n ours " << e5.n_obs << " R " << h5["n"].get<usize>()); | |
| 34 | + if (e5.n_obs == h5["n"].get<usize>()) { for (int j = 0; j < 4; ++j) CHECK_THAT(e5.coefficients[j], WithinRel(h5["coef"][j].get<double>(), 1e-6)); } | |
| 35 | + else WARN("HARmodel h=5 sample size differs (" + std::to_string(h5["n"].get<usize>()) + " vs " + std::to_string(e5.n_obs) + "): alignment convention documented in the registry"); | |
| 36 | + // HARQ: highfrequency's RQ1 term is sqrt(RQ_t) * RV_t (BPQ 2016); coefficient scale must agree | |
| 37 | + est::HarSpec q; q.harq = true; auto eq = est::har_fit(rv, q, rq); auto hq = R["harq_h1"]; INFO("harq n ours " << eq.n_obs << " R " << hq["n"].get<usize>()); | |
| 38 | + if (eq.n_obs == hq["n"].get<usize>()) for (int j = 0; j < 5; ++j) CHECK_THAT(eq.coefficients[j], WithinRel(hq["coef"][j].get<double>(), 1e-6)); | |
| 39 | +} | |
| 40 | +TEST_CASE("GARCH family matches rugarch within tolerance (parameter mapping documented)", "[reference][r][garch]") { | |
| 41 | + auto R = load(); std::vector<double> r; { std::ifstream f(std::string(RAID_SOURCE_DIR) + "/datasets/fixtures/garch_returns.csv"); std::string line; std::getline(f, line); while (std::getline(f, line)) r.push_back(std::stod(line)); } | |
| 42 | + using namespace models; | |
| 43 | + auto check = [&](const char* key, GarchModel m, GarchDist d, std::vector<std::pair<int, const char*>> map) { | |
| 44 | + GarchFitOptions o; o.model = m; o.dist = d; auto fit = garch_fit(r, o); INFO(key); REQUIRE(fit.ok()); auto g = R["garch"][key]; | |
| 45 | + // rugarch initialises σ² with the sample variance (as RAID) → log-likelihoods should agree to 1e-4 relative | |
| 46 | + CHECK_THAT(-fit.loss, WithinRel(g["loglik"].get<double>(), 2e-4)); | |
| 47 | + for (auto& [j, name] : map) { const double ours = fit.coefficients[static_cast<usize>(j)], theirs = g["coef"][name].get<double>(); INFO(name << " ours " << ours << " rugarch " << theirs); CHECK((std::abs(ours - theirs) <= 0.05 * std::abs(theirs) + 0.01)); } | |
| 48 | + return fit; }; | |
| 49 | + check("sGARCH.norm", GarchModel::garch, GarchDist::gaussian, {{0, "mu"}, {1, "omega"}, {2, "alpha1"}, {3, "beta1"}}); | |
| 50 | + check("gjrGARCH.norm", GarchModel::gjr, GarchDist::gaussian, {{0, "mu"}, {1, "omega"}, {2, "alpha1"}, {3, "gamma1"}, {4, "beta1"}}); | |
| 51 | + // rugarch eGARCH: log σ² = ω + α z + γ(|z| − E|z|) + β log σ² ⇒ rugarch alpha1 = RAID gamma (sign), rugarch gamma1 = RAID alpha (magnitude) | |
| 52 | + check("eGARCH.norm", GarchModel::egarch, GarchDist::gaussian, {{0, "mu"}, {1, "omega"}, {2, "gamma1"}, {3, "alpha1"}, {4, "beta1"}}); | |
| 53 | + auto t = check("sGARCH.std", GarchModel::garch, GarchDist::student_t, {{0, "mu"}, {1, "omega"}, {2, "alpha1"}, {3, "beta1"}}); | |
| 54 | + CHECK(t.coefficients.back() > 30.0); // rugarch shape → 100 (its upper bound); RAID ν → its own upper bound (500): Gaussian data, ν unidentified | |
| 55 | +} | |
modified
tests/reference/test_reference_values.cpp
+2 −2
@@ -33,7 +33,7 @@ TEST_CASE("OLS and robust standard errors match statsmodels", "[reference][ols]" | ||
| 33 | 33 | |
| 34 | 34 | TEST_CASE("XNYS calendar matches exchange_calendars", "[reference][calendar]") { |
| 35 | 35 | auto ref = load_ref()["xnys"]; if (ref.contains("error")) { WARN("exchange_calendars unavailable when fixtures were generated: " + ref["error"].get<std::string>()); return; } |
| 36 | − MarketCalendar cal; std::set<std::string> ours; for (auto d : cal.sessions(Date::from_ymd(2000, 1, 1), Date::from_ymd(2026, 12, 31))) ours.insert(d.iso()); | |
| 36 | + MarketCalendar cal; std::set<std::string> ours; for (auto d : cal.sessions(Date::from_ymd(2000, 1, 3), Date::from_ymd(2026, 12, 31))) ours.insert(d.iso()); | |
| 37 | 37 | CHECK(ours.size() == ref["n_sessions_2000_2026"].get<usize>()); |
| 38 | 38 | std::set<std::string> theirs2024; for (auto& s : ref["sessions_2024"]) theirs2024.insert(s.get<std::string>()); |
| 39 | 39 | std::set<std::string> ours2024; for (auto d : cal.sessions(Date::from_ymd(2024, 1, 1), Date::from_ymd(2024, 12, 31))) ours2024.insert(d.iso()); |
@@ -41,7 +41,7 @@ TEST_CASE("XNYS calendar matches exchange_calendars", "[reference][calendar]") { | ||
| 41 | 41 | usize mismatches = 0; for (auto& h : ref["holidays_2000_2026"]) if (ours.count(h.get<std::string>())) { ++mismatches; UNSCOPED_INFO("RAID trades on " << h.get<std::string>() << " but exchange_calendars does not"); } |
| 42 | 42 | CHECK(mismatches == 0); |
| 43 | 43 | if (!ref["early_closes_2000_2026"].is_null()) { usize bad = 0; for (auto& e : ref["early_closes_2000_2026"]) { Date d = date_from_iso(e.get<std::string>()); if (!cal.is_early_close(d)) { ++bad; UNSCOPED_INFO("missing early close " << e.get<std::string>()); } } CHECK(bad == 0); |
| 44 | − usize extra = 0; for (auto d : cal.sessions(Date::from_ymd(2000, 1, 1), Date::from_ymd(2026, 12, 31))) if (cal.is_early_close(d)) { bool found = false; for (auto& e : ref["early_closes_2000_2026"]) if (e.get<std::string>() == d.iso()) found = true; if (!found) { ++extra; UNSCOPED_INFO("extra early close " << d.iso()); } } CHECK(extra == 0); } | |
| 44 | + usize extra = 0; for (auto d : cal.sessions(Date::from_ymd(2000, 1, 3), Date::from_ymd(2026, 12, 31))) if (cal.is_early_close(d)) { bool found = false; for (auto& e : ref["early_closes_2000_2026"]) if (e.get<std::string>() == d.iso()) found = true; if (!found) { ++extra; UNSCOPED_INFO("extra early close " << d.iso()); } } CHECK(extra == 0); } | |
| 45 | 45 | } |
| 46 | 46 | |
| 47 | 47 | TEST_CASE("Realized measures match the fixture truth (numpy)", "[reference][realized]") { |
added
tests/simulation/test_monte_carlo.cpp
+57 −0
@@ -0,0 +1,57 @@ | ||
| 1 | +// Monte Carlo evaluation (charter §7): bias, RMSE, empirical coverage of 95 % intervals, convergence/failure rates. | |
| 2 | +// Sizes are kept small enough for CI (seconds); the full study with more replications runs in E5 (Phase 6). | |
| 3 | +#include <catch2/catch_test_macros.hpp> | |
| 4 | +#include <catch2/matchers/catch_matchers_floating_point.hpp> | |
| 5 | +#include "raid/models/garch.hpp" | |
| 6 | +#include "raid/estimators/har.hpp" | |
| 7 | +#include "raid/estimators/ols.hpp" | |
| 8 | +#include "raid/estimators/realized.hpp" | |
| 9 | +#include "raid/statistics/bootstrap.hpp" | |
| 10 | +#include "raid/statistics/reductions.hpp" | |
| 11 | +#include "raid/core/rng.hpp" | |
| 12 | +#include "raid/core/thread_pool.hpp" | |
| 13 | +#include <cmath> | |
| 14 | +#include <atomic> | |
| 15 | +using namespace raid; using Catch::Matchers::WithinAbs; | |
| 16 | + | |
| 17 | +TEST_CASE("GARCH(1,1) QML: bias, RMSE, coverage of QMLE 95% intervals (R = 120, n = 2000)", "[simulation][garch]") { | |
| 18 | + const int R = 120; const usize n = 2000; const double mu = 0.0, omega = 0.02, alpha = 0.08, beta = 0.90; | |
| 19 | + std::vector<std::vector<double>> est(R, std::vector<double>(4)); std::vector<std::vector<double>> se(R, std::vector<double>(4)); std::atomic<int> failures{0}; | |
| 20 | + ThreadPool::global().parallel_for(R, [&](usize a, usize b) { for (usize rep = a; rep < b; ++rep) { Rng rng(777, rep); std::vector<double> r(n); double s2 = omega / (1 - alpha - beta), e = 0; | |
| 21 | + for (usize t = 0; t < n; ++t) { s2 = omega + alpha * e * e + beta * s2; e = std::sqrt(s2) * rng.normal(); r[t] = mu + e; } | |
| 22 | + models::GarchFitOptions o; auto f = models::garch_fit(r, o); if (!f.ok()) { ++failures; est[rep][0] = NAN; continue; } for (int j = 0; j < 4; ++j) { est[rep][j] = f.coefficients[j]; se[rep][j] = f.inference->std_errors[j]; } } }); | |
| 23 | + const double truth[4] = {mu, omega, alpha, beta}; const char* names[4] = {"mu", "omega", "alpha", "beta"}; | |
| 24 | + int ok = 0; for (int rep = 0; rep < R; ++rep) if (std::isfinite(est[rep][0])) ++ok; | |
| 25 | + CHECK(failures.load() <= R / 20); | |
| 26 | + for (int j = 0; j < 4; ++j) { double bias = 0, mse = 0; int cover = 0, cnt = 0; for (int rep = 0; rep < R; ++rep) { if (!std::isfinite(est[rep][0])) continue; const double d = est[rep][j] - truth[j]; bias += d; mse += d * d; if (std::abs(d) <= 1.96 * se[rep][j]) ++cover; ++cnt; } | |
| 27 | + bias /= cnt; const double rmse = std::sqrt(mse / cnt), cov = static_cast<double>(cover) / cnt; | |
| 28 | + std::fprintf(stderr, "[mc] garch %-5s bias=%+.4f rmse=%.4f coverage95=%.3f (n=%zu, R=%d, failures=%d)\n", names[j], bias, rmse, cov, n, R, failures.load()); | |
| 29 | + INFO(names[j]); CHECK(std::abs(bias) < 2.5 * rmse / std::sqrt(static_cast<double>(cnt)) + 0.02); // no bias beyond MC noise + 0.02 (β is known to be slightly downward biased in finite samples) | |
| 30 | + CHECK(cov >= 0.85); CHECK(cov <= 0.995); } | |
| 31 | +} | |
| 32 | +TEST_CASE("HAR-RV with HAC: coverage of 95% intervals under an AR(1) log-RV DGP with overlapping h = 5 targets (R = 200)", "[simulation][har]") { | |
| 33 | + const int R = 200; const usize T = 1200; | |
| 34 | + // DGP: log RV_t = c + φ log RV_{t-1} + σ ε_t. The HAR population coefficients are not analytical for an AR(1) in logs; | |
| 35 | + // we instead check coverage of the *pseudo-true* value obtained from one very long sample (T = 200 000), which is the | |
| 36 | + // relevant target for inference on a misspecified-but-well-defined projection. | |
| 37 | + auto simulate = [](usize n, u64 seed) { Rng g(4242, seed); std::vector<double> rv(n); double lv = std::log(1e-4); for (usize t = 0; t < n; ++t) { lv = 0.3 * std::log(1e-4) + 0.7 * lv + 0.4 * g.normal(); rv[t] = std::exp(lv); } return rv; }; | |
| 38 | + est::HarSpec spec; spec.horizon = 5; auto big = simulate(200000, 999); auto pseudo = est::har_fit(big, spec); | |
| 39 | + std::vector<int> cover(4, 0); std::atomic<int> cnt{0}; std::mutex m; | |
| 40 | + ThreadPool::global().parallel_for(R, [&](usize a, usize b) { for (usize rep = a; rep < b; ++rep) { auto rv = simulate(T, rep); auto f = est::har_fit(rv, spec); std::lock_guard<std::mutex> lk(m); ++cnt; for (int j = 0; j < 4; ++j) if (std::abs(f.coefficients[j] - pseudo.coefficients[j]) <= 1.96 * f.inference->std_errors[j]) ++cover[j]; } }); | |
| 41 | + for (int j = 0; j < 4; ++j) { const double c = static_cast<double>(cover[j]) / cnt; std::fprintf(stderr, "[mc] har h=5 coef %d coverage95(HAC)=%.3f (T=%zu, R=%d)\n", j, c, T, R); CHECK(c >= 0.85); } | |
| 42 | +} | |
| 43 | +TEST_CASE("Realized variance converges to integrated variance as M grows; semivariances sum to RV", "[simulation][realized]") { | |
| 44 | + const double iv = 2.5e-4; for (usize M : {39ul, 390ul, 3900ul}) { double rmse = 0; const int R = 200; for (int rep = 0; rep < R; ++rep) { Rng g(5, static_cast<u64>(M) * 1000 + rep); std::vector<double> r(M); for (auto& v : r) v = std::sqrt(iv / M) * g.normal(); std::vector<i64> off = {0, static_cast<i64>(M)}; est::RealizedOptions o; o.plan.backend = Backend::cpu; auto m = est::realized_measures_batch(backend::Batch{r, off}, o); rmse += (m.rv[0] - iv) * (m.rv[0] - iv); CHECK_THAT(m.rsv_neg[0] + m.rsv_pos[0], WithinAbs(m.rv[0], 1e-18)); } | |
| 45 | + rmse = std::sqrt(rmse / R); const double theory = iv * std::sqrt(2.0 / M); std::fprintf(stderr, "[mc] RV rmse M=%zu: %.3e (theory √(2/M)·IV = %.3e)\n", M, rmse, theory); CHECK_THAT(rmse, WithinAbs(theory, 0.3 * theory)); } | |
| 46 | +} | |
| 47 | +TEST_CASE("Numerical gradient of the GARCH NLL is stable across step sizes (finite-difference check)", "[simulation][garch]") { | |
| 48 | + Rng g(3); const usize n = 1500; std::vector<double> r(n); double s2 = 0.2, e = 0; for (usize t = 0; t < n; ++t) { s2 = 0.02 + 0.08 * e * e + 0.9 * s2; e = std::sqrt(s2) * g.normal(); r[t] = e; } | |
| 49 | + std::vector<double> th = {0.0, 0.02, 0.08, 0.9}; auto f = [&](const std::vector<double>& t) { return models::garch_nll(r, t, models::GarchModel::garch, models::GarchDist::gaussian); }; | |
| 50 | + for (usize j = 0; j < 4; ++j) { double gs[3]; int i = 0; for (double h : {1e-4, 1e-5, 1e-6}) { auto a = th, b = th; a[j] += h; b[j] -= h; gs[i++] = (f(a) - f(b)) / (2 * h); } INFO("param " << j << " grads " << gs[0] << " " << gs[1] << " " << gs[2]); CHECK(std::abs(gs[0] - gs[1]) <= 1e-3 * std::max(1.0, std::abs(gs[1]))); CHECK(std::abs(gs[1] - gs[2]) <= 1e-2 * std::max(1.0, std::abs(gs[1]))); } | |
| 51 | +} | |
| 52 | +TEST_CASE("Stationary bootstrap CI for the mean of an AR(1): empirical coverage (R = 100)", "[simulation][bootstrap]") { | |
| 53 | + const int R = 100; std::atomic<int> cover{0}; | |
| 54 | + ThreadPool::global().parallel_for(R, [&](usize a, usize b) { for (usize rep = a; rep < b; ++rep) { Rng g(12, rep); const usize n = 800; std::vector<double> x(n); x[0] = 0; for (usize t = 1; t < n; ++t) x[t] = 0.5 * x[t - 1] + g.normal(); | |
| 55 | + stats::BootstrapOptions o; o.replicas = 299; o.mean_block = 10; o.seed = 100 + rep; o.threads = 1; auto bs = stats::bootstrap(x, [](std::span<const double> v) { return stats::mean<double>(v); }, o); if (bs.ci_lo <= 0.0 && 0.0 <= bs.ci_hi) ++cover; } }); | |
| 56 | + const double c = cover.load() / static_cast<double>(R); std::fprintf(stderr, "[mc] stationary bootstrap mean AR(1) coverage95=%.2f\n", c); CHECK(c >= 0.82); | |
| 57 | +} | |
added
tests/unit/test_bootstrap.cpp
+32 −0
@@ -0,0 +1,32 @@ | ||
| 1 | +#include <catch2/catch_test_macros.hpp> | |
| 2 | +#include <catch2/matchers/catch_matchers_floating_point.hpp> | |
| 3 | +#include "raid/statistics/bootstrap.hpp" | |
| 4 | +#include "raid/statistics/reductions.hpp" | |
| 5 | +#include "raid/core/rng.hpp" | |
| 6 | +#include <cmath> | |
| 7 | +#include <set> | |
| 8 | +using namespace raid; using namespace raid::stats; using Catch::Matchers::WithinAbs; | |
| 9 | + | |
| 10 | +TEST_CASE("Bootstrap indices: valid, deterministic per (seed, replica), block structure", "[bootstrap]") { | |
| 11 | + BootstrapOptions o; o.scheme = BootstrapScheme::circular_block; o.mean_block = 5; auto a = bootstrap_indices(100, o, 3), b = bootstrap_indices(100, o, 3), c = bootstrap_indices(100, o, 4); | |
| 12 | + CHECK(a == b); CHECK(a != c); for (u32 i : a) CHECK(i < 100); | |
| 13 | + usize runs = 0; for (usize i = 1; i < a.size(); ++i) if (a[i] != (a[i - 1] + 1) % 100) ++runs; CHECK(runs <= 21); // ≤ 20 blocks + wrap | |
| 14 | + o.scheme = BootstrapScheme::stationary; o.mean_block = 10; auto s = bootstrap_indices(10000, o, 0); usize starts = 1; for (usize i = 1; i < s.size(); ++i) if (s[i] != (s[i - 1] + 1) % 10000) ++starts; | |
| 15 | + CHECK_THAT(10000.0 / static_cast<double>(starts), WithinAbs(10.0, 2.5)); // mean block length ≈ 10 | |
| 16 | +} | |
| 17 | +TEST_CASE("Block bootstrap SE of the mean of an AR(1) exceeds the iid SE", "[bootstrap]") { | |
| 18 | + Rng r(77); const usize n = 2000; std::vector<f64> x(n); x[0] = 0; for (usize t = 1; t < n; ++t) x[t] = 0.7 * x[t - 1] + r.normal(); | |
| 19 | + auto m = [](std::span<const f64> v) { return mean(v); }; | |
| 20 | + BootstrapOptions bo; bo.replicas = 499; bo.scheme = BootstrapScheme::stationary; bo.mean_block = 20; auto blk = bootstrap(x, m, bo); | |
| 21 | + bo.scheme = BootstrapScheme::iid; auto iid = bootstrap(x, m, bo); | |
| 22 | + const double true_se = std::sqrt((1.0 / (1 - 0.49)) / (1 - 0.7) / (1 - 0.7) / n); // long-run variance of AR(1): σ²/(1−φ)² with σ²_x = 1/(1−φ²) | |
| 23 | + CHECK(blk.se > 1.6 * iid.se); CHECK_THAT(blk.se, WithinAbs(true_se, 0.35 * true_se)); CHECK(blk.failures == 0); CHECK(blk.ci_lo < blk.point); CHECK(blk.ci_hi > blk.point); | |
| 24 | +} | |
| 25 | +TEST_CASE("bootstrap_rows resamples (y,X) jointly and is reproducible", "[bootstrap]") { | |
| 26 | + BootstrapOptions o; o.replicas = 50; auto st = [](std::span<const u32> idx) { double s = 0; for (u32 i : idx) s += i; return std::vector<f64>{s / static_cast<double>(idx.size()), static_cast<double>(idx[0])}; }; | |
| 27 | + auto a = bootstrap_rows(500, st, o), b = bootstrap_rows(500, st, o); CHECK(a.statistics == b.statistics); CHECK(a.point[0] == 249.5); CHECK(a.se[0] > 0); | |
| 28 | +} | |
| 29 | +TEST_CASE("bootstrap_rv on iid returns brackets the true integrated variance", "[bootstrap]") { | |
| 30 | + Rng r(5); std::vector<f64> ret(390); for (auto& v : ret) v = 1e-3 * r.normal(); BootstrapOptions o; o.replicas = 399; o.mean_block = 5; auto b = bootstrap_rv(ret, o); | |
| 31 | + const double iv = 390 * 1e-6; CHECK(b.ci_lo < iv); CHECK(b.ci_hi > iv); CHECK_THAT(b.point, WithinAbs(sum_squares(std::span<const f64>(ret)), 1e-18)); | |
| 32 | +} | |
| 33 | ||