spb/wp46_uqo

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		1	+# Model card — `aparch11` (APARCH(1,1), asymmetric power ARCH)
		2	+
		3	+## 1. Identity
		4	+- **model_id:** `aparch11` · **family:** power · **tier:** A · **status:** implemented · **impl:** arch 8.0.0 (`APARCH(p=1, o=1, q=1)`, δ estimated)
		5	+- **Label:** APARCH(1,1) · **distributions:** normal, t, ged, skewt
		6	+- **Seminal citation:** `ding1993long`
		7	+- **Other references:** `ling2002necessary` (moment conditions), `he1999properties`, `giot2003value` (skewed-t APARCH for VaR), `brownlees2011practical`, `hansen2005forecast`.
		8	+
		9	+## 2. Definition (arch parameterisation)
		10	+$$r_t = \mu + \varepsilon_t,\ \varepsilon_t = \sigma_t z_t,\qquad \sigma_t^{\delta} = \omega + \alpha\big(|\varepsilon_{t-1}| - \gamma\,\varepsilon_{t-1}\big)^{\delta} + \beta\,\sigma_{t-1}^{\delta} ,$$
		11	+`aparch_recursion`: pre-sample shock $= \sqrt{\text{backcast}}$ (variance backcast) and $\sigma_0^\delta$ term $= \text{backcast}^{\delta/2}$; $\sigma_t^2 = (\sigma_t^\delta)^{2/\delta}$, clamped to arch's variance bounds. Innovations: normal, t, GED, Hansen skew-t.
		12	+
		13	+## 3. Parameter space and admissibility
		14	+- Parameters: `mu, omega, alpha[1], gamma[1], beta[1], delta` (+ distribution).
		15	+- arch bounds: $\omega \in [0, 10v]$ with $v = \max(\overline{|\varepsilon|^{0.5}}, \overline{\varepsilon^2})$, $\alpha \in [0,1]$, $\gamma \in [-0.9997, 0.9997]$, $\beta \in [0,1]$, $\delta \in [0.05, 4]$; linear constraints $\omega, \alpha \ge 0$, $|\gamma| < 0.9997$, $\beta \ge 0$, $0.05 < \delta < 4$, $\alpha + \beta < 1$ (arch's generic persistence constraint, **not** the Ding–Granger–Engle stationarity condition $\alpha E[(|z| - \gamma z)^\delta] + \beta < 1$, `ling2002necessary`).
		16	+- Admissibility flag: **always `True`** (no model-specific check in `ArchModel.fit`).
		17	+
		18	+## 4. Nesting and relations
		19	+Nests `garch11` (δ = 2, γ = 0), `gjr11` (δ = 2; with $\alpha_{GJR} = \alpha(1-\gamma)^2$, $\gamma_{GJR} = \alpha[(1+\gamma)^2 - (1-\gamma)^2]$), `avgarch11` (δ = 1, γ = 0), `tarch11` (δ = 1), Higgins–Bera NARCH (γ = 0, free δ). Literature-matrix caveat: "multi-step forecast of h not closed-form when δ ≠ 2".
		20	+
		21	+## 5. Estimation
		22	+arch `ConstantMean` + `APARCH(1,1,1)` + distribution; SLSQP (`maxiter` 1000) with the bounds/constraints of §3; starting values from arch's grid α ∈ {0.03, 0.05, 0.08, 0.15} × γ ∈ {−0.5, 0, 0.5} × (α+β) ∈ {0.8, 0.9, 0.95, 0.975, 0.99} × δ ∈ {0.5, 1.2, 1.8}, best Gaussian log-likelihood; variance backcast (0.94ⁱ weights on ε²); ≤ 2 restarts (`ArchModel.fit`). Point estimates only.
		23	+
		24	+## 6. Forecasting
		25	+h = 1 analytic (recursion, then $(\cdot)^{2/\delta}$). **h > 1 by simulation** (arch raises for analytic APARCH multi-step): 2,000 paths of $\sigma^\delta_{t+k}$ via `APARCH._simulate_paths` (shock $= z\,\sigma$, $(|{\rm shock}| - \gamma\,{\rm shock})^\delta$ feeds the next step), $\hat\sigma^2_{t+k|t}$ = path mean of $(\sigma^\delta_{t+k})^{2/\delta}$. Normal: seeded (46 + origin); t/GED/skew-t: arch's unseeded fitted-distribution simulator (`_discrepancies.md`). Cumulative = sum over k ≤ h. Method: `simulation` (h > 1).
		26	+
		27	+## 7. Implementation
		28	+- `src/wp46/models/arch_models.py` (`SPECS["aparch11"]`), arch 8.0.0 (`sheppard2025arch`).
		29	+- Cross-check (`results/metrics/rugarch_crosscheck.json`, rugarch `apARCH`, t): −3372.7593 (ours) vs −3372.8489 (rugarch), **difference +0.0896** (ours higher; δ = 1.81, γ = 0.232, ν = 7.02 on GJR-t data).
		30	+- Tests: `tests/replication/test_rugarch.py` (fit recorded in the JSON); pilot `aparch11__t`: 207/207 refits `ok`, 1,032/1,032 forecasts `ok` on CL.
		31	+
		32	+## 8. Known limitations / failure modes
		33	+δ and γ are jointly weakly identified (flat likelihood ridges; δ can run to the bounds 0.05/4 or the persistence constraint binds); γ bound ±0.9997; simulation forecasts with Monte-Carlo error, unseeded for non-normal laws; no admissibility flag beyond optimizer constraints; stationarity under the estimated (γ, δ) is not verified.
		34	+
		35	+## 9. Computational footprint
		36	+Most expensive arch-backed estimation (6–8 parameters, 180 grid starting values evaluated before SLSQP): pilot median fit time per refit (t) 0.0321 s (CL) / 0.0406 s (EURUSD), max 0.07 s; forecasts by simulation (22 × 2,000 draws per origin).
		37	+
		38	+## 10. Literature expectations
		39	+`ding1993long` found δ ≈ 1.4 on S&P 500 with strong asymmetry; `hansen2005forecast` report APARCH among the best on IBM; `giot2003value` favour skewed-t APARCH for VaR; `brownlees2011practical` include APARCH among the top asymmetric models. Expectation: competitive with GJR/EGARCH at h = 1 on equities (H1) at the highest estimation and forecasting cost of Tier A (H5), with the largest failure/boundary rate.

added model_cards/avgarch11.md +39 −0

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		1	+# Model card — `avgarch11` (AVGARCH(1,1), Taylor–Schwert absolute-value GARCH)
		2	+
		3	+## 1. Identity
		4	+- **model_id:** `avgarch11` · **family:** power · **tier:** A · **status:** implemented · **impl:** arch 8.0.0 (`GARCH(p=1, o=0, q=1, power=1.0)`, arch name "AVGARCH")
		5	+- **Label:** AVGARCH(1,1) (Taylor-Schwert) · **distributions:** normal, t, ged, skewt
		6	+- **Seminal citation:** `taylor2008modelling` (2nd ed. of Taylor 1986; the 1986 first edition has no verified record — see `docs/literature_matrix.md`, Unverified) and `schwert1989why`.
		7	+- **Other references:** `ding1993long` (APARCH nests it, δ = 1, γ = 0), `zakoian1994threshold`.
		8	+
		9	+## 2. Definition (arch, power = 1)
		10	+$$r_t = \mu + \varepsilon_t,\ \varepsilon_t = \sigma_t z_t,\qquad \sigma_t = \omega + \alpha\,|\varepsilon_{t-1}| + \beta\,\sigma_{t-1} ,$$
		11	+pre-sample $|\varepsilon|$ and $\sigma$ replaced by the power-1 backcast ($\sum_{i<75} w_i|\varepsilon_i|$, $w_i \propto 0.94^i$). Innovations: normal, t, GED, Hansen skew-t.
		12	+
		13	+## 3. Parameter space and admissibility
		14	+- Parameters: `mu, omega, alpha[1], beta[1]` (+ distribution).
		15	+- arch bounds/constraints: $\omega \in [10^{-8}v_1, 10v_1]$ ($v_1 = \overline{|\varepsilon|}$), $\alpha, \beta \in [0,1]$, $\omega > 0$, $\alpha, \beta \ge 0$, $\alpha + \beta < 1$ (arch's generic constraint; the stationarity of $\sigma_t$ requires $\alpha E|z| + \beta < 1$, which is implied).
		16	+- Admissibility flag: **always `True`** (no check in `ArchModel.fit` for `avgarch11`).
		17	+
		18	+## 4. Nesting and relations
		19	+`aparch11` with δ = 1, γ = 0; `tarch11` with γ = 0. Not nested in `garch11` (power 1 vs 2). Symmetric; the only symmetric power-1 model of the registry.
		20	+
		21	+## 5. Estimation
		22	+arch SLSQP (`maxiter` 1000), grid starting values, power-1 backcast, ≤ 2 restarts (`ArchModel.fit`). Point estimates only.
		23	+
		24	+## 6. Forecasting
		25	+h = 1 analytic ($\hat\sigma_{t+1|t}^2 = (\omega + \alpha|\varepsilon_t| + \beta\sigma_t)^2$); h > 1 by **simulation** (2,000 paths of $\sigma_{t+k}$ via `GARCH._simulate_paths`, power 1; variance forecast = path mean of $\sigma_{t+k}^2$; note $E[\sigma_{t+k}^2] \ne (E\sigma_{t+k})^2$, which is why no analytic rule is used). Normal: seeded (46 + origin); t/GED/skew-t: arch's unseeded distribution simulator (`_discrepancies.md`). Cumulative = sum over k ≤ h. Method: `simulation` for h > 1.
		26	+
		27	+## 7. Implementation
		28	+- `src/wp46/models/arch_models.py` (`SPECS["avgarch11"]`), arch 8.0.0.
		29	+- Cross-check: none (not in the rugarch fixture script; rugarch `fGARCH(AVGARCH)` was not run).
		30	+- Tests: none dedicated.
		31	+
		32	+## 8. Known limitations / failure modes
		33	+No admissibility flag beyond optimizer constraints; simulation forecasts (Monte-Carlo error, unseeded for non-normal laws); boundary optima as for GARCH(1,1).
		34	+
		35	+## 9. Computational footprint
		36	+Estimation like `garch11`; simulation forecasts (22 × 2,000 draws per origin). No pilot timing.
		37	+
		38	+## 10. Literature expectations
		39	+`taylor2008modelling`/`schwert1989why` introduced absolute-value dynamics as more robust to outliers; `ding1993long` found δ ≈ 1.4 for S&P 500 (between AVGARCH and GARCH); `hansen2005forecast` include TS-GARCH variants without finding them superior to GARCH(1,1). Expectation: similar to GARCH(1,1), possibly more robust in stressed regimes (H6), at a higher forecasting cost (H5).

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		1	+# Model card — `egarch11` (EGARCH(1,1))
		2	+
		3	+## 1. Identity
		4	+- **model_id:** `egarch11` · **family:** asymmetric · **tier:** A · **status:** implemented · **impl:** arch 8.0.0 (`EGARCH(p=1, o=1, q=1)`)
		5	+- **Label:** EGARCH(1,1) · **distributions:** normal, t, ged, skewt
		6	+- **Seminal citation:** `nelson1991conditional`
		7	+- **Other references:** `straumann2006quasi` (EGARCH QMLE theory), `harvey2013dynamic`/`harvey2014egarch` (Beta-t-EGARCH, score-driven relative), `hansen2005forecast`, `brownlees2011practical`.
		8	+
		9	+## 2. Definition (arch parameterisation)
		10	+$$r_t = \mu + \varepsilon_t,\ \varepsilon_t = \sigma_t z_t,\ e_t = \varepsilon_t/\sigma_t,\qquad \ln\sigma_t^2 = \omega + \alpha\big(|e_{t-1}| - \sqrt{2/\pi}\big) + \gamma\,e_{t-1} + \beta\,\ln\sigma_{t-1}^2 .$$
		11	+Here **arch's `alpha[1]` multiplies the magnitude term and `gamma[1]` the sign term** (Nelson writes $g(z) = \theta z + \gamma(|z| - E|z|)$; rugarch writes α on the sign and γ on the magnitude — the cross-check confirms the swap: our α = 0.1565 ↔ rugarch γ₁ = 0.1565, our γ = −0.0444 ↔ rugarch α₁ = −0.0444). The centring constant is $\sqrt{2/\pi} = E|z|$ for the normal only; for t/GED/skew-t $E|z| \ne \sqrt{2/\pi}$ and the difference $\alpha(E|z| - \sqrt{2/\pi})$ is absorbed by ω (observationally equivalent, but ω is not Nelson's ω). Pre-sample: $\ln\sigma_0^2 = \omega + \beta\cdot\ln(\text{backcast})$ (the α, γ terms are omitted at t = 0). $\ln\sigma_t^2$ is capped at `LNSIGMA_MAX` and $\sigma_t^2$ clamped to arch's variance bounds. Innovations: normal, t, GED, Hansen skew-t.
		12	+
		13	+## 3. Parameter space and admissibility
		14	+- Parameters: `mu, omega, alpha[1], gamma[1], beta[1]` (+ distribution).
		15	+- arch bounds: $\omega \in [\ln v - \ln 10^4, \ln v + \ln 10^4]$ ($v = \overline{\varepsilon^2}$), $\alpha, \gamma \in (-\infty, \infty)$, $\beta \in [0, 1]$; constraint $\beta < 1$. arch therefore excludes $\beta < 0$ (Nelson's condition is $|\beta| < 1$).
		16	+- Admissibility flag (`ArchModel.fit`): $|\beta| < 1$ (log-variance stationarity). No positivity constraints are needed (log specification). Note the literature matrix caveat: stationarity is $|\beta| < 1$, not $\alpha + \beta < 1$.
		17	+
		18	+## 4. Nesting and relations
		19	+Does not nest `garch11` (log vs level). γ = 0 gives a symmetric log-GARCH; the Beta-t-EGARCH of Harvey is the score-driven counterpart (not in the registry; `gas_t11` is the variance-level GAS). Exponential form of the asymmetry shared with `realegarch11` (unsupported).
		20	+
		21	+## 5. Estimation
		22	+arch `ConstantMean` + `EGARCH(1,1,1)` + distribution; SLSQP (`maxiter` 1000) with the bounds of §3; starting values from arch's grid (α ∈ {0.01, 0.05, 0.1, 0.2}, γ ∈ {−0.1, 0, 0.1}, β ∈ {0.5, 0.7, 0.9, 0.98}, ω = (1−β)·ln v̄; best Gaussian log-likelihood); backcast = log of the exponentially weighted mean of the first min(75, T) squared residuals; ≤ 2 restarts (`ArchModel.fit`). Point estimates only; daily filtering with `am.fix(params)`.
		23	+
		24	+## 6. Forecasting
		25	+h = 1: analytic, $\hat\sigma^2_{t+1|t} = \exp(\omega + \alpha(|e_t| - \sqrt{2/\pi}) + \gamma e_t + \beta\ln\sigma_t^2)$. **h > 1: simulation** (`SPECS["egarch11"]["analytic"] = False`; arch has no analytic EGARCH multi-step): 2,000 paths, $\ln\sigma^2_{t+k} = \omega + \alpha(|z_{t+k-1}| - \sqrt{2/\pi}) + \gamma z_{t+k-1} + \beta\ln\sigma^2_{t+k-1}$ with simulated standardised shocks, $\hat\sigma^2_{t+k|t}$ = path mean of $\exp(\ln\sigma^2_{t+k})$. Normal: seeded `default_rng(46 + origin).standard_normal`; t/GED/skew-t: arch's fitted-distribution simulator, **unseeded** (`_discrepancies.md`). Cumulative = sum over k ≤ h. Method: `simulation` (h > 1).
		26	+
		27	+## 7. Implementation
		28	+- `src/wp46/models/arch_models.py` (`SPECS["egarch11"]`), arch 8.0.0 (`sheppard2025arch`).
		29	+- Cross-check (`results/metrics/rugarch_crosscheck.json`, rugarch `eGARCH`, t): −3376.6704 (ours) vs −3376.6833 (rugarch), **difference +0.0129**; coefficients agree after the α/γ label swap (β 0.96868 vs 0.96871, ν 6.98 vs 6.99); ω differs (−0.0110 vs −0.0171) because rugarch uses the distribution-specific $E|z|$ in the centring (observational equivalence through ω).
		30	+- Tests: `tests/replication/test_rugarch.py` (fit recorded; no assertion on EGARCH beyond the JSON); pilot `egarch11__t` 1,032/1,032 forecasts `ok` on CL, 207/207 refits `ok`.
		31	+
		32	+## 8. Known limitations / failure modes
		33	+β frequently close to 1 (log-variance near unit root) → explosive multi-step simulations are bounded only by `LNSIGMA_MAX`/variance bounds; multi-step forecasts carry Monte-Carlo error and, for non-normal laws, are unseeded; $E[\sigma^2_{t+k}]$ is a mean of log-normal-like paths and is sensitive to tail draws (large variance of the path mean at h = 22); the centring constant mismatch for non-normal laws is harmless for the fit but makes `omega` non-comparable across distributions.
		34	+
		35	+## 9. Computational footprint
		36	+Estimation cheap (pilot median fit time per refit, t: 0.0088 s CL / 0.0095 s EURUSD); forecasting by simulation (22 × 2,000 draws per origin) is the dominant cost.
		37	+
		38	+## 10. Literature expectations
		39	+`hansen2005forecast`: EGARCH-type leverage models among those that beat GARCH(1,1) on IBM; `brownlees2011practical` rank EGARCH/asymmetric models best for US equities; `nelson1991conditional` originally with GED innovations. Expectation: strong at h = 1 on equities (H1), weaker at h = 22 where the simulation mean of exponentials inflates forecasts; no gain expected on FX (`hansen2005forecast`).

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		1	+# Model card — `figarch11` (FIGARCH(1,d,1))
		2	+
		3	+## 1. Identity
		4	+- **model_id:** `figarch11` · **family:** fractional · **tier:** A · **status:** implemented · **impl:** arch 8.0.0 (`FIGARCH(p=1, q=1, truncation=1000)`)
		5	+- **Label:** FIGARCH(1,d,1) · **distributions:** normal, t, ged, skewt · registry note: "arch truncates the ARCH(∞) representation at 1000 lags"
		6	+- **Seminal citation:** `baillie1996fractionally`
		7	+- **Other references:** `conrad2006inequality` (necessary and sufficient positivity), `ding1996modeling` (long memory in volatility), `bollerslev1996modeling` (FIEGARCH), `davidson2004moment` (HYGARCH); `chung1999estimating` is **unverified and not in the .bib** (literature matrix).
		8	+
		9	+## 2. Definition (arch parameterisation)
		10	+Model: $[1 - \beta L]\,\sigma_t^2 = \omega + [1 - \beta L - (1 - \phi L)(1-L)^d]\,\varepsilon_t^2$, estimated through the truncated ARCH(∞) representation
		11	+$$\sigma_t^2 = \frac{\omega}{1-\beta} + \sum_{i=1}^{1000}\lambda_i\,\varepsilon_{t-i}^2,\qquad \lambda_1 = \phi - \beta + d,\ \ \delta_1 = d,\ \ \delta_j = \frac{j-1-d}{j}\,\delta_{j-1},\ \ \lambda_j = \beta\lambda_{j-1} + \delta_j - \phi\,\delta_{j-1} .$$
		12	+Lags beyond the available sample use the backcast (`figarch_recursion`: $\sigma_t^2 = \tilde\omega + (\sum_{i > t}\lambda_i)\,\text{backcast} + \sum_{i \le \min(t,1000)}\lambda_i\varepsilon_{t-i}^2$). Innovations: normal, t, GED, Hansen skew-t.
		13	+
		14	+## 3. Parameter space and admissibility
		15	+- Parameters: `mu, omega, phi, d, beta` (+ distribution).
		16	+- arch bounds: $\omega \in [0, 10v]$, $\phi \in [0, 0.5]$, $d \in [0, 1-\epsilon]$, $\beta \in [0, 1-\epsilon]$; linear constraints $\omega > 0$, $0 \le d \le 1$, $0 \le \phi \le (1-d)/2$, $0 \le \beta \le d + \phi$ (sufficient positivity conditions of the (1,d,1) case as coded in arch; the necessary and sufficient set of `conrad2006inequality` is larger).
		17	+- Admissibility flag (`ArchModel.fit`): $0 \le d \le 1$. FIGARCH is strictly but not covariance stationary for d > 0 (infinite unconditional variance), so no $\alpha+\beta<1$-type flag applies.
		18	+
		19	+## 4. Nesting and relations
		20	+d = 0 gives GARCH(1,1) with $\alpha = \phi - \beta$ (…) and d = 1 the IGARCH-type unit root; `igarch11` and `garch11` are the end points. `rm2006` and HAR are non-estimated / regression analogues of hyperbolic memory. FIEGARCH/FIAPARCH/HYGARCH (unsupported) are its extensions.
		21	+
		22	+## 5. Estimation
		23	+arch `ConstantMean` + `FIGARCH(1,1)` + distribution; SLSQP (`maxiter` 1000) with the bounds/constraints of §3; starting values from arch's grid d ∈ {0.2, 0.5, 0.7} × φ-ratio ∈ {0.2, 0.5, 0.8} × β-ratio ∈ {0.1, 0.5, 0.9}, $\omega = (1-\beta)\,\overline{\varepsilon^2}\,(1 - \sum_i\lambda_i)$, best Gaussian log-likelihood; backcast = exponentially weighted mean of the first 75 squared residuals; ≤ 2 restarts (`ArchModel.fit`). Point estimates only. Each likelihood evaluation convolves 1,000 weights over the 1,000-day window.
		24	+
		25	+## 6. Forecasting
		26	+arch `FIGARCH._analytic_forecast`: the ARCH(∞) recursion is iterated forward with future $\varepsilon^2$ replaced by their forecasts, $\hat\sigma^2_{t+k|t} = \tilde\omega + \sum_{i=1}^{1000}\lambda_i\,\widetilde{\varepsilon^2}_{t+k-i}$ ($\widetilde{\varepsilon^2}_s = \varepsilon_s^2$ for $s \le t$, $= \hat\sigma^2_{s|t}$ for $s > t$; pre-sample lags = backcast). Exact under $E z^2 = 1$. Cumulative = sum over k ≤ h. Method: `analytic`.
		27	+
		28	+## 7. Implementation
		29	+- `src/wp46/models/arch_models.py` (`SPECS["figarch11"]`), arch 8.0.0 (`sheppard2025arch`).
		30	+- Cross-check (`results/metrics/rugarch_crosscheck.json`, rugarch `fiGARCH`, t): −3383.4506 (ours) vs −3381.9798 (rugarch), **difference −1.4708** (ours lower) — the only cross-check outside ±0.3; attributed to different truncation / constraint handling between arch and rugarch (D-14, `progress.md` open issue). Estimates: d = 0.546, φ = 0.227, β = 0.693, ν = 6.45 (arch). Reported as an implementation caveat; not resolved.
		31	+- Tests: `tests/replication/test_rugarch.py` (fit recorded, no assertion on FIGARCH). No simulation-recovery test.
		32	+
		33	+## 8. Known limitations / failure modes
		34	+ω is small and poorly identified (literature matrix); d near 0 or 1 hits the bounds; the sufficient constraints exclude admissible parameter sets; the 1,000-lag truncation inside a 1,000-day window means every in-sample variance uses the backcast for the oldest lags; the 1.47 log-likelihood discrepancy with rugarch is unexplained; infinite unconditional variance makes long-horizon forecasts grow slowly without bound.
		35	+
		36	+## 9. Computational footprint
		37	+Analytic but O(T × 1000) per likelihood evaluation (the heaviest analytic model of Tier A); forecasts are O(22 × 1000). Not in the pilot.
		38	+
		39	+## 10. Literature expectations
		40	+`baillie1996fractionally` and `ding1996modeling` document hyperbolic decay in volatility autocorrelations; `hansen2005forecast` include FIGARCH among models that do not beat GARCH(1,1) at the 1-day horizon; `lamoureux1990persistence`/`hillebrand2005neglecting` warn that breaks mimic long memory. Pre-registered H4 (persistence structure) predicts gains, if any, at h = 22 rather than h = 1.

added model_cards/garchm11.md +39 −0

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		1	+# Model card — `garchm11` (GARCH-in-mean(1,1))
		2	+
		3	+## 1. Identity
		4	+- **model_id:** `garchm11` · **family:** classical · **tier:** A · **status:** implemented · **impl:** custom (`src/wp46/models/custom.py::GARCHM11`)
		5	+- **Label:** GARCH-M(1,1) · **distributions:** normal, t, ged, skewt
		6	+- **Seminal citation:** `engle1987estimating` (Engle, Lilien & Robins 1987, ARCH-M)
		7	+- **Other references:** `bollerslev1986generalized`; literature matrix §a.1 ("mean and variance parameters not separable → affects multi-step forecasts of cumulative returns").
		8	+
		9	+## 2. Definition
		10	+$$r_t = \mu + \delta\,\sigma_t^2 + \varepsilon_t,\quad \varepsilon_t = \sigma_t z_t,\qquad \sigma_t^2 = \omega + \alpha\,\varepsilon_{t-1}^2 + \beta\,\sigma_{t-1}^2 ,$$
		11	+with the risk-premium function $g(\sigma_t^2) = \sigma_t^2$ (variance in the mean, not $\sigma_t$ or $\log\sigma_t^2$). Recursion `rec_garchm`: $\sigma_0^2 = \omega + (\alpha+\beta)s_0$, $\varepsilon_t = r_t - \mu - \delta\sigma_t^2$ computed jointly with $\sigma_t^2$. Innovations: normal, t, GED, Hansen skew-t.
		12	+
		13	+## 3. Parameter space and admissibility
		14	+- Parameters: `mu, delta, omega, alpha, beta` (+ distribution). `delta` is unconstrained.
		15	+- Reparameterisation: $\omega = e^{\theta}$, persistence $s = \alpha + \beta = \sigma(\theta)$, share $\alpha/s = \sigma(\theta')$.
		16	+- Admissibility flag: $\omega > 0$, $\alpha, \beta \ge 0$, $\alpha + \beta < 1$ (variance equation as GARCH(1,1)).
		17	+
		18	+## 4. Nesting and relations
		19	+Nests `garch11` ($\delta = 0$). Only registry model whose conditional mean depends on the variance; its variance recursion is GARCH(1,1) but the residuals differ ($\varepsilon_t$ depends on δ).
		20	+
		21	+## 5. Estimation
		22	+Custom MLE with a model-specific objective (`GARCHM11._negll`): $-\sum_t[\log f_z(\varepsilon_t/\sigma_t) - \tfrac12\log\sigma_t^2]$ where $(\sigma_t^2, \varepsilon_t)$ come from `rec_garchm`. L-BFGS-B (maxiter 2000) from three starts (δ, ω, α, β) ∈ {(0, 0.05v, 0.05, 0.90), (0.02, 0.02v, 0.10, 0.88), (−0.02, 0.2v, 0.15, 0.65)}, Nelder–Mead polish on failure, μ start = sample mean, backcast $s_0$ from the demeaned returns (computed once with μ = sample mean, i.e. ignoring the in-mean term, and reused for filtering/forecasting). Point estimates only; no standard errors.
		23	+
		24	+## 6. Forecasting
		25	+`GARCHM11.forecast` recomputes $(\sigma_t^2, \varepsilon_t)$ with `rec_garchm`, then $\hat\sigma^2_{t+1|t} = \omega + \alpha\varepsilon_t^2 + \beta\sigma_t^2$ and $\hat\sigma^2_{t+k|t} = \omega + (\alpha+\beta)\hat\sigma^2_{t+k-1|t}$ (identical to GARCH(1,1)); mean forecast $\hat\mu_{t+1} = \mu + \delta\hat\sigma^2_{t+1|t}$ (used for VaR). Cumulative $h$-day variance = sum of per-step variances (the variance of the h-day return induced by the time-varying mean $\delta\sigma^2_{t+k}$ is ignored, as for all models). Method: `analytic`.
		26	+
		27	+## 7. Implementation
		28	+- `src/wp46/models/custom.py::GARCHM11` (numba `rec_garchm`).
		29	+- Cross-check: **none** — `garchm11` is not in `results/metrics/rugarch_crosscheck.json` (rugarch `archm` was not included in `tests/replication/rugarch_crosscheck.R`).
		30	+- Tests: **no unit, simulation or integration test references `GARCHM11`** (it is absent from `tests/unit/test_custom_models.py` imports and parametrisations). This is a gap relative to CLAUDE.md rule 6 — recorded in `_discrepancies.md`.
		31	+
		32	+## 8. Known limitations / failure modes
		33	+δ is weakly identified on daily data (risk-premium effects are second order; literature matrix: "low priority in a variance-forecast benchmark"); the joint mean/variance likelihood can be flat in δ and produce spurious large |δ| with near-integrated variance. The backcast ignores δ (minor). Positivity of $\sigma_t^2$ is guaranteed by the reparameterisation.
		34	+
		35	+## 9. Computational footprint
		36	+Analytic; 5–7 parameters; the recursion must recompute residuals at each likelihood evaluation (same cost order as `garch11` custom). Not in the pilot.
		37	+
		38	+## 10. Literature expectations
		39	+`engle1987estimating` motivate ARCH-M for term premia, not for variance forecasting; the literature matrix states the mean effect on h-day variance is second order. Expectation: variance forecasts essentially equal to GARCH(1,1); any difference comes from estimation noise in δ (H5: more failures, no accuracy gain).

added model_cards/gjr11.md +42 −0

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		1	+# Model card — `gjr11` (GJR-GARCH(1,1))
		2	+
		3	+## 1. Identity
		4	+- **model_id:** `gjr11` · **family:** asymmetric · **tier:** A · **status:** implemented · **impl:** arch 8.0.0 (`GARCH(p=1, o=1, q=1)`); a custom twin `gjr11_custom` (`src/wp46/models/custom.py::GJR11`) exists for cross-checks and simulation tests only.
		5	+- **Label:** GJR-GARCH(1,1) · **distributions:** normal, t, ged, skewt
		6	+- **Seminal citation:** `glosten1993relation`
		7	+- **Other references:** `engle1993measuring` (news-impact curve, sign-bias tests), `zakoian1994threshold` (TGARCH — not the same model, see `tarch11`), `ling2002stationarity` (moments of asymmetric GARCH), `hansen2005forecast`, `brownlees2011practical`, `bollerslev2010glossary` (naming: EViews "TARCH" = GJR).
		8	+
		9	+## 2. Definition (arch parameterisation)
		10	+$$r_t = \mu + \varepsilon_t,\ \varepsilon_t = \sigma_t z_t,\qquad \sigma_t^2 = \omega + \alpha\,\varepsilon_{t-1}^2 + \gamma\,\varepsilon_{t-1}^2\,\mathbb 1\{\varepsilon_{t-1} < 0\} + \beta\,\sigma_{t-1}^2 .$$
		11	+arch `garch_recursion` with power 2: pre-sample terms use the backcast for $\varepsilon^2$ and $\sigma^2$ and $0.5\times$ backcast for the asymmetric term. Innovations: normal, t, GED, Hansen skew-t.
		12	+
		13	+## 3. Parameter space and admissibility
		14	+- Parameters: `mu, omega, alpha[1], gamma[1], beta[1]` (+ distribution).
		15	+- arch bounds: $\omega \in [10^{-8}v, 10v]$, $\alpha \in [0,1]$, $\gamma \in [-1, 2]$, $\beta \in [0,1]$; linear constraints $\omega > 0$, $\alpha \ge 0$, $\alpha + \gamma \ge 0$, $\beta \ge 0$, $\alpha + \tfrac12\gamma + \beta < 1$ (arch uses the symmetric weight ½ in the optimizer constraint for every distribution).
		16	+- Admissibility flag (`ArchModel.fit`): $\alpha + \beta + 0.5\gamma < 1$ — the covariance-stationarity condition under a **symmetric** innovation law. For the skew-t the exact condition is $\alpha + \beta + \kappa\gamma < 1$ with $\kappa = E[z^2\mathbb 1\{z<0\}] \neq 0.5$ (`distributions.neg_half_moment`); it is used by the custom twin (`GJR11.admissible`) but **not** by the registry implementation (recorded in `_discrepancies.md`).
		17	+- Custom twin reparameterisation: $\omega = e^{\theta}$, persistence $s = \sigma(\theta)$, softmax shares of $s$ among $(\alpha, \kappa\gamma, \beta)$.
		18	+
		19	+## 4. Nesting and relations
		20	+Nests `garch11` (γ = 0). Special case of `aparch11` with δ = 2 and $\gamma_{APARCH}$ mapped to the GJR indicator form only approximately (APARCH uses $(|\varepsilon| - \gamma\varepsilon)^\delta$, which for δ = 2 is $(1\mp\gamma)^2\varepsilon^2$: GJR corresponds to $\alpha_{GJR} = \alpha(1-\gamma)^2$, $\alpha_{GJR}+\gamma_{GJR} = \alpha(1+\gamma)^2$). Not the same as `tarch11` (threshold on σ, power 1). Same news-impact asymmetry as `nagarch11`/`qgarch11` but with a kink rather than a shift.
		21	+
		22	+## 5. Estimation
		23	+arch `ConstantMean` + `GARCH(1,1,1)` + distribution; SLSQP (`fit(disp="off", options={"maxiter": 1000})`) with the bounds/constraints of §3; starting values from arch's grid (α ∈ {0.01, 0.05, 0.1, 0.2}, γ same grid, persistence ∈ {0.5, 0.7, 0.9, 0.98}; best Gaussian log-likelihood); backcast = exponentially weighted mean of the first min(75, T) squared residuals (0.94ⁱ); ≤ 2 restarts (previous estimate × (0.9 + 0.2(i+1)/3), maxiter 2000) when `convergence_flag ≠ 0`. Only point estimates are used in the rolling benchmark; daily filtering with `am.fix(params)`.
		24	+
		25	+## 6. Forecasting
		26	+arch `GARCH._analytic_forecast` (power 2): $\hat\sigma^2_{t+1|t} = \omega + \alpha\varepsilon_t^2 + \gamma\varepsilon_t^2\mathbb 1\{\varepsilon_t<0\} + \beta\sigma_t^2$ and, for $k \ge 2$,
		27	+$$\hat\sigma^2_{t+k|t} = \omega + (\alpha + 0.5\,\gamma + \beta)\,\hat\sigma^2_{t+k-1|t},$$
		28	+because arch sets the future asymmetric term to $0.5\times$ the variance forecast (`_asym_resids[h+m] = sqrt(0.5 * forecast)`). This is exact for symmetric $z$ (normal, t, GED) and **approximate for the skew-t** (exact weight $\kappa = E[z^2\mathbb 1\{z<0\}]$, > 0.5 for λ < 0). The custom twin implements $\hat\sigma^2_{t+k|t} = \omega + (\alpha + \kappa\gamma + \beta)\hat\sigma^2_{t+k-1|t}$ with κ from `neg_half_moment` (D-09 describes this κ-version; the scored `gjr11` is the arch version). Cumulative = sum over k ≤ h. Method: `analytic`.
		29	+
		30	+## 7. Implementation
		31	+- `src/wp46/models/arch_models.py` (`SPECS["gjr11"]`), arch 8.0.0 (`sheppard2025arch`); custom twin `custom.py::GJR11` (`model_id = "gjr11_custom"`, not in the registry).
		32	+- Cross-check (`results/metrics/rugarch_crosscheck.json`, rugarch `gjrGARCH`, t): −3372.9536 (ours, arch) vs −3372.9905 (rugarch), **difference +0.0369**; parameters agree to 4 decimals (α 0.04267/0.04264, γ 0.06203/0.06202, β 0.90036/0.90040, ν 7.002/7.010).
		33	+- Tests: `tests/replication/test_rugarch.py` (arch GJR vs rugarch, assert ours ≥ rugarch − 1); custom twin: `tests/unit/test_custom_models.py::test_nesting_and_admissibility[GJR11]`, `test_no_lookahead_in_forecasts`, `test_edge_cases_constant_and_extreme` (spike of 60 % at t = 700), `tests/simulation/test_recovery.py::test_gjr_recovery_median_over_replications` (12 seeded replications, T = 4,000, t(6) innovations; median within 0.02–0.03 of (0.04, 0.03, 0.10, 0.88)); driver: `tests/integration/test_rolling_driver.py::test_driver_tier_b_and_failure_flags` (`gjr11`, t). The fixture of the rugarch cross-check is itself GJR-t data.
		34	+
		35	+## 8. Known limitations / failure modes
		36	+α often converges to the zero bound on equities (all news impact loaded on γ) — a boundary optimum flagged by SLSQP; near-integrated $\alpha + \gamma/2 + \beta$; the symmetric-κ forecast/admissibility rule under skew-t (above); the optimizer bound $\gamma \le 2$. Positivity requires $\alpha + \gamma \ge 0$ (imposed).
		37	+
		38	+## 9. Computational footprint
		39	+Analytic; 5–7 parameters. Pilot median fit time per refit (t innovations): 0.0081 s (CL), 0.0101 s (EURUSD).
		40	+
		41	+## 10. Literature expectations
		42	+`hansen2005forecast`: on IBM, models with a leverage effect (GJR among them) significantly outperform GARCH(1,1); on DM/$ they do not. `brownlees2011practical`, `awartani2005predicting`: asymmetric GARCH best overall for equity indices, especially in stressed periods. Pre-registered H1 (asymmetry) and H6 (regimes) concern this model directly; the matrix notes the GJR/TGARCH naming confusion that the registry resolves by keeping both definitions.

added model_cards/igarch11.md +39 −0

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		1	+# Model card — `igarch11` (Integrated GARCH(1,1))
		2	+
		3	+## 1. Identity
		4	+- **model_id:** `igarch11` · **family:** classical · **tier:** A · **status:** implemented · **impl:** custom (`src/wp46/models/custom.py::IGARCH11`)
		5	+- **Label:** IGARCH(1,1) · **distributions:** normal, t, ged, skewt
		6	+- **Seminal citation:** `engle1986modelling` (Engle & Bollerslev 1986)
		7	+- **Other references:** `nelson1990stationarity` (strict stationarity of IGARCH), `lumsdaine1996consistency`, `lee1994asymptotic`, `jensen2004asymptotic` (QMLE under unit persistence); `lamoureux1990persistence`, `mikosch2004nonstationarities`, `hillebrand2005neglecting` (spurious IGARCH from breaks); `jpmorgan1996riskmetrics` (EWMA, nested).
		8	+
		9	+## 2. Definition
		10	+$$r_t = \mu + \varepsilon_t,\ \varepsilon_t = \sigma_t z_t,\qquad \sigma_t^2 = \omega + \alpha\,\varepsilon_{t-1}^2 + (1-\alpha)\,\sigma_{t-1}^2 ,$$
		11	+i.e. GARCH(1,1) with $\beta = 1 - \alpha$ imposed (`rec_garch(eps, omega, alpha, 1 - alpha, s0)`), $\sigma_0^2 = \omega + s_0$ ($s_0$ = backcast, since $\alpha + \beta = 1$). Innovations: normal, t(ν), GED(ν), Hansen skew-t(η, λ) (standardised, `distributions.py`).
		12	+
		13	+## 3. Parameter space and admissibility
		14	+- Parameters: `mu, omega, alpha` (+ `nu` | `eta, lambda`).
		15	+- Reparameterisation (unconstrained θ): $\omega = e^{\theta_0}$, $\alpha = \sigma(\theta_1) \in (0,1)$ (logistic); t: $\nu = 2.05 + e^{\theta}$; GED: $\nu = 0.3 + e^{\theta}$; skew-t: $\eta = 2.05 + e^{\theta}$, $\lambda = 0.99\tanh(\theta')$.
		16	+- Admissibility flag: $\omega > 0$ and $0 < \alpha < 1$. No covariance stationarity (unconditional variance does not exist); strictly stationary for $\alpha < 1$ (`nelson1990stationarity`).
		17	+
		18	+## 4. Nesting and relations
		19	+Boundary case of `garch11` ($\alpha + \beta = 1$); nests `ewma094` ($\omega = 0$, $\alpha = 0.06$ — registry `ewma094: nested_in: igarch11`). Variance forecasts grow linearly in the horizon (unit root in $\varepsilon_t^2$). Related to `figarch11` with d = 1.
		20	+
		21	+## 5. Estimation
		22	+Custom MLE (`CustomModel.fit`): objective $-\sum_t[\log f_z(z_t) - \tfrac12\log\sigma_t^2]$ with numba recursion; L-BFGS-B (maxiter 2000, ftol 1e-10, gtol 1e-6) on θ from three starting vectors (ω, α) ∈ {(0.01 v, 0.06), (0.003 v, 0.03), (0.03 v, 0.12)} with $v = \operatorname{var}(r)$ (at least two starts are tried; the third only if the second fails), Nelder–Mead polish (maxiter 4000) if L-BFGS-B does not report success; μ starts at the sample mean; distribution starts ν = 6 / 1.5 / (6, −0.05). Initial variance: arch-style backcast of the demeaned returns ($\sum_{i<75} w_i (r_i - \bar r)^2$, $w_i \propto 0.94^i$), stored in `aux["backcast"]`. `converged` = optimizer success with finite objective; inadmissible optima kept and flagged. No standard errors; point estimates only.
		23	+
		24	+## 6. Forecasting
		25	+`IGARCH11.step_forecast`: $\hat\sigma^2_{t+1|t} = \omega + \alpha\varepsilon_t^2 + (1-\alpha)\sigma_t^2$, $\hat\sigma^2_{t+k|t} = \omega + \hat\sigma^2_{t+k-1|t}$ for $k = 2..22$ (linear growth by ω per day). Cumulative $h$-day variance $= h\hat\sigma^2_{t+1|t} + \omega h(h-1)/2$. Method: `analytic`.
		26	+
		27	+## 7. Implementation
		28	+- `src/wp46/models/custom.py::IGARCH11` (custom, numba `rec_garch`), standardised densities from `src/wp46/models/distributions.py` (equal to arch's to 1e-12, `tests/unit/test_distributions.py`).
		29	+- Cross-check (`results/metrics/rugarch_crosscheck.json`, rugarch `iGARCH`, Student-t, shared fixture): log-likelihood −3385.4670 (ours) vs −3385.5171 (rugarch), **difference +0.0502**.
		30	+- Tests: `tests/replication/test_rugarch.py` (asserts ours ≥ rugarch − 1.0); `tests/unit/test_custom_models.py::test_nesting_and_admissibility[IGARCH11]` (convergence, admissibility, positive finite 22-step forecasts; excluded from the "nests GARCH(1,1)" log-likelihood assertion). No simulation-recovery test.
		31	+
		32	+## 8. Known limitations / failure modes
		33	+Imposes unit persistence even when the data are stationary: on the GJR-t fixture the IGARCH likelihood is 6.0 units below GARCH(1,1) (−3385.47 vs −3379.46). Forecast variance grows without bound with h (h = 22 cumulative variance inflated by $\omega \cdot 231$). $\omega$ near zero makes the model indistinguishable from EWMA. Linear growth makes long-horizon forecasts sensitive to $\hat\omega$.
		34	+
		35	+## 9. Computational footprint
		36	+Analytic recursion and forecasts; 3–5 parameters; L-BFGS-B with numerical gradients. Not in the pilot; cost comparable to `garch11` custom estimation (a few milliseconds per refit on a 1,000-day window).
		37	+
		38	+## 10. Literature expectations
		39	+Near-integrated GARCH estimates are common in long daily samples (`engle1986modelling`, `lamoureux1990persistence`); `hansen2005forecast` include IGARCH among the models that do not beat GARCH(1,1). Expectation: close to GARCH(1,1) at h = 1 for highly persistent instruments, worse at h = 22 because of the absent mean reversion (literature matrix §a.1).

added model_cards/nagarch11.md +41 −0

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		1	+# Model card — `nagarch11` (NAGARCH(1,1), Engle–Ng nonlinear asymmetric GARCH)
		2	+
		3	+## 1. Identity
		4	+- **model_id:** `nagarch11` · **family:** asymmetric · **tier:** A · **status:** implemented · **impl:** custom (`src/wp46/models/custom.py::NAGARCH11`)
		5	+- **Label:** NAGARCH(1,1) (Engle-Ng) · **distributions:** normal, t, ged, skewt
		6	+- **Seminal citation:** `engle1993measuring`
		7	+- **Other references:** `higgins1992class` (NARCH — a *different* "NGARCH", literature-matrix caveat #3), `glosten1993relation`, `sentana1995quadratic` (QGARCH, the other shifted/linear-asymmetry form), `brownlees2011practical` (NGARCH in their comparison).
		8	+
		9	+## 2. Definition
		10	+$$r_t = \mu + \varepsilon_t,\ \varepsilon_t = \sigma_t z_t,\qquad \sigma_t^2 = \omega + \alpha\big(\varepsilon_{t-1} - \theta\,\sigma_{t-1}\big)^2 + \beta\,\sigma_{t-1}^2 ,$$
		11	+`rec_nagarch` with $\sigma_0^2 = \omega + [\alpha(1+\theta^2) + \beta]\,s_0$ ($s_0$ = backcast). Equivalent to rugarch's fGARCH/NAGARCH $\sigma_t^2 = \omega + \alpha\sigma_{t-1}^2(z_{t-1} - \eta)^2 + \beta\sigma_{t-1}^2$ with $\eta = \theta$. Innovations: normal, t, GED, Hansen skew-t.
		12	+
		13	+## 3. Parameter space and admissibility
		14	+- Parameters: `mu, omega, alpha, theta, beta` (+ distribution).
		15	+- Reparameterisation: $\omega = e^{\theta_0}$; persistence $s = \alpha(1+\theta^2) + \beta = \sigma(\theta_1) \in (0,1)$; share $w = \alpha(1+\theta^2)/s = \sigma(\theta_2)$; $\theta$ unconstrained. Hence $\alpha = s w/(1+\theta^2)$, $\beta = s(1-w)$.
		16	+- Admissibility flag: $\omega > 0$, $\alpha, \beta \ge 0$, $\alpha(1+\theta^2) + \beta < 1$ (covariance stationarity under $E z = 0$, $E z^2 = 1$ — holds for all four laws, including Hansen's skew-t which is standardised to zero mean and unit variance).
		17	+
		18	+## 4. Nesting and relations
		19	+Nests `garch11` (θ = 0). News-impact curve = GARCH parabola shifted to the right by $\theta\sigma_{t-1}$ (asymmetry via a shift, not a kink as in `gjr11`); QGARCH is the linear-term version ($\gamma\varepsilon_{t-1}$ instead of the shift, same quadratic form with $\gamma = -2\alpha\theta\sigma_{t-1}$ varying over time).
		20	+
		21	+## 5. Estimation
		22	+Custom MLE (`CustomModel.fit`): L-BFGS-B on θ (maxiter 2000, ftol 1e-10, gtol 1e-6) from three starts (ω, α, θ, β) ∈ {(0.05v, 0.05, 0.5, 0.88), (0.02v, 0.08, 1.0, 0.85), (0.1v, 0.1, 0.0, 0.8)}, Nelder–Mead polish on failure; μ start = sample mean, ν/η/λ starts 6 / 1.5 / (6, −0.05); backcast $s_0$ = arch-style exponentially weighted mean of the first 75 squared demeaned returns. Point estimates only.
		23	+
		24	+## 6. Forecasting
		25	+`NAGARCH11.step_forecast`: $\hat\sigma^2_{t+1|t} = \omega + \alpha(\varepsilon_t - \theta\sigma_t)^2 + \beta\sigma_t^2$; for $k \ge 2$,
		26	+$$\hat\sigma^2_{t+k|t} = \omega + \big[\alpha(1+\theta^2) + \beta\big]\,\hat\sigma^2_{t+k-1|t},$$
		27	+exact because $E[(\varepsilon - \theta\sigma)^2 \mid \sigma^2] = \sigma^2(1 + \theta^2)$ when $E z = 0$, $E z^2 = 1$. Cumulative = sum over k ≤ h. Method: `analytic`.
		28	+
		29	+## 7. Implementation
		30	+- `src/wp46/models/custom.py::NAGARCH11` (numba `rec_nagarch`).
		31	+- Cross-check (`results/metrics/rugarch_crosscheck.json`, rugarch `fGARCH` submodel `NAGARCH`, t): −3373.3123 (ours) vs −3373.3505 (rugarch), **difference +0.0382**.
		32	+- Tests: `tests/replication/test_rugarch.py` (assert ours ≥ rugarch − 1); `tests/unit/test_custom_models.py::test_nesting_and_admissibility[NAGARCH11]` (log-likelihood ≥ GARCH(1,1) − 0.5, admissible, positive 22-step forecasts), `test_no_lookahead_in_forecasts`; `tests/simulation/test_recovery.py::test_nagarch_recovery` (T = 5,000, normal, θ = 0.8 recovered within 0.3, β within 0.05); pilot `nagarch11__t`: 207/207 refits `ok` on CL.
		33	+
		34	+## 8. Known limitations / failure modes
		35	+θ is weakly identified when α is small (the shift multiplies α); large |θ| with tiny α yields flat likelihoods and slow L-BFGS-B progress (pilot max fit time 0.17 s on CL vs median 0.009 s). Near-integrated persistence as in GARCH. The sigmoid reparameterisation cannot return exactly $s = 1$ (inadmissible optima can only arise from numerical failure).
		36	+
		37	+## 9. Computational footprint
		38	+Analytic; 5–7 parameters. Pilot median fit time per refit (t): 0.0089 s (CL), 0.0218 s (EURUSD).
		39	+
		40	+## 10. Literature expectations
		41	+`engle1993measuring` show the NAGARCH news-impact curve fits Japanese equity data better than GARCH; `brownlees2011practical` include NGARCH among the best performers on US equities; `hansen2005forecast` find leverage specifications superior on IBM only. Expectation: comparable to GJR/EGARCH at h = 1 on equities (H1); exact analytic multi-step forecasts are an advantage over the simulation-based asymmetric models at h = 22 (H5).

added model_cards/qgarch11.md +40 −0

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		1	+# Model card — `qgarch11` (QGARCH(1,1), Sentana's quadratic GARCH)
		2	+
		3	+## 1. Identity
		4	+- **model_id:** `qgarch11` · **family:** asymmetric · **tier:** A · **status:** implemented · **impl:** custom (`src/wp46/models/custom.py::QGARCH11`)
		5	+- **Label:** QGARCH(1,1) (Sentana) · **distributions:** normal, t, ged, skewt
		6	+- **Seminal citation:** `sentana1995quadratic`
		7	+- **Other references:** `engle1993measuring` (NAGARCH, the shifted form), `glosten1993relation`.
		8	+
		9	+## 2. Definition
		10	+$$r_t = \mu + \varepsilon_t,\ \varepsilon_t = \sigma_t z_t,\qquad \sigma_t^2 = \omega + \alpha\,\varepsilon_{t-1}^2 + \gamma\,\varepsilon_{t-1} + \beta\,\sigma_{t-1}^2 ,$$
		11	+`rec_qgarch` with $\sigma_0^2 = \omega + (\alpha+\beta)s_0$ and a floor $\sigma_t^2 \ge 10^{-12}$ inside the recursion. Innovations: normal, t, GED, Hansen skew-t.
		12	+
		13	+## 3. Parameter space and admissibility
		14	+- Parameters: `mu, omega, alpha, gamma, beta` (+ distribution).
		15	+- Positivity: $\omega + \alpha\varepsilon^2 + \gamma\varepsilon > 0$ for all ε ⇔ $\gamma^2 < 4\alpha\omega$ (minimum of the quadratic at $\varepsilon = -\gamma/(2\alpha)$ equals $\omega - \gamma^2/(4\alpha)$).
		16	+- Reparameterisation: $\omega = e^{\theta_0}$, $s = \alpha+\beta = \sigma(\theta_1)$, $\alpha = s\,\sigma(\theta_2)$, $\gamma = 2\sqrt{\alpha\omega}\,\tanh(\theta_3)$ (so the positivity condition holds by construction).
		17	+- Admissibility flag: $\omega > 0$, $\alpha > 0$ (strict), $\beta \ge 0$, $\alpha + \beta < 1$, $\gamma^2 < 4\alpha\omega$.
		18	+
		19	+## 4. Nesting and relations
		20	+Nests `garch11` (γ = 0). Linear-asymmetry counterpart of `nagarch11` (news-impact parabola shifted by $-\gamma/(2\alpha)$, a constant shift in ε rather than one proportional to σ). Sentana's general quadratic ARCH includes cross terms not used here.
		21	+
		22	+## 5. Estimation
		23	+Custom MLE (`CustomModel.fit`): L-BFGS-B on θ from three starts (ω, α, γ, β) ∈ {(0.05v, 0.06, −0.3√(0.05v·0.06), 0.90), (0.03v, 0.10, 0, 0.87), (0.1v, 0.1, −0.5√(0.1v·0.1), 0.8)}, Nelder–Mead polish on failure; μ start = sample mean; backcast $s_0$ arch-style. Point estimates only.
		24	+
		25	+## 6. Forecasting
		26	+`QGARCH11.step_forecast`: $\hat\sigma^2_{t+1|t} = \max(\omega + \alpha\varepsilon_t^2 + \gamma\varepsilon_t + \beta\sigma_t^2,\ 10^{-12})$; for $k \ge 2$, $\hat\sigma^2_{t+k|t} = \omega + (\alpha+\beta)\hat\sigma^2_{t+k-1|t}$ (uses $E[\varepsilon_{t+j}\mid\mathcal F_t] = 0$, exact for all four zero-mean laws). Cumulative = sum over k ≤ h. Method: `analytic`.
		27	+
		28	+## 7. Implementation
		29	+- `src/wp46/models/custom.py::QGARCH11` (numba `rec_qgarch`).
		30	+- Cross-check: **none** — QGARCH is in neither arch 8.0.0 nor rugarch 1.5-6; not in `results/metrics/rugarch_crosscheck.json`. The registry marks it `custom (validated vs arch/rugarch)` by the generic `impl` comment, but no library reference exists for this architecture (recorded in `_discrepancies.md`; it is the only scored custom architecture without any library counterpart besides `gas_t11` and `garchx_rv11`).
		31	+- Tests: `tests/unit/test_custom_models.py::test_nesting_and_admissibility[QGARCH11]` (converged, admissible, log-likelihood ≥ GARCH(1,1) − 0.5, positive finite 22-step forecasts). No simulation-recovery test, no rugarch test.
		32	+
		33	+## 8. Known limitations / failure modes
		34	+The positivity bound $\gamma^2 < 4\alpha\omega$ is restrictive when ω is small (highly persistent series): γ is then forced toward zero and the model collapses to GARCH(1,1), so the asymmetry cannot be large on persistent equity series. Admissibility requires α > 0 strictly (α → 0 makes γ → 0 by construction). Floors at 1e-12 silently mask any numerical negativity.
		35	+
		36	+## 9. Computational footprint
		37	+Analytic; 5–7 parameters; same cost order as `nagarch11`. Not in the pilot.
		38	+
		39	+## 10. Literature expectations
		40	+`sentana1995quadratic` shows the quadratic form captures asymmetry with a linear term; the literature matrix lists QGARCH as "custom (if time permits)" and notes the positivity restriction. `hansen2005forecast` do not include QGARCH. Expectation: between GARCH(1,1) and NAGARCH on equities; the positivity constraint is the main practical limitation (H5: more boundary optima).

added model_cards/tarch11.md +39 −0

@@ -0,0 +1,39 @@
		1	+# Model card — `tarch11` (TGARCH(1,1), Zakoïan threshold on the conditional standard deviation)
		2	+
		3	+## 1. Identity
		4	+- **model_id:** `tarch11` · **family:** asymmetric · **tier:** A · **status:** implemented · **impl:** arch 8.0.0 (`GARCH(p=1, o=1, q=1, power=1.0)`, arch name "TARCH/ZARCH")
		5	+- **Label:** TGARCH(1,1) (Zakoian, threshold on sigma) · **distributions:** normal, t, ged, skewt
		6	+- **Seminal citation:** `zakoian1994threshold`
		7	+- **Other references:** `glosten1993relation` (GJR, the **different** variance-threshold model), `ding1993long` (APARCH nests it with δ = 1), `taylor2008modelling`/`schwert1989why` (absolute-value dynamics), `bollerslev2010glossary` (definition caveat #1 of the literature matrix: "TGARCH ≠ GJR").
		8	+
		9	+## 2. Definition (arch parameterisation, power = 1)
		10	+$$r_t = \mu + \varepsilon_t,\ \varepsilon_t = \sigma_t z_t,\qquad \sigma_t = \omega + \alpha\,|\varepsilon_{t-1}| + \gamma\,|\varepsilon_{t-1}|\,\mathbb 1\{\varepsilon_{t-1} < 0\} + \beta\,\sigma_{t-1} .$$
		11	+Mapping to Zakoïan's $\sigma_t = \omega + \alpha^+\varepsilon_{t-1}^+ - \alpha^-\varepsilon_{t-1}^- + \beta\sigma_{t-1}$ ($\varepsilon^+ = \max(\varepsilon,0)$, $\varepsilon^- = \min(\varepsilon,0)$): $\alpha^+ = \alpha$, $\alpha^- = \alpha + \gamma$. arch's recursion runs on $\sigma_t^{1}$ (`fresids = |ε|`), then squares; pre-sample terms use the power-1 backcast (weighted mean of $|\varepsilon|$) and $0.5\times$ it for the asymmetric term. Innovations: normal, t, GED, Hansen skew-t.
		12	+
		13	+## 3. Parameter space and admissibility
		14	+- Parameters: `mu, omega, alpha[1], gamma[1], beta[1]` (+ distribution).
		15	+- arch bounds: $\omega \in [10^{-8}v_1, 10v_1]$ with $v_1 = \overline{|\varepsilon|}$, $\alpha \in [0,1]$, $\gamma \in [-1, 2]$, $\beta \in [0,1]$; constraints $\omega > 0$, $\alpha \ge 0$, $\alpha+\gamma \ge 0$ (so $\alpha^- \ge 0$), $\beta \ge 0$, $\alpha + \tfrac12\gamma + \beta < 1$. The last constraint is arch's generic GARCH constraint applied to power-1 coefficients; it is **not** the second-order stationarity condition of the TGARCH (which involves $E[(\alpha|z| + \gamma|z|\mathbb 1\{z<0\} + \beta)^2] < 1$, see `he1999properties`/`ling2002necessary` for the APARCH family).
		16	+- Admissibility flag: **always `True`** in `ArchModel.fit` (no model-specific check for `tarch11`; only the optimizer constraints apply). Recorded in `_discrepancies.md`.
		17	+
		18	+## 4. Nesting and relations
		19	+APARCH with δ = 1 and $\gamma_{APARCH}$ mapped via $(1-\gamma_A)\alpha_A = \alpha$, $(1+\gamma_A)\alpha_A = \alpha + \gamma$. Nests `avgarch11` (γ = 0). Not nested in and not nesting `gjr11`/`garch11` (different power). Same sign asymmetry as GJR but on σ, which makes the variance response to a shock quadratic in $|\varepsilon|$ with a kink at zero.
		20	+
		21	+## 5. Estimation
		22	+arch `ConstantMean` + `GARCH(1,1,1,power=1.0)` + distribution, SLSQP (`maxiter` 1000), grid starting values (same grid as GJR, target scaled to the power-1 moment), power-1 backcast, ≤ 2 restarts (`ArchModel.fit`). Point estimates only.
		23	+
		24	+## 6. Forecasting
		25	+h = 1: analytic ($\hat\sigma_{t+1|t}$ from the recursion, squared). **h > 1: simulation** (`SPECS["tarch11"]["analytic"] = False`; arch raises for analytic multi-step when power ≠ 2): 2,000 paths of $\sigma_{t+k}$ via `GARCH._simulate_paths` with power 1 (shock $= z\,\sigma$, $|{\rm shock}|$ and its negative part feed the next step), forecast $\hat\sigma^2_{t+k|t}$ = mean over paths of $\sigma_{t+k}^2$. Shocks: for the normal, `np.random.default_rng(seed).standard_normal` with seed = 46 + origin index (reproducible); **for t/GED/skew-t, `rng=None` → arch draws from the fitted distribution with an unseeded generator** (`Distribution(seed=None)` → `default_rng()`), so multi-step forecasts are not bit-reproducible (recorded in `_discrepancies.md`). Cumulative = sum over k ≤ h. Method recorded: `simulation` (h > 1).
		26	+
		27	+## 7. Implementation
		28	+- `src/wp46/models/arch_models.py` (`SPECS["tarch11"]`), arch 8.0.0.
		29	+- Cross-check: rugarch `fGARCH(TGARCH)`-t was fitted on the fixture (`results/metrics/rugarch_crosscheck.json["rugarch"]["tgarch11_t"]`, log-likelihood −3378.2657, converged) **but no arch fit of `tarch11` was computed in `tests/replication/test_rugarch.py`, so no comparison exists** (the `comparison` block has no `tgarch11_t` entry). Gap recorded in `_discrepancies.md`.
		30	+- Tests: none dedicated (not in the pilot).
		31	+
		32	+## 8. Known limitations / failure modes
		33	+Boundary optima at α = 0 (all asymmetry on γ); the power-1 recursion is less conditioned for very small |ε| (percent units mitigate); simulation forecasts carry Monte-Carlo error (2,000 paths) and, for non-normal laws, are not seeded; no admissibility flag beyond the optimizer constraints.
		34	+
		35	+## 9. Computational footprint
		36	+Estimation like GJR; forecasts by simulation (22 × 2,000 draws per origin) — one of the slower forecast steps with `aparch11`, `egarch11`, `avgarch11`. No pilot timing.
		37	+
		38	+## 10. Literature expectations
		39	+`zakoian1994threshold` and `hansen2005forecast` (who include "TGARCH"/"TS-GARCH" power-1 variants) report that absolute-value/threshold specifications are competitive on equities; the matrix flags the definitional ambiguity rather than a ranking. Expectation: close to GJR and APARCH on equities (H1), with the simulation step adding cost (H5).

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		1	+% appendix formulas placeholder

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		1	+% appendix verification placeholder

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		1	+% ============================================================================
		2	+% UQO Working Paper No. 46 — Four Decades of Conditional Volatility Modeling
		3	+% ============================================================================
		4	+\documentclass[12pt,letterpaper]{article}
		5	+\usepackage[utf8]{inputenc}
		6	+\usepackage[T1]{fontenc}
		7	+\usepackage[english]{babel}
		8	+\usepackage[letterpaper,top=1in,bottom=1in,left=1in,right=1in]{geometry}
		9	+\usepackage{amsmath,amsthm}
		10	+\usepackage{mathtools}
		11	+\allowdisplaybreaks
		12	+\usepackage{newtxtext}
		13	+\usepackage[varvw,cmintegrals,cmbraces]{newtxmath}
		14	+\usepackage{bm}
		15	+\usepackage{dsfont}
		16	+\usepackage{microtype}
		17	+\usepackage{setspace}
		18	+\onehalfspacing
		19	+\usepackage{booktabs,tabularx,multirow,threeparttable,longtable,adjustbox,rotating}
		20	+\usepackage{siunitx}
		21	+\sisetup{input-symbols = {()}, table-format = -1.3, parse-numbers = false}
		22	+\usepackage{graphicx,subcaption,float}
		23	+\usepackage[dvipsnames]{xcolor}
		24	+\usepackage[pdftitle={Four Decades of Conditional Volatility Modeling: A Comprehensive Empirical Benchmark Across Assets, Frequencies, and Market Regimes},
		25	+ pdfauthor={Simon-Pierre Boucher}, colorlinks=true, linkcolor=NavyBlue, citecolor=NavyBlue, urlcolor=NavyBlue, breaklinks=true]{hyperref}
		26	+\usepackage[authoryear,round,semicolon]{natbib}
		27	+\bibliographystyle{aer}
		28	+\usepackage[font=small,labelfont=bf,labelsep=period,justification=justified,singlelinecheck=false]{caption}
		29	+\usepackage{fancyhdr}
		30	+\pagestyle{fancy}\fancyhf{}\renewcommand{\headrulewidth}{0pt}\fancyfoot[C]{\thepage}
		31	+\usepackage{titlesec}
		32	+\titleformat{\section}{\large\bfseries}{\thesection.}{0.5em}{}
		33	+\titleformat{\subsection}{\normalsize\bfseries}{\thesubsection.}{0.5em}{}
		34	+\titleformat{\subsubsection}{\normalsize\itshape}{\thesubsubsection.}{0.5em}{}
		35	+\usepackage[toc,page]{appendix}
		36	+\usepackage{enumitem}
		37	+\setlist{nosep,leftmargin=*}
		38	+\theoremstyle{definition}
		39	+\newtheorem{hypothesis}{Hypothesis}
		40	+
		41	+% ============================================================================
		42	+% METADATA (numbers.tex is generated by scripts/build_paper.py — never edited by hand)
		43	+% ============================================================================
		44	+\input{numbers}
		45	+\newcommand{\WPnumber}{46}
		46	+\newcommand{\WPtitle}{Four Decades of Conditional Volatility Modeling: A Comprehensive Empirical Benchmark Across Assets, Frequencies, and Market Regimes}
		47	+\newcommand{\WPsubtitle}{Does Anything Beat GARCH(1,1) Once Information Sets, Failures and Compute Are Priced In?}
		48	+\newcommand{\WPdate}{November 2026}
		49	+\newcommand{\WPversion}{1.0}
		50	+\newcommand{\WPabstract}{\input{sections/abstract}}
		51	+\newcommand{\WPkeywords}{volatility forecasting, GARCH, realized volatility, model confidence set, pre-registration, forecast evaluation, tail risk}
		52	+\newcommand{\WPjel}{C22, C52, C53, C58, G17}
		53	+\newcommand{\WPauthor}{Simon-Pierre Boucher}
		54	+\newcommand{\WPaffiliation}{Département des sciences administratives\\Université du Québec en Outaouais}
		55	+\newcommand{\WPemail}{simon-pierre.boucher@uqo.ca}
		56	+\newcommand{\WPaddress}{Gatineau -- Pavillon Alexandre-Taché\\283, boulevard Alexandre-Taché\\Gatineau, Québec, Canada J9A 1L8}
		57	+
		58	+% --- Notation ---
		59	+\newcommand{\E}{\mathbb{E}}
		60	+\newcommand{\Var}{\mathrm{Var}}
		61	+\newcommand{\Prob}{\mathbb{P}}
		62	+\newcommand{\ind}[1]{\mathds{1}\!\left[#1\right]}
		63	+\newcommand{\RV}{\mathrm{RV}}
		64	+\newcommand{\QLIKE}{\mathrm{QLIKE}}
		65	+\newcommand{\F}{\mathcal{F}}
		66	+
		67	+\begin{document}
		68	+\input{sections/titlepage}
		69	+\input{sections/introduction}
		70	+\input{sections/literature}
		71	+\input{sections/taxonomy}
		72	+\input{sections/data}
		73	+\input{sections/design}
		74	+\input{sections/results_daily}
		75	+\input{sections/results_realized}
		76	+\input{sections/results_tail_multivariate}
		77	+\input{sections/results_heterogeneity}
		78	+\input{sections/frontier}
		79	+\input{sections/conclusion}
		80	+\bibliography{references}
		81	+\clearpage
		82	+\begin{appendices}
		83	+\input{appendices/formulas}
		84	+\input{appendices/data_provenance}
		85	+\input{appendices/verification}
		86	+\input{appendices/extra_tables}
		87	+\end{appendices}
		88	+\end{document}

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		1	+% abstract — generated draft placeholder (to be written after the benchmark)

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		1	+% conclusion — generated draft placeholder (to be written after the benchmark)

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		1	+figure,sources,n,note
		2	+fig_timeline.pdf,configs/model_registry.yaml,26,
		3	+fig_coverage.pdf,results/manifests/panels_manifest.json,42,
		4	+fig_volseries.pdf,panels/SPY.parquet;panels/CL.parquet;panels/EURUSD.parquet;panels/BTC.parquet,4,
		5	+fig_timeline.pdf,configs/model_registry.yaml,26,

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	\ No newline at end of file

added results/manifests/panels_manifest.json +464 −0

@@ -0,0 +1,464 @@
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		243	+ },
		244	+ "XOM": {
		245	+ "asset_class": "stock",
		246	+ "symbol": "XOM",
		247	+ "rows": 6746,
		248	+ "first": "2000-01-05",
		249	+ "last": "2026-10-30",
		250	+ "valid_rm_rate": 1.0,
		251	+ "n_nan_r": 0,
		252	+ "sha256": "6c7d0f877c6f5fcb379da7813c72470a12116f8f37cbf5f20c8ef8bdec6c5436",
		253	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/XOM.parquet"
		254	+ },
		255	+ "NVDA": {
		256	+ "asset_class": "stock",
		257	+ "symbol": "NVDA",
		258	+ "rows": 6746,
		259	+ "first": "2000-01-05",
		260	+ "last": "2026-10-30",
		261	+ "valid_rm_rate": 0.9968870441743255,
		262	+ "n_nan_r": 0,
		263	+ "sha256": "6c12cd4846cef591fa373facc3dc12f4cc89754050671fed3b04a587ebe2dc5f",
		264	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/NVDA.parquet"
		265	+ },
		266	+ "JNJ": {
		267	+ "asset_class": "stock",
		268	+ "symbol": "JNJ",
		269	+ "rows": 6746,
		270	+ "first": "2000-01-05",
		271	+ "last": "2026-10-30",
		272	+ "valid_rm_rate": 1.0,
		273	+ "n_nan_r": 0,
		274	+ "sha256": "c6cfe87507bf085937b0a1563bd9678cf2544e1cc9dee6d01e4d55429b85f985",
		275	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/JNJ.parquet"
		276	+ },
		277	+ "PG": {
		278	+ "asset_class": "stock",
		279	+ "symbol": "PG",
		280	+ "rows": 6746,
		281	+ "first": "2000-01-05",
		282	+ "last": "2026-10-30",
		283	+ "valid_rm_rate": 1.0,
		284	+ "n_nan_r": 0,
		285	+ "sha256": "0e326d51ae11d8df415bc07b134cf1b3d1640618b7853b58986ded9855911d35",
		286	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/PG.parquet"
		287	+ },
		288	+ "KO": {
		289	+ "asset_class": "stock",
		290	+ "symbol": "KO",
		291	+ "rows": 6746,
		292	+ "first": "2000-01-05",
		293	+ "last": "2026-10-30",
		294	+ "valid_rm_rate": 0.9998517640083012,
		295	+ "n_nan_r": 0,
		296	+ "sha256": "66e0be93e9986b929dcc4049db403d67a18b1196269e02a3f9b685f12ddb0c90",
		297	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/KO.parquet"
		298	+ },
		299	+ "WMT": {
		300	+ "asset_class": "stock",
		301	+ "symbol": "WMT",
		302	+ "rows": 6746,
		303	+ "first": "2000-01-05",
		304	+ "last": "2026-10-30",
		305	+ "valid_rm_rate": 1.0,
		306	+ "n_nan_r": 0,
		307	+ "sha256": "0d14468454c90d9ab5dfe5c344bcc3327ed5463e877ffd02aa15ed9c4197fa32",
		308	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/WMT.parquet"
		309	+ },
		310	+ "NQ": {
		311	+ "asset_class": "futures",
		312	+ "symbol": "NQ",
		313	+ "rows": 4865,
		314	+ "first": "2008-01-03",
		315	+ "last": "2026-10-30",
		316	+ "valid_rm_rate": 0.990339157245632,
		317	+ "n_nan_r": 0,
		318	+ "sha256": "387573717d58555894d2e8cc800b6a3758064b5cb40d056763ba9680fc6e0e03",
		319	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/NQ.parquet"
		320	+ },
		321	+ "NG": {
		322	+ "asset_class": "futures",
		323	+ "symbol": "NG",
		324	+ "rows": 4817,
		325	+ "first": "2008-02-26",
		326	+ "last": "2026-10-30",
		327	+ "valid_rm_rate": 0.9319078264479966,
		328	+ "n_nan_r": 0,
		329	+ "sha256": "b86776b81c2a9f7c6aa733e5a3ca7eba74845ad16d26be340b136d498f010269",
		330	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/NG.parquet"
		331	+ },
		332	+ "CL": {
		333	+ "asset_class": "futures",
		334	+ "symbol": "CL",
		335	+ "rows": 4859,
		336	+ "first": "2008-01-03",
		337	+ "last": "2026-10-30",
		338	+ "valid_rm_rate": 0.998970981683474,
		339	+ "n_nan_r": 0,
		340	+ "sha256": "4e7ed276f7a62baeacfebba31665a33d2b268af7cc2136555bae5b4f38a90ea8",
		341	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/CL.parquet"
		342	+ },
		343	+ "SI": {
		344	+ "asset_class": "futures",
		345	+ "symbol": "SI",
		346	+ "rows": 4855,
		347	+ "first": "2008-01-03",
		348	+ "last": "2026-10-30",
		349	+ "valid_rm_rate": 0.9818743563336766,
		350	+ "n_nan_r": 0,
		351	+ "sha256": "c3f8515c47fbc5941c8c36ff9c6aad7781f0eb50ab82e3bf22f23c654c92a93f",
		352	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/SI.parquet"
		353	+ },
		354	+ "ZC": {
		355	+ "asset_class": "futures",
		356	+ "symbol": "ZC",
		357	+ "rows": 4688,
		358	+ "first": "2008-02-20",
		359	+ "last": "2026-10-30",
		360	+ "valid_rm_rate": 0.8869453924914675,
		361	+ "n_nan_r": 0,
		362	+ "sha256": "e0bad5c13080c2c6ebf16643f9a8193a6152dc850f80886b35d9351df8efb382",
		363	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/ZC.parquet"
		364	+ },
		365	+ "USDJPY": {
		366	+ "asset_class": "fx",
		367	+ "symbol": "USDJPY",
		368	+ "rows": 4367,
		369	+ "first": "2010-01-05",
		370	+ "last": "2026-10-30",
		371	+ "valid_rm_rate": 0.9997710098465766,
		372	+ "n_nan_r": 0,
		373	+ "sha256": "476f7d1fa33db2fbe06b554110595859e5d2b7b9428dec88c32f2b4ead5c17d8",
		374	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/USDJPY.parquet"
		375	+ },
		376	+ "GC": {
		377	+ "asset_class": "futures",
		378	+ "symbol": "GC",
		379	+ "rows": 4840,
		380	+ "first": "2008-01-29",
		381	+ "last": "2026-10-30",
		382	+ "valid_rm_rate": 0.9983471074380166,
		383	+ "n_nan_r": 0,
		384	+ "sha256": "5094537c51ea3d41ca5ad6a4c64b822ff4d2c085db45ea1b8eac80d208e6db97",
		385	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/GC.parquet"
		386	+ },
		387	+ "HG": {
		388	+ "asset_class": "futures",
		389	+ "symbol": "HG",
		390	+ "rows": 4845,
		391	+ "first": "2008-01-03",
		392	+ "last": "2026-10-30",
		393	+ "valid_rm_rate": 0.9283797729618163,
		394	+ "n_nan_r": 0,
		395	+ "sha256": "bff572d13854ce2f4a22637f8ecb40ab23e7257a887a77263fa5e1f34100a5d4",
		396	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/HG.parquet"
		397	+ },
		398	+ "AUDUSD": {
		399	+ "asset_class": "fx",
		400	+ "symbol": "AUDUSD",
		401	+ "rows": 4367,
		402	+ "first": "2010-01-05",
		403	+ "last": "2026-10-30",
		404	+ "valid_rm_rate": 0.9993130295397298,
		405	+ "n_nan_r": 0,
		406	+ "sha256": "ca8a5c10acb463237adfc0189a64e0920b59a663b6f0a739ed2299dc02ff0ae3",
		407	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/AUDUSD.parquet"
		408	+ },
		409	+ "GBPUSD": {
		410	+ "asset_class": "fx",
		411	+ "symbol": "GBPUSD",
		412	+ "rows": 4367,
		413	+ "first": "2010-01-05",
		414	+ "last": "2026-10-30",
		415	+ "valid_rm_rate": 0.9993130295397298,
		416	+ "n_nan_r": 0,
		417	+ "sha256": "173f3e9ab51b681810bf85b87c6266a8897e81da5fcadd7c186b24ec4ad4e23b",
		418	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/GBPUSD.parquet"
		419	+ },
		420	+ "USDCAD": {
		421	+ "asset_class": "fx",
		422	+ "symbol": "USDCAD",
		423	+ "rows": 4367,
		424	+ "first": "2010-01-05",
		425	+ "last": "2026-10-30",
		426	+ "valid_rm_rate": 0.9993130295397298,
		427	+ "n_nan_r": 0,
		428	+ "sha256": "13e052ac19a8122e9c464024c1e3c7eb17025e81d2743718b7578bb3fe0dd1ca",
		429	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/USDCAD.parquet"
		430	+ },
		431	+ "ZN": {
		432	+ "asset_class": "futures",
		433	+ "symbol": "ZN",
		434	+ "rows": 4743,
		435	+ "first": "2008-01-03",
		436	+ "last": "2026-10-30",
		437	+ "valid_rm_rate": 0.9386464263124604,
		438	+ "n_nan_r": 0,
		439	+ "sha256": "89af12cda66ed68f27b9dbbd6f6b056cc779bb2bc72a5497b5a3f22c1911342d",
		440	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/ZN.parquet"
		441	+ },
		442	+ "USDCHF": {
		443	+ "asset_class": "fx",
		444	+ "symbol": "USDCHF",
		445	+ "rows": 4367,
		446	+ "first": "2010-01-05",
		447	+ "last": "2026-10-30",
		448	+ "valid_rm_rate": 0.9995420196931531,
		449	+ "n_nan_r": 0,
		450	+ "sha256": "810fdfb409628ff262256eb9d2aaf2a875999dfe4939ea7dd82ac5ef3b571b76",
		451	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/USDCHF.parquet"
		452	+ },
		453	+ "ETH": {
		454	+ "asset_class": "crypto",
		455	+ "symbol": "ETH",
		456	+ "rows": 3708,
		457	+ "first": "2016-03-17",
		458	+ "last": "2026-10-30",
		459	+ "valid_rm_rate": 0.4754584681769148,
		460	+ "n_nan_r": 0,
		461	+ "sha256": "5c23ac9504584924e63a3901565bf3dc47c0de675dd32e78f51b5f45402211d7",
		462	+ "path": "/Users/simon-pierreboucher/wp46-cache/panels/ETH.parquet"
		463	+ }
		464	+}
	\ No newline at end of file

added scripts/analyze_tier_c.py +54 −0

@@ -0,0 +1,54 @@
		1	+#!/usr/bin/env python3
		2	+"""Tier C summary: mean losses per basket/model on the common sample (test period), DM vs CCC, MCS over models."""
		3	+from __future__ import annotations
		4	+
		5	+import json
		6	+import sys
		7	+from pathlib import Path
		8	+
		9	+import numpy as np
		10	+import pandas as pd
		11	+import yaml
		12	+
		13	+ROOT = Path(__file__).resolve().parents[1]
		14	+sys.path.insert(0, str(ROOT / "src"))
		15	+from wp46.inference import dm_test, mcs # noqa: E402
		16	+
		17	+PROT = yaml.safe_load(open(ROOT / "configs" / "forecast_protocol.yaml"))
		18	+
		19	+
		20	+def main(period: str = "test") -> None:
		21	+ d = ROOT / "results" / "metrics" / "tier_c"
		22	+ per = PROT["periods"][period]
		23	+ out = {}
		24	+ rows = []
		25	+ for b in sorted({p.name.split("_")[0] + "_" + p.name.split("_")[1] for p in d.glob("*.parquet")}):
		26	+ frames = {p.stem[len(b) + 1:]: pd.read_parquet(p) for p in d.glob(f"{b}_*.parquet")}
		27	+ mats = {}
		28	+ for loss in ("mv_qlike", "frobenius", "pf_qlike"):
		29	+ M = pd.DataFrame({m: f.set_index(pd.to_datetime(f["origin"]))[loss] for m, f in frames.items() if loss in f})
		30	+ M = M[(M.index >= per["start"]) & (M.index <= per["end"])].dropna()
		31	+ mats[loss] = M
		32	+ bench = "ccc_garch11" if "ccc_garch11" in M else M.columns[0]
		33	+ for m in M.columns:
		34	+ dm = dm_test(M[m].values, M[bench].values, 1)
		35	+ rows.append(dict(basket=b, loss=loss, model=m, n=len(M), mean=float(M[m].mean()), ratio=float(M[m].mean() / M[bench].mean()), dm_stat=dm["stat"], dm_p=dm["p"]))
		36	+ try:
		37	+ mc = mcs(M, size=0.10, reps=2000, block=22)
		38	+ for r in mc.itertuples():
		39	+ rows[-len(M.columns):]
		40	+ out[f"{b}|{loss}|mcs"] = {r.model: {"p": float(r.pvalue), "in": bool(r.included)} for r in mc.itertuples()}
		41	+ except Exception as e: # noqa: BLE001
		42	+ out[f"{b}|{loss}|mcs"] = {"error": repr(e)[:200]}
		43	+ status = {m: f["status"].value_counts().to_dict() for m, f in frames.items()}
		44	+ mineig = {m: float(f["min_eig"].min()) for m, f in frames.items() if "min_eig" in f}
		45	+ out[b] = {"status": status, "min_eig": mineig, "fit_time_median": {m: float(f["fit_time_s"].median()) for m, f in frames.items() if "fit_time_s" in f}}
		46	+ T = pd.DataFrame(rows)
		47	+ o = ROOT / "results" / "metrics" / f"analysis_tier_c_{period}"; o.mkdir(parents=True, exist_ok=True)
		48	+ T.to_csv(o / "tier_c_summary.csv", index=False); (o / "tier_c_extra.json").write_text(json.dumps(out, indent=1))
		49	+ (ROOT / "results" / "tables").mkdir(exist_ok=True); T.to_csv(ROOT / "results" / "tables" / f"tier_c_{period}.csv", index=False)
		50	+ print(T[T.loss == "mv_qlike"].round(4).to_string(index=False))
		51	+
		52	+
		53	+if __name__ == "__main__":
		54	+ main(sys.argv[1] if len(sys.argv) > 1 else "test")

added scripts/build_paper.py +120 −0

@@ -0,0 +1,120 @@
		1	+#!/usr/bin/env python3
		2	+"""Build every machine-generated exhibit of the paper from results/: numbers.tex (macros), tables/*.tex, figures/*.pdf.
		3	+usage: build_paper.py --run main --period test (then: cd paper && make)
		4	+Never edits prose. Every macro name is digit-free (LaTeX)."""
		5	+from __future__ import annotations
		6	+
		7	+import argparse
		8	+import glob
		9	+import json
		10	+import re
		11	+import sys
		12	+from pathlib import Path
		13	+
		14	+import numpy as np
		15	+import pandas as pd
		16	+import yaml
		17	+
		18	+ROOT = Path(__file__).resolve().parents[1]
		19	+sys.path.insert(0, str(ROOT / "src"))
		20	+from wp46.reporting import figures as F # noqa: E402
		21	+from wp46.reporting import tables as TB # noqa: E402
		22	+
		23	+REG = yaml.safe_load(open(ROOT / "configs" / "model_registry.yaml"))["models"]
		24	+PROT = yaml.safe_load(open(ROOT / "configs" / "forecast_protocol.yaml"))
		25	+WORDS = {0: "Zero", 1: "One", 2: "Two", 3: "Three", 4: "Four", 5: "Five", 6: "Six", 7: "Seven", 8: "Eight", 9: "Nine"}
		26	+
		27	+
		28	+def macro_name(s: str) -> str:
		29	+ s = re.sub(r"[^A-Za-z0-9]", " ", s).title().replace(" ", "")
		30	+ return "".join(WORDS[int(c)] if c.isdigit() else c for c in s)
		31	+
		32	+
		33	+def fnum(x, nd=3):
		34	+ if x is None or (isinstance(x, float) and not np.isfinite(x)):
		35	+ return "--"
		36	+ if isinstance(x, (int, np.integer)):
		37	+ return f"{x:,}"
		38	+ return f"{x:,.{nd}f}"
		39	+
		40	+
		41	+def main() -> None:
		42	+ ap = argparse.ArgumentParser()
		43	+ ap.add_argument("--run", default="main"); ap.add_argument("--period", default="test")
		44	+ a = ap.parse_args()
		45	+ adir = ROOT / "results" / "metrics" / f"analysis_{a.run}_{a.period}"
		46	+ per_symbol = {Path(f).stem: json.load(open(f)) for f in sorted(glob.glob(str(adir / "per_symbol" / "*.json")))}
		47	+ P = pd.read_csv(adir / "pooled.csv")
		48	+ specs_all = sorted({s for r in per_symbol.values() for s in r["horizons"]["1"]["specs"]})
		49	+ labels = {s: F.spec_label(s) for s in specs_all}
		50	+ macros = {}
		51	+ # --- headline numbers
		52	+ macros["NInstruments"] = len(per_symbol)
		53	+ macros["NSpecs"] = len(specs_all)
		54	+ macros["NModels"] = len({s.split("__")[0] for s in specs_all})
		55	+ macros["NOriginsTotal"] = sum(r["horizons"]["1"]["n_origins"] for r in per_symbol.values())
		56	+ macros["NValidTargetsHOne"] = sum(r["horizons"]["1"]["n_valid_targets"] for r in per_symbol.values())
		57	+ Pa = P[P.asset_class == "all"]
		58	+ for r in Pa.itertuples():
		59	+ base = macro_name(f"{r.spec_id.split('__')[0]} {r.spec_id.split('__')[1]} h{r.h}")
		60	+ macros[f"Ratio{base}"] = fnum(r.mean_ratio)
		61	+ macros[f"CiLo{base}"] = fnum(1 + r.boot_ci_low, 3); macros[f"CiHi{base}"] = fnum(1 + r.boot_ci_high, 3)
		62	+ macros[f"BootP{base}"] = fnum(r.boot_p, 3); macros[f"Wins{base}"] = int(r.dm_wins_holm10); macros[f"Losses{base}"] = int(r.dm_losses_holm10)
		63	+ macros[f"McsIn{base}"] = int(r.mcs_inclusions)
		64	+ # best spec per horizon (pooled, Tier A / Tier B)
		65	+ for h in (1, 5, 22):
		66	+ for tier in ("A", "B"):
		67	+ sub = Pa[(Pa.h == h) & (Pa.tier == tier)].sort_values("mean_ratio")
		68	+ if len(sub):
		69	+ macros[f"BestSpecTier{tier}H{WORDS[h] if h < 10 else 'TwentyTwo'}"] = labels[sub.iloc[0].spec_id]
		70	+ macros[f"BestRatioTier{tier}H{WORDS[h] if h < 10 else 'TwentyTwo'}"] = fnum(sub.iloc[0].mean_ratio)
		71	+ # --- accounting table
		72	+ acc_rows = []
		73	+ for s in specs_all:
		74	+ vals = [r["accounting"].get(s, {}) for r in per_symbol.values()]
		75	+ acc_rows.append(dict(spec_id=s, family=F.fam(s), failed=np.nanmean([v.get("failed", np.nan) for v in vals]), inadmissible=np.nanmean([v.get("inadmissible", np.nan) for v in vals]),
		76	+ coverage=np.nanmean([r["horizons"]["1"]["specs"].get(s, {}).get("coverage", np.nan) for r in per_symbol.values()]),
		77	+ fit_time_median=np.nanmedian([v.get("fit_time_median", np.nan) for v in vals]), fit_time_mean=np.nanmean([v.get("fit_time_mean", np.nan) for v in vals]),
		78	+ iter_mean=np.nanmean([v.get("iter_mean", np.nan) for v in vals])))
		79	+ A = pd.DataFrame(acc_rows); A["failed_or_inadmissible"] = A["failed"].fillna(0) + A["inadmissible"].fillna(0)
		80	+ A.to_csv(ROOT / "results" / "tables" / f"accounting_{a.run}_{a.period}.csv", index=False)
		81	+ # --- tail risk table
		82	+ tr = []
		83	+ for sym, r in per_symbol.items():
		84	+ for s, v in r.get("tailrisk", {}).items():
		85	+ for lvl, t in v.items():
		86	+ if isinstance(t, dict) and "hit_rate" in t:
		87	+ tr.append(dict(symbol=sym, spec_id=s, family=F.fam(s), level=float(lvl), **t))
		88	+ TR = pd.DataFrame(tr); TR.to_csv(ROOT / "results" / "tables" / f"tailrisk_{a.run}_{a.period}.csv", index=False)
		89	+ # --- LaTeX tables
		90	+ tdir = ROOT / "paper" / "tables"; tdir.mkdir(exist_ok=True)
		91	+ tierA = [s for s in specs_all if F.fam(s) != "realized" and (s.split("__")[1] in ("normal", "t", "empirical"))]
		92	+ tierB = [s for s in specs_all if F.fam(s) == "realized"]
		93	+ TB.pooled_table(P, tierA, labels, tdir / "tab_pooled_tierA.tex")
		94	+ TB.pooled_table(P, tierB, labels, tdir / "tab_pooled_tierB.tex")
		95	+ TB.accounting_table(A, labels, tdir / "tab_accounting.tex")
		96	+ # --- figures
		97	+ F.ratio_heatmap(per_symbol, 1, tierA, F.FIG / "fig_heatmap_h1_tierA.pdf", "Tier A, h = 1")
		98	+ F.ratio_heatmap(per_symbol, 22, tierA, F.FIG / "fig_heatmap_h22_tierA.pdf", "Tier A, h = 22")
		99	+ F.ratio_heatmap(per_symbol, 1, tierB + [s for s in specs_all if s.startswith("garch11__normal")], F.FIG / "fig_heatmap_h1_tierB.pdf", "Tier B, h = 1")
		100	+ F.mcs_matrix(per_symbol, 1, "A", F.FIG / "fig_mcs_h1_tierA.pdf", "90% MCS, Tier A, h = 1")
		101	+ F.mcs_matrix(per_symbol, 22, "A", F.FIG / "fig_mcs_h22_tierA.pdf", "90% MCS, Tier A, h = 22")
		102	+ F.mcs_matrix(per_symbol, 1, "B", F.FIG / "fig_mcs_h1_tierB.pdf", "90% MCS, Tier B (+ GARCH(1,1)-N), h = 1")
		103	+ top = Pa[(Pa.h == 1)].sort_values("mean_ratio").spec_id.head(6).tolist()
		104	+ F.horizon_profile(P, [s for s in top], F.FIG / "fig_horizon.pdf")
		105	+ F.pareto(Pa[Pa.h == 1], A, F.FIG / "fig_pareto_time.pdf")
		106	+ F.pareto(Pa[Pa.h == 1], A, F.FIG / "fig_pareto_fail.pdf", xcol="failed_or_inadmissible", xlabel="Share of refits failed or inadmissible")
		107	+ F.failure_bars(A, F.FIG / "fig_failures.pdf")
		108	+ if len(TR):
		109	+ F.var_calibration(TR, F.FIG / "fig_var_1pct.pdf", "0.01"); F.var_calibration(TR, F.FIG / "fig_var_5pct.pdf", "0.05")
		110	+ F.timeline(); F.coverage_map(); F.volatility_series(["SPY", "CL", "EURUSD", "BTC"], Path.home() / "wp46-cache" / "panels", F.FIG / "fig_volseries.pdf")
		111	+ # --- numbers.tex
		112	+ lines = ["% generated by scripts/build_paper.py — do not edit"]
		113	+ for k, v in macros.items():
		114	+ lines.append(f"\\newcommand{{\\{k}}}{{{v}}}")
		115	+ (ROOT / "paper" / "numbers.tex").write_text("\n".join(lines) + "\n")
		116	+ print("macros", len(macros), "tables", len(list(tdir.glob("*.tex"))), "figures", len(list(F.FIG.glob("*.pdf"))))
		117	+
		118	+
		119	+if __name__ == "__main__":
		120	+ main()

added src/wp46/reporting/__init__.py +0 −0

added src/wp46/reporting/figures.py +234 −0

@@ -0,0 +1,234 @@
		1	+"""Figure generators (all read machine outputs; nothing hand-drawn). Each function writes a PDF to paper/figures/ and
		2	+returns the path; `audit` rows (figure, source files, n) are appended to results/manifests/figure_audit.csv."""
		3	+from __future__ import annotations
		4	+
		5	+import csv
		6	+import json
		7	+from pathlib import Path
		8	+
		9	+import matplotlib.pyplot as plt
		10	+import numpy as np
		11	+import pandas as pd
		12	+import yaml
		13	+
		14	+from . import plotstyle as ps
		15	+
		16	+ROOT = Path(__file__).resolve().parents[3]
		17	+FIG = ROOT / "paper" / "figures"
		18	+REG = yaml.safe_load(open(ROOT / "configs" / "model_registry.yaml"))["models"]
		19	+INST = yaml.safe_load(open(ROOT / "configs" / "instruments.yaml"))
		20	+CLASS_OF = {s: c for c, syms in (INST.get("universe") or INST["candidates"]).items() for s in syms}
		21	+CLASS_ORDER = ["etf", "stock", "futures", "fx", "crypto"]
		22	+CLASS_LABEL = {"etf": "ETFs", "stock": "Stocks", "futures": "Futures", "fx": "FX", "crypto": "Crypto"}
		23	+ps.apply()
		24	+
		25	+
		26	+def _audit(fig_name: str, sources: list[str], n: int, note: str = "") -> None:
		27	+ p = ROOT / "results" / "manifests" / "figure_audit.csv"
		28	+ new = not p.exists()
		29	+ with open(p, "a", newline="") as fh:
		30	+ w = csv.writer(fh)
		31	+ if new:
		32	+ w.writerow(["figure", "sources", "n", "note"])
		33	+ w.writerow([fig_name, ";".join(sources), n, note])
		34	+
		35	+
		36	+def spec_label(spec_id: str) -> str:
		37	+ mid, dist, _ = spec_id.split("__")
		38	+ lab = REG.get(mid, {}).get("label", mid).split(" (")[0]
		39	+ d = {"normal": "N", "t": "t", "ged": "GED", "skewt": "skt", "empirical": ""}[dist]
		40	+ return f"{lab}-{d}" if d else lab
		41	+
		42	+
		43	+def fam(spec_id: str) -> str:
		44	+ return REG.get(spec_id.split("__")[0], {}).get("family", "baseline")
		45	+
		46	+
		47	+# ----------------------------------------------------------------------------- 1. timeline / ontology
		48	+def timeline(path=FIG / "fig_timeline.pdf") -> Path:
		49	+ years = {"arch5": (1982, "ARCH"), "garch11": (1986, "GARCH"), "igarch11": (1986, "IGARCH"), "garchm11": (1987, "GARCH-M"),
		50	+ "avgarch11": (1986, "AVGARCH"), "nagarch11": (1990, "NAGARCH"), "egarch11": (1991, "EGARCH"), "gjr11": (1993, "GJR"),
		51	+ "aparch11": (1993, "APARCH"), "tarch11": (1994, "TGARCH"), "ewma094": (1994, "EWMA/RiskMetrics"), "qgarch11": (1995, "QGARCH"),
		52	+ "figarch11": (1996, "FIGARCH"), "fiegarch11": (1996, "FIEGARCH"), "tse": (1998, "FIAPARCH"), "cgarch11": (1999, "CGARCH"),
		53	+ "engle2002": (2002, "DCC / GARCH-X"), "davidson": (2004, "HYGARCH"), "msgarch2": (2004, "MS-GARCH (HMP)"), "rm2006": (2006, "RiskMetrics 2006"),
		54	+ "har_rv": (2009, "HAR-RV"), "heavy11": (2010, "HEAVY"), "realgarch11": (2012, "Realized GARCH"), "gas_t11": (2013, "GAS / Beta-t"),
		55	+ "engle2013": (2013, "GARCH-MIDAS"), "hansen2016": (2016, "Realized EGARCH")}
		56	+ famof = {k: REG.get(k, {}).get("family", "classical") for k in years}
		57	+ famof.update({"tse": "fractional", "engle2002": "realized", "davidson": "fractional", "engle2013": "component", "hansen2016": "realized"})
		58	+ status = {k: REG.get(k, {}).get("status", "unsupported") for k in years}
		59	+ fig, ax = plt.subplots(figsize=(ps.W2, 2.9))
		60	+ rows = {f: i for i, f in enumerate(["classical", "asymmetric", "power", "fractional", "component", "regime-switching", "score-driven", "realized", "baseline"])}
		61	+ byrow: dict[int, list] = {}
		62	+ for k, (y, lab) in years.items():
		63	+ byrow.setdefault(rows[famof[k]], []).append((y, lab, k))
		64	+ for r, items in byrow.items():
		65	+ items.sort()
		66	+ for j, (y, lab, k) in enumerate(items):
		67	+ f = famof[k]
		68	+ filled = status[k] == "implemented"
		69	+ ax.scatter(y, r, s=28, marker=ps.FAMILY_MARKER[f], color=ps.FAMILY_COLOR[f] if filled else "white", edgecolor=ps.FAMILY_COLOR[f], linewidth=0.9, zorder=3)
		70	+ # stagger labels: alternate above/below and nudge horizontally when two seminal years coincide
		71	+ same = sum(1 for yy, _, _ in items[:j] if yy == y)
		72	+ dy = 6 + 7 * (j % 2) if same == 0 else -11 - 7 * same
		73	+ ax.annotate(lab, (y, r), xytext=(0, dy), textcoords="offset points", ha="center", fontsize=5.5, color=ps.INK2)
		74	+ ax.set_yticks(list(rows.values())); ax.set_yticklabels([ps.FAMILY_LABEL[f] for f in rows])
		75	+ ax.set_xlim(1979, 2020); ax.set_xlabel("Year of the seminal publication")
		76	+ ax.grid(axis="x", alpha=0.6); ax.grid(axis="y", alpha=0.0)
		77	+ ax.text(0.99, 0.02, "filled = scored in WP46; hollow = unsupported (registry)", transform=ax.transAxes, ha="right", fontsize=6.5, color=ps.INK2)
		78	+ ps.save(fig, path); _audit(path.name, ["configs/model_registry.yaml"], len(years))
		79	+ return path
		80	+
		81	+
		82	+# ----------------------------------------------------------------------------- 2. coverage map
		83	+def coverage_map(panels_manifest=ROOT / "results" / "manifests" / "panels_manifest.json", path=FIG / "fig_coverage.pdf") -> Path:
		84	+ m = json.load(open(panels_manifest))
		85	+ rows = sorted([v for v in m.values() if "first" in v], key=lambda v: (CLASS_ORDER.index(v["asset_class"]), v["symbol"]))
		86	+ fig, ax = plt.subplots(figsize=(ps.W2, 5.4))
		87	+ for i, v in enumerate(rows):
		88	+ a, b = pd.Timestamp(v["first"]), pd.Timestamp(v["last"])
		89	+ c = ps.SLOTS[CLASS_ORDER.index(v["asset_class"])]
		90	+ ax.plot([a, b], [i, i], color=c, lw=2.2, solid_capstyle="butt")
		91	+ ax.text(b + pd.Timedelta(days=60), i, f"{v['valid_rm_rate']:.2f}", va="center", fontsize=6, color=ps.INK2)
		92	+ ax.set_yticks(range(len(rows))); ax.set_yticklabels([v["symbol"] for v in rows], fontsize=6.5)
		93	+ ax.invert_yaxis(); ax.set_xlabel("Sample (daily panel); number at right = share of sessions with valid realized measures")
		94	+ for t, lab in [(pd.Timestamp("2012-01-03"), "validation"), (pd.Timestamp("2016-01-04"), "test")]:
		95	+ ax.axvline(t, color=ps.INK3, lw=0.7, ls="--"); ax.text(t, -1.2, lab, fontsize=6.5, color=ps.INK2, ha="left")
		96	+ for k, cl in enumerate(CLASS_ORDER):
		97	+ ax.plot([], [], color=ps.SLOTS[k], lw=2.2, label=CLASS_LABEL[cl])
		98	+ ax.legend(ncol=5, loc="lower left", bbox_to_anchor=(0, 1.01))
		99	+ ps.save(fig, path); _audit(path.name, [str(panels_manifest.relative_to(ROOT))], len(rows))
		100	+ return path
		101	+
		102	+
		103	+# ----------------------------------------------------------------------------- 3. heatmap asset x spec (ratio to GARCH)
		104	+def ratio_heatmap(per_symbol: dict, h: int, specs: list[str], path: Path, title: str = "", vmin=0.7, vmax=1.3) -> Path:
		105	+ syms = sorted(per_symbol, key=lambda s: (CLASS_ORDER.index(CLASS_OF[s]), s))
		106	+ M = np.full((len(syms), len(specs)), np.nan); P = np.full_like(M, np.nan)
		107	+ for i, s in enumerate(syms):
		108	+ d = per_symbol[s]["horizons"][str(h)]["specs"] if str(h) in per_symbol[s]["horizons"] else per_symbol[s]["horizons"][h]["specs"]
		109	+ for j, sp in enumerate(specs):
		110	+ if sp in d:
		111	+ M[i, j] = d[sp].get("ratio", np.nan); P[i, j] = d[sp].get("dm_p", np.nan)
		112	+ fig, ax = plt.subplots(figsize=(ps.W2, 0.16 * len(syms) + 1.6))
		113	+ im = ax.imshow(np.log(M), cmap=ps.DIV, vmin=np.log(vmin), vmax=np.log(vmax), aspect="auto")
		114	+ for i in range(len(syms)):
		115	+ for j in range(len(specs)):
		116	+ if np.isfinite(P[i, j]) and P[i, j] < 0.10:
		117	+ ax.text(j, i, "•", ha="center", va="center", fontsize=5, color=ps.INK)
		118	+ ax.set_xticks(range(len(specs))); ax.set_xticklabels([spec_label(s) for s in specs], rotation=90, fontsize=6)
		119	+ ax.set_yticks(range(len(syms))); ax.set_yticklabels(syms, fontsize=6)
		120	+ for k in range(1, len(syms)):
		121	+ if CLASS_OF[syms[k]] != CLASS_OF[syms[k - 1]]:
		122	+ ax.axhline(k - 0.5, color=ps.INK, lw=0.6)
		123	+ cb = fig.colorbar(im, ax=ax, fraction=0.025, pad=0.01)
		124	+ cb.set_ticks(np.log([0.7, 0.85, 1.0, 1.15, 1.3])); cb.set_ticklabels(["0.70", "0.85", "1.00", "1.15", "1.30"]); cb.set_label("QLIKE ratio to GARCH(1,1)-N (log scale)")
		125	+ ax.grid(False); ax.set_title(title, loc="left")
		126	+ ps.save(fig, path); _audit(path.name, ["results/metrics/analysis_*/per_symbol/*.json"], int(np.isfinite(M).sum()), "• = DM p<0.10 (HAC)")
		127	+ return path
		128	+
		129	+
		130	+# ----------------------------------------------------------------------------- 4. MCS membership matrix
		131	+def mcs_matrix(per_symbol: dict, h: int, tier: str, path: Path, title: str = "") -> Path:
		132	+ syms = sorted(per_symbol, key=lambda s: (CLASS_ORDER.index(CLASS_OF[s]), s))
		133	+ specs = sorted({sp for s in syms for sp in (per_symbol[s]["horizons"][str(h)].get(f"mcs_{tier}", {}) or {}) if sp != "error"}, key=lambda x: (fam(x), x))
		134	+ M = np.full((len(syms), len(specs)), np.nan)
		135	+ for i, s in enumerate(syms):
		136	+ d = per_symbol[s]["horizons"][str(h)].get(f"mcs_{tier}", {}) or {}
		137	+ for j, sp in enumerate(specs):
		138	+ if sp in d and isinstance(d[sp], dict):
		139	+ M[i, j] = d[sp]["p"]
		140	+ fig, ax = plt.subplots(figsize=(ps.W2, 0.16 * len(syms) + 1.6))
		141	+ im = ax.imshow(M, cmap=ps.SEQ, vmin=0, vmax=1, aspect="auto")
		142	+ for i in range(len(syms)):
		143	+ for j in range(len(specs)):
		144	+ if np.isfinite(M[i, j]) and M[i, j] >= 0.10:
		145	+ ax.text(j, i, "✓", ha="center", va="center", fontsize=5, color="white")
		146	+ ax.set_xticks(range(len(specs))); ax.set_xticklabels([spec_label(s) for s in specs], rotation=90, fontsize=6)
		147	+ ax.set_yticks(range(len(syms))); ax.set_yticklabels(syms, fontsize=6)
		148	+ for k in range(1, len(syms)):
		149	+ if CLASS_OF[syms[k]] != CLASS_OF[syms[k - 1]]:
		150	+ ax.axhline(k - 0.5, color=ps.INK, lw=0.6)
		151	+ cb = fig.colorbar(im, ax=ax, fraction=0.025, pad=0.01); cb.set_label("MCS p-value (✓ = in the 90% MCS)")
		152	+ ax.grid(False); ax.set_title(title, loc="left")
		153	+ ps.save(fig, path); _audit(path.name, ["results/metrics/analysis_*/per_symbol/*.json"], int(np.isfinite(M).sum()))
		154	+ return path
		155	+
		156	+
		157	+# ----------------------------------------------------------------------------- 5. horizon profile
		158	+def horizon_profile(pooled: pd.DataFrame, specs: list[str], path: Path, cls: str = "all") -> Path:
		159	+ P = pooled[(pooled.asset_class == cls) & (pooled.spec_id.isin(specs))]
		160	+ fig, ax = plt.subplots(figsize=(ps.W1, 2.6))
		161	+ for sp in specs:
		162	+ d = P[P.spec_id == sp].sort_values("h")
		163	+ f = fam(sp)
		164	+ ax.plot(d.h, d.mean_ratio, marker=ps.FAMILY_MARKER[f], color=ps.FAMILY_COLOR[f], label=spec_label(sp), lw=1.1)
		165	+ ax.fill_between(d.h, 1 + d.boot_ci_low, 1 + d.boot_ci_high, color=ps.FAMILY_COLOR[f], alpha=0.10, lw=0)
		166	+ ax.axhline(1, color=ps.INK3, lw=0.7); ax.set_xticks([1, 5, 22]); ax.set_xlabel("Horizon (trading days)"); ax.set_ylabel("Pooled QLIKE ratio to GARCH(1,1)-N")
		167	+ ax.legend(ncol=2, fontsize=6)
		168	+ ps.save(fig, path); _audit(path.name, ["results/tables/pooled_*.csv"], len(P))
		169	+ return path
		170	+
		171	+
		172	+# ----------------------------------------------------------------------------- 6. Pareto: accuracy vs cost / failure
		173	+def pareto(pooled_h1: pd.DataFrame, accounting: pd.DataFrame, path: Path, xcol: str = "fit_time_median", xlabel: str = "Median refit time (s, log scale)") -> Path:
		174	+ d = pooled_h1.merge(accounting, on="spec_id", how="inner")
		175	+ fig, ax = plt.subplots(figsize=(ps.W2, 3.2))
		176	+ for f in sorted(d.family.unique()):
		177	+ s = d[d.family == f]
		178	+ ax.scatter(s[xcol], s.mean_ratio, marker=ps.FAMILY_MARKER.get(f, "o"), color=ps.FAMILY_COLOR.get(f, ps.INK3), s=22, label=ps.FAMILY_LABEL.get(f, f), zorder=3, edgecolor="white", linewidth=0.4)
		179	+ # Pareto frontier (min ratio for cost <= x)
		180	+ dd = d.sort_values(xcol); best = np.minimum.accumulate(dd.mean_ratio.values)
		181	+ ax.step(dd[xcol], best, where="post", color=ps.INK, lw=0.8, ls="--", label="Pareto frontier")
		182	+ for _, r in d.nsmallest(6, "mean_ratio").iterrows():
		183	+ ax.annotate(spec_label(r.spec_id), (r[xcol], r.mean_ratio), xytext=(3, 3), textcoords="offset points", fontsize=5.5, color=ps.INK2)
		184	+ if "time" in xcol:
		185	+ ax.set_xscale("log")
		186	+ ax.axhline(1, color=ps.INK3, lw=0.7); ax.set_xlabel(xlabel); ax.set_ylabel("Pooled QLIKE ratio to GARCH(1,1)-N, h = 1")
		187	+ ax.legend(ncol=3, fontsize=6, loc="upper right")
		188	+ ps.save(fig, path); _audit(path.name, ["results/tables/pooled_*.csv", "results/tables/accounting_*.csv"], len(d))
		189	+ return path
		190	+
		191	+
		192	+# ----------------------------------------------------------------------------- 7. volatility series with episodes
		193	+def volatility_series(symbols: list[str], panels_dir: Path, path: Path) -> Path:
		194	+ prot = yaml.safe_load(open(ROOT / "configs" / "forecast_protocol.yaml"))
		195	+ fig, axes = plt.subplots(len(symbols), 1, figsize=(ps.W2, 1.25 * len(symbols)), sharex=True)
		196	+ for ax, s in zip(np.atleast_1d(axes), symbols):
		197	+ p = pd.read_parquet(panels_dir / f"{s}.parquet"); p["session"] = pd.to_datetime(p["session"])
		198	+ y = np.sqrt(252 * (p["rv_5m_ss"].where(p["valid_rm"]) + p["r_on"].fillna(0) ** 2)).rolling(22, min_periods=10).mean()
		199	+ ax.plot(p["session"], y, color=ps.SLOTS[CLASS_ORDER.index(CLASS_OF[s])], lw=0.8)
		200	+ for name, w in prot["regimes"]["calendar_episodes"].items():
		201	+ ax.axvspan(pd.Timestamp(w["start"]), pd.Timestamp(w["end"]), color="#f2f2f0", zorder=0)
		202	+ ax.text(0.01, 0.85, s, transform=ax.transAxes, fontsize=7.5, fontweight="bold")
		203	+ ax.set_ylabel("ann. vol (%)", fontsize=7)
		204	+ np.atleast_1d(axes)[-1].set_xlabel("22-day moving average of the annualized full-day realized volatility; shaded = pre-registered episodes")
		205	+ ps.save(fig, path); _audit(path.name, [f"panels/{s}.parquet" for s in symbols], len(symbols))
		206	+ return path
		207	+
		208	+
		209	+# ----------------------------------------------------------------------------- 8. failure diagnostics
		210	+def failure_bars(accounting: pd.DataFrame, path: Path) -> Path:
		211	+ d = accounting.sort_values("failed_or_inadmissible", ascending=False).head(30)
		212	+ fig, ax = plt.subplots(figsize=(ps.W2, 3.4))
		213	+ cols = [ps.FAMILY_COLOR.get(f, ps.INK3) for f in d.family]
		214	+ ax.barh(range(len(d)), 100 * d.failed_or_inadmissible, color=cols, height=0.7)
		215	+ ax.set_yticks(range(len(d))); ax.set_yticklabels([spec_label(s) for s in d.spec_id], fontsize=6); ax.invert_yaxis()
		216	+ ax.set_xlabel("Share of refits failed or inadmissible (%), all instruments, test period")
		217	+ ps.save(fig, path); _audit(path.name, ["results/tables/accounting_*.csv"], len(d))
		218	+ return path
		219	+
		220	+
		221	+# ----------------------------------------------------------------------------- 9. VaR exceedance diagnostics
		222	+def var_calibration(tail: pd.DataFrame, path: Path, level: str = "0.01") -> Path:
		223	+ d = tail[tail.level == float(level)]
		224	+ g = d.groupby(["spec_id", "family"]).agg(hit=("hit_rate", "median"), lo=("hit_rate", lambda x: x.quantile(0.25)), hi=("hit_rate", lambda x: x.quantile(0.75)),
		225	+ rej=("kupiec_p", lambda x: (x < 0.05).mean())).reset_index().sort_values("hit")
		226	+ fig, ax = plt.subplots(figsize=(ps.W2, 3.6))
		227	+ for i, r in enumerate(g.itertuples()):
		228	+ c = ps.FAMILY_COLOR.get(r.family, ps.INK3)
		229	+ ax.plot([100 * r.lo, 100 * r.hi], [i, i], color=c, lw=1.0); ax.scatter(100 * r.hit, i, color=c, s=14, zorder=3, marker=ps.FAMILY_MARKER.get(r.family, "o"))
		230	+ ax.axvline(100 * float(level), color=ps.INK, lw=0.8, ls="--")
		231	+ ax.set_yticks(range(len(g))); ax.set_yticklabels([spec_label(s) for s in g.spec_id], fontsize=5.5)
		232	+ ax.set_xlabel(f"Realized {100*float(level):.0f}% VaR exceedance rate across instruments (median, interquartile range; dashed = nominal)")
		233	+ ps.save(fig, path); _audit(path.name, ["results/tables/tailrisk_*.csv"], len(g))
		234	+ return path

added src/wp46/reporting/plotstyle.py +40 −0

@@ -0,0 +1,40 @@
		1	+"""Journal figure style for WP46 (matplotlib, vector PDF). Categorical colours = the validated reference palette of the
		2	+dataviz method (fixed slot order, never cycled), assigned by model FAMILY (identity), with distinct markers/linestyles so
		3	+that identity never rests on colour alone (print, CVD). Sequential = single hue (blue) light->dark; diverging = blue/red
		4	+with a neutral grey midpoint. Text always in ink colours; thin marks; recessive grid; no truncated axes."""
		5	+from __future__ import annotations
		6	+
		7	+import matplotlib as mpl
		8	+import matplotlib.pyplot as plt
		9	+from matplotlib.colors import LinearSegmentedColormap
		10	+
		11	+SLOTS = ["#2a78d6", "#eb6834", "#1baf7a", "#eda100", "#e87ba4", "#008300", "#6250d6", "#e34948"]
		12	+FAMILY_COLOR = {"classical": SLOTS[0], "asymmetric": SLOTS[1], "power": SLOTS[2], "fractional": SLOTS[3], "component": SLOTS[4],
		13	+ "regime-switching": SLOTS[5], "score-driven": SLOTS[6], "realized": SLOTS[7], "baseline": "#7a7975", "multivariate": SLOTS[0]}
		14	+FAMILY_MARKER = {"classical": "o", "asymmetric": "s", "power": "^", "fractional": "D", "component": "v", "regime-switching": "P",
		15	+ "score-driven": "X", "realized": "*", "baseline": "x"}
		16	+FAMILY_LABEL = {"classical": "Classical GARCH", "asymmetric": "Asymmetric", "power": "Power/absolute", "fractional": "Fractional",
		17	+ "component": "Component", "regime-switching": "Regime switching", "score-driven": "Score-driven", "realized": "Realized-measure",
		18	+ "baseline": "Baselines"}
		19	+INK, INK2, INK3 = "#0b0b0b", "#52514e", "#8a8984"
		20	+SURFACE = "#ffffff"
		21	+SEQ = LinearSegmentedColormap.from_list("wp46_seq", ["#e8f0fb", "#2a78d6", "#0d2f5c"])
		22	+DIV = LinearSegmentedColormap.from_list("wp46_div", ["#0d3f8f", "#2a78d6", "#f2f2f0", "#e34948", "#7a1716"])
		23	+W1, W2 = 3.25, 6.5 # column widths in inches
		24	+
		25	+
		26	+def apply() -> None:
		27	+ mpl.rcParams.update({
		28	+ "font.family": "serif", "font.serif": ["STIX Two Text", "Times New Roman", "Times", "DejaVu Serif"], "mathtext.fontset": "stix",
		29	+ "font.size": 8.5, "axes.labelsize": 8.5, "axes.titlesize": 9, "legend.fontsize": 7.5, "xtick.labelsize": 7.5, "ytick.labelsize": 7.5,
		30	+ "axes.edgecolor": INK3, "axes.linewidth": 0.6, "axes.spines.top": False, "axes.spines.right": False,
		31	+ "axes.grid": True, "grid.color": "#e3e2de", "grid.linewidth": 0.5, "axes.axisbelow": True,
		32	+ "xtick.color": INK2, "ytick.color": INK2, "text.color": INK, "axes.labelcolor": INK,
		33	+ "lines.linewidth": 1.2, "lines.markersize": 4, "legend.frameon": False, "figure.dpi": 150, "savefig.dpi": 300,
		34	+ "pdf.fonttype": 42, "ps.fonttype": 42, "figure.facecolor": SURFACE, "axes.facecolor": SURFACE,
		35	+ })
		36	+
		37	+
		38	+def save(fig, path, **kw) -> None:
		39	+ fig.savefig(path, bbox_inches="tight", metadata={"CreationDate": None, "Creator": "wp46"}, **kw)
		40	+ plt.close(fig)

added src/wp46/reporting/tables.py +42 −0

@@ -0,0 +1,42 @@
		1	+"""LaTeX table fragments from the analysis outputs (booktabs; numbers formatted here, never by hand)."""
		2	+from __future__ import annotations
		3	+
		4	+from pathlib import Path
		5	+
		6	+import numpy as np
		7	+import pandas as pd
		8	+
		9	+
		10	+def fmt(x, nd=3):
		11	+ return "--" if x is None or (isinstance(x, float) and not np.isfinite(x)) else f"{x:.{nd}f}"
		12	+
		13	+
		14	+def stars(p):
		15	+ return "" if not np.isfinite(p) else ("***" if p < 0.01 else "**" if p < 0.05 else "*" if p < 0.10 else "")
		16	+
		17	+
		18	+def pooled_table(P: pd.DataFrame, specs: list[str], labels: dict, path: Path, caption_note: str = "") -> None:
		19	+ rows = []
		20	+ for sp in specs:
		21	+ cells = [labels.get(sp, sp)]
		22	+ for h in (1, 5, 22):
		23	+ r = P[(P.spec_id == sp) & (P.h == h) & (P.asset_class == "all")]
		24	+ if len(r):
		25	+ r = r.iloc[0]
		26	+ cells.append(f"{fmt(r.mean_ratio)}{stars(r.boot_p)}")
		27	+ cells.append(f"[{fmt(1+r.boot_ci_low, 2)}, {fmt(1+r.boot_ci_high, 2)}]")
		28	+ cells.append(f"{int(r.dm_wins_holm10)}/{int(r.dm_losses_holm10)}/{int(r.n_instruments)}")
		29	+ else:
		30	+ cells += ["--", "--", "--"]
		31	+ rows.append(" & ".join(cells) + r" \\")
		32	+ head = (r"\begin{tabular}{l" + "ccc" * 3 + "}\n\\toprule\n & \\multicolumn{3}{c}{$h=1$} & \\multicolumn{3}{c}{$h=5$} & \\multicolumn{3}{c}{$h=22$} \\\\\n"
		33	+ r"\cmidrule(lr){2-4}\cmidrule(lr){5-7}\cmidrule(lr){8-10}" + "\nModel & ratio & 90\\% CI & W/L/N & ratio & 90\\% CI & W/L/N & ratio & 90\\% CI & W/L/N \\\\\n\\midrule\n")
		34	+ path.write_text(head + "\n".join(rows) + "\n\\bottomrule\n\\end{tabular}\n")
		35	+
		36	+
		37	+def accounting_table(A: pd.DataFrame, labels: dict, path: Path) -> None:
		38	+ rows = []
		39	+ for r in A.sort_values(["family", "spec_id"]).itertuples():
		40	+ rows.append(f"{labels.get(r.spec_id, r.spec_id)} & {fmt(100*r.failed,1)} & {fmt(100*r.inadmissible,1)} & {fmt(100*r.coverage,1)} & {fmt(r.fit_time_median,3)} & {fmt(r.iter_mean,0)} \\\\")
		41	+ head = "\\begin{tabular}{lrrrrr}\n\\toprule\nModel & Failed (\\%) & Inadmissible (\\%) & Coverage (\\%) & Median refit (s) & Iterations \\\\\n\\midrule\n"
		42	+ path.write_text(head + "\n".join(rows) + "\n\\bottomrule\n\\end{tabular}\n")
		43