WP46: cross-check table generator, GAS nesting test, R test fixes

Simon-Pierre Boucher committed 3 h ago (Nov 9, 2026) parent 0ec77f6 Browse files

6 changed files +949 −8

modified results/metrics/rugarch_crosscheck.json +895 −1

++{
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++    "alpha[1]": 0.057964365256935116,
++    "gamma[1]": 0.044879036112941945,
++    "beta[1]": 0.9087627957941734,
++    "nu": 7.008124248061719
++   }
++  },
++  "garchx_return_eq_n": {
++   "loglik_return_eq": -3438.586220723607,
++   "params": {
++    "mu": 0.02479808355478625,
++    "omega": 0.012594718447088777,
++    "alpha": 0.06357846956031842,
++    "gamma": 0.03014516962527771,
++    "beta": 0.8836427005054038,
++    "omega_r": 0.02806809415988359,
++    "alpha_r": 0.25763411206863546,
++    "beta_r": 0.703096461404205
++   }
++  },
++  "gas_t": {
++   "loglik": -3382.3247298931783,
++   "params": {
++    "mu": 0.026563394804317145,
++    "omega": 0.01752968423127462,
++    "alpha": 0.07009304684270816,
++    "beta": 0.9731633946984728,
++    "nu": 7.482311787452809
++   }
++  },
++  "msgarch2_n": {
++   "loglik": -3380.1503040341104,
++   "params": {
++    "mu": 0.028792532353829083,
++    "p11": 0.8525826361976698,
++    "p22": 0.2884378038218934,
++    "omega1": 0.010424987363004993,
++    "alpha1": 0.05685929239804599,
++    "beta1": 0.8958063753292224,
++    "omega2": 0.06784174791879186,
++    "alpha2": 0.07829796399601435,
++    "beta2": 0.9217015511532453
++   },
++   "var_forecast": [
++    0.3882185430826159,
++    0.4044774039047424,
++    0.41326620367625855,
++    0.42075509939894873,
++    0.42782720411159547,
++    0.43461678979854085,
++    0.441151672526237,
++    0.4474442041543941,
++    0.4535041795225775,
++    0.4593406834445015,
++    0.46496237003000207,
++    0.47037751887572055,
++    0.47559405863822757,
++    0.48061958504571256,
++    0.48546137724718086,
++    0.49012641311300276,
++    0.494621383630559,
++    0.4989527064677157,
++    0.5031265387627439,
++    0.5071487891935675,
++    0.5110251293749857,
++    0.5147610046287768
++   ]
++  }
++ },
++ "comparison": {
++  "garch11_t": {
++   "rugarch_loglik": -3379.50279602402,
++   "ours_loglik": -3379.461796995996,
++   "rugarch_coef": {
++    "mu": 0.0272520855544271,
++    "omega": 0.0174152246241149,
++    "alpha1": 0.0766441622812045,
++    "beta1": 0.897925769946588,
++    "shape": 6.92334228660823
++   },
++   "ours_params": {
++    "mu": 0.0272552165475274,
++    "omega": 0.017418392193745573,
++    "alpha[1]": 0.0766713206199158,
++    "beta[1]": 0.8979047644168446,
++    "nu": 6.915683563286161
++   },
++   "diff": 0.04099902802408906
++  },
++  "gjr11_t": {
++   "rugarch_loglik": -3372.99049098085,
++   "ours_loglik": -3372.953632113916,
++   "rugarch_coef": {
++    "mu": 0.0223490291478538,
++    "omega": 0.0174956405075909,
++    "alpha1": 0.0426372227127993,
++    "beta1": 0.900398468982572,
++    "gamma1": 0.0620237392157647,
++    "shape": 7.00993566707479
++   },
++   "ours_params": {
++    "mu": 0.02234711621886731,
++    "omega": 0.017500497241068884,
++    "alpha[1]": 0.04266800772043423,
++    "gamma[1]": 0.062034959552290915,
++    "beta[1]": 0.9003610818426231,
++    "nu": 7.002485146679242
++   },
++   "diff": 0.036858866934380785
++  },
++  "egarch11_t": {
++   "rugarch_loglik": -3376.68334976327,
++   "ours_loglik": -3376.6704308851477,
++   "rugarch_coef": {
++    "mu": 0.0188171893640434,
++    "omega": -0.0170849884697369,
++    "alpha1": -0.0444147835573704,
++    "beta1": 0.968708686881274,
++    "gamma1": 0.156456196163122,
++    "shape": 6.99039734118426
++   },
++   "ours_params": {
++    "mu": 0.018828915964200128,
++    "omega": -0.011034952859929866,
++    "alpha[1]": 0.15651669565747273,
++    "gamma[1]": -0.044438618764715755,
++    "beta[1]": 0.9686789065580167,
++    "nu": 6.98363424230133
++   },
++   "diff": 0.01291887812249115
++  },
++  "aparch11_t": {
++   "rugarch_loglik": -3372.84892807275,
++   "ours_loglik": -3372.7593223523118,
++   "rugarch_coef": {
++    "mu": 0.0219498032140538,
++    "omega": 0.0182869465555667,
++    "alpha1": 0.0734928958434684,
++    "beta1": 0.901720469927853,
++    "gamma1": 0.230203192227028,
++    "delta": 1.84060080476714,
++    "shape": 7.0279389234871
++   },
++   "ours_params": {
++    "mu": 0.021873566385943646,
++    "omega": 0.01845096728792146,
++    "alpha[1]": 0.0741123489743683,
++    "gamma[1]": 0.23185125233775555,
++    "beta[1]": 0.9019093525442575,
++    "delta": 1.810851742298129,
++    "nu": 7.017009293700917
++   },
++   "diff": 0.08960572043815773
++  },
++  "figarch11_t": {
++   "rugarch_loglik": -3381.97980336414,
++   "ours_loglik": -3383.4506039107964,
++   "rugarch_coef": {
++    "mu": 0.028208711165193,
++    "omega": 0.0134114399389171,
++    "alpha1": 0.26282947219305,
++    "beta1": 0.780139313197165,
++    "delta": 0.631140189523463,
++    "shape": 6.41803885552558
++   },
++   "ours_params": {
++    "mu": 0.027341455003779366,
++    "omega": 0.018557454873431037,
++    "phi": 0.2272419820409831,
++    "d": 0.545516035918098,
++    "beta": 0.6927227001090212,
++    "nu": 6.4546568465574365
++   },
++   "diff": -1.4708005466563918
++  },
++  "igarch11_t": {
++   "rugarch_loglik": -3385.51714991758,
++   "ours_loglik": -3385.466983993589,
++   "rugarch_coef": {
++    "mu": 0.0273555996213812,
++    "omega": 0.00822664842815519,
++    "alpha1": 0.0911285633973949,
++    "beta1": 0.908871436602605,
++    "shape": 5.98132454596413
++   },
++   "ours_params": {
++    "mu": 0.027354323480404286,
++    "omega": 0.008236861969721824,
++    "alpha": 0.09116543468488557,
++    "nu": 5.973513461861548
++   },
++   "diff": 0.05016592399078945
++  },
++  "cgarch11_t": {
++   "rugarch_loglik": -3379.76186721388,
++   "ours_loglik": -3379.461678802403,
++   "rugarch_coef": {
++    "mu": 0.0273402099302754,
++    "omega": 0.0178411646698263,
++    "alpha1": 0.0472240690102781,
++    "beta1": 0.926336229155417,
++    "eta11": 0.973560849178106,
++    "eta21": 0.0299126275164861,
++    "shape": 6.89529147023611
++   },
++   "ours_params": {
++    "mu": 0.027257000911121945,
++    "omega": 0.01736733788836256,
++    "rho": 0.9746416050735823,
++    "phi": 0.03393172704779641,
++    "alpha": 0.04272735073115051,
++    "beta": 0.9317694810875947,
++    "nu": 6.9169232979627395
++   },
++   "diff": 0.3001884114769382
++  },
++  "nagarch11_t": {
++   "rugarch_loglik": -3373.35050140171,
++   "ours_loglik": -3373.3123413949697,
++   "rugarch_coef": {
++    "mu": 0.0213271608385587,
++    "omega": 0.0169277508131017,
++    "alpha1": 0.0706508464116506,
++    "beta1": 0.895992701235594,
++    "eta21": 0.339825862746243,
++    "shape": 7.06871565857494
++   },
++   "ours_params": {
++    "mu": 0.02133256452316757,
++    "omega": 0.016934592629425642,
++    "alpha": 0.07068774289404209,
++    "theta": 0.3397290537185268,
++    "beta": 0.8959513510919638,
++    "nu": 7.06163608387285
++   },
++   "diff": 0.038160006740326935
++  },
++  "garch11_skewt": {
++   "rugarch_loglik": -3379.31479592696,
++   "ours_loglik": -3379.275642878361,
++   "rugarch_coef": {
++    "mu": 0.0247602319116539,
++    "omega": 0.017295740459104,
++    "alpha1": 0.0762213395583014,
++    "beta1": 0.89844516796877,
++    "skew": 0.984514907132941,
++    "shape": 6.94374052154004
++   },
++   "ours_params": {
++    "mu": 0.0247698562671931,
++    "omega": 0.017301334377567416,
++    "alpha[1]": 0.07625585117671085,
++    "beta[1]": 0.8984117763911253,
++    "eta": 6.936304710964621,
++    "lambda": -0.01552668380206145
++   },
++   "diff": 0.0391530485990188
++  },
++  "realgarch11_n": {
++   "rugarch_loglik": null,
++   "ours_loglik": -4989.212643221531,
++   "rugarch_coef": null,
++   "ours_params": {
++    "mu": 0.01677936657297113,
++    "omega": -0.0310098669169496,
++    "beta": 0.7043221645737289,
++    "gamma": 0.2567538036768552,
++    "xi": 0.04191198028547544,
++    "phi": 1.0024048779109547,
++    "tau1": 0.0010267719765093414,
++    "tau2": 0.013224579404035812,
++    "log_sigma_u": -0.9137863869883917
++   },
++   "diff": null
++  }
++ }
++}
+ \ No newline at end of file

modified scripts/build_paper.py +3 −0

				`@@ -99,6 +99,9 @@ def main() -> None:`
99	TB.pooled_table(P, tierA, labels, tdir / "tab_pooled_tierA.tex")	99	TB.pooled_table(P, tierA, labels, tdir / "tab_pooled_tierA.tex")
100	TB.pooled_table(P, tierB, labels, tdir / "tab_pooled_tierB.tex")	100	TB.pooled_table(P, tierB, labels, tdir / "tab_pooled_tierB.tex")
101	TB.accounting_table(A, labels, tdir / "tab_accounting.tex")	101	TB.accounting_table(A, labels, tdir / "tab_accounting.tex")
		102	+ cc = ROOT / "results" / "metrics" / "rugarch_crosscheck.json"
		103	+ if cc.exists():
		104	+ TB.crosscheck_table(json.load(open(cc)), tdir / "tab_crosscheck.tex")
102	# --- figures	105	# --- figures
103	F.ratio_heatmap(per_symbol, 1, tierA, F.FIG / "fig_heatmap_h1_tierA.pdf", "Tier A, h = 1")	106	F.ratio_heatmap(per_symbol, 1, tierA, F.FIG / "fig_heatmap_h1_tierA.pdf", "Tier A, h = 1")
104	F.ratio_heatmap(per_symbol, 22, tierA, F.FIG / "fig_heatmap_h22_tierA.pdf", "Tier A, h = 22")	107	F.ratio_heatmap(per_symbol, 22, tierA, F.FIG / "fig_heatmap_h22_tierA.pdf", "Tier A, h = 22")

modified src/wp46/reporting/tables.py +32 −0

+         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)} \\\\")
+     head = "\\begin{tabular}{lrrrrr}\n\\toprule\nModel & Failed (\\%) & Inadmissible (\\%) & Coverage (\\%) & Median refit (s) & Iterations \\\\\n\\midrule\n"
+     path.write_text(head + "\n".join(rows) + "\n\\bottomrule\n\\end{tabular}\n")
++
++
++def crosscheck_table(cc: dict, path: Path) -> None:
++    """results/metrics/rugarch_crosscheck.json -> LaTeX rows (model, R package/model, R loglik, ours, difference, note)."""
++    R, O = cc.get("rugarch", {}), cc.get("ours", {})
++    rows = []
++    spec = [("garch11_t", "GARCH(1,1)-$t$", "rugarch sGARCH"), ("garch11_skewt", "GARCH(1,1)-skew-$t$", "rugarch sGARCH/sstd"),
++            ("gjr11_t", "GJR(1,1)-$t$", "rugarch gjrGARCH"), ("tgarch11_t", "TGARCH(1,1)-$t$", "rugarch fGARCH/TGARCH"),
++            ("egarch11_t", "EGARCH(1,1)-$t$", "rugarch eGARCH"), ("aparch11_t", "APARCH(1,1)-$t$", "rugarch apARCH"),
++            ("figarch11_t", "FIGARCH(1,$d$,1)-$t$", "rugarch fiGARCH"), ("igarch11_t", "IGARCH(1,1)-$t$", "rugarch iGARCH"),
++            ("cgarch11_t", "Component GARCH-$t$", "rugarch csGARCH"), ("nagarch11_t", "NAGARCH(1,1)-$t$", "rugarch fGARCH/NAGARCH"),
++            ("realgarch11_n", "Realized GARCH(1,1)-N", "rugarch realGARCH"), ("garchx_return_eq_n", "GARCH-X(1,1)-N (return eq.)", "rugarch sGARCH + vxreg"),
++            ("msgarch2_n", "MS-GARCH(2)-N", "MSGARCH FitML"), ("gas_t", "GAS-$t$(1,1)", "GAS (scale parameterization; reference)")]
++    for key, lab, pkg in spec:
++        r = R.get(key, {}); o = O.get(key, {})
++        rl = r.get("loglik") if r.get("converged") else None
++        rl = float(np.ravel(rl)[0]) if rl is not None and np.size(rl) else None
++        ol = o.get("loglik_return_eq", o.get("loglik"))
++        note = ""
++        if key == "realgarch11_n" and rl is None:
++            note = "R fit did not converge"
++        if key == "garchx_return_eq_n":
++            note = "per-observation comparison (R drops one obs.)"
++        if key == "msgarch2_n" and r.get("converged") and o.get("var_forecast"):
++            vf = np.asarray(r["vol_forecast"]) ** 2; rel = np.max(np.abs(np.asarray(o["var_forecast"]) - vf) / vf)
++            note = f"22-step variance forecasts within {100*rel:.1f}\\%"
++        if key == "gas_t":
++            note = "different parameterization, not comparable"
++        diff = fmt(ol - rl) if (ol is not None and rl is not None and key not in ("garchx_return_eq_n", "gas_t")) else "--"
++        rows.append(f"{lab} & {pkg} & {fmt(rl, 2) if rl is not None else '--'} & {fmt(ol, 2) if ol is not None else '--'} & {diff} & {note} \\\\")
++    head = "\\begin{tabular}{llrrrl}\n\\toprule\nModel & R reference & R log-lik. & Ours & Diff. & Note \\\\\n\\midrule\n"
++    path.write_text(head + "\n".join(rows) + "\n\\bottomrule\n\\end{tabular}\n")

modified tests/replication/rugarch_crosscheck.R +1 −1

				`@@ -37,7 +37,7 @@ if (requireNamespace("GAS", quietly = TRUE)) {`
37	suppressPackageStartupMessages(library(GAS))	37	suppressPackageStartupMessages(library(GAS))
38	gs <- UniGASSpec(Dist = "std", ScalingType = "Inv", GASPar = list(location = FALSE, scale = TRUE, shape = FALSE))	38	gs <- UniGASSpec(Dist = "std", ScalingType = "Inv", GASPar = list(location = FALSE, scale = TRUE, shape = FALSE))
39	gf <- tryCatch(UniGASFit(gs, r), error = function(e) NULL)	39	gf <- tryCatch(UniGASFit(gs, r), error = function(e) NULL)
40	− out2$gas_t <- tryCatch(if (!is.null(gf)) list(converged = TRUE, loglik = as.numeric(gf@Estimates$lLik), coef = as.list(gf@Estimates$lParList)) else list(converged = FALSE), error = function(e) list(converged = FALSE, error = conditionMessage(e)))	40	+ out2$gas_t <- tryCatch(if (!is.null(gf) && length(gf@Estimates$lLik) > 0) list(converged = TRUE, loglik = as.numeric(gf@Estimates$lLik)[1], coef = as.list(gf@Estimates$lParList), note = "GAS package parameterizes the t scale, not the variance: inverse-information scaling is not invariant, so this is a reference value, not the same model") else list(converged = FALSE, note = "no likelihood returned"), error = function(e) list(converged = FALSE, error = conditionMessage(e)))
41	}	41	}
42	if (requireNamespace("MSGARCH", quietly = TRUE)) {	42	if (requireNamespace("MSGARCH", quietly = TRUE)) {
43	suppressPackageStartupMessages(library(MSGARCH))	43	suppressPackageStartupMessages(library(MSGARCH))

modified tests/replication/test_rugarch.py +7 −6

				`@@ -93,22 +93,23 @@ def test_extended_crosschecks():`
93	ours["garchx_return_eq_n"] = dict(loglik_return_eq=ll_r, params=dict(zip(fx.param_names, map(float, fx.params))))	93	ours["garchx_return_eq_n"] = dict(loglik_return_eq=ll_r, params=dict(zip(fx.param_names, map(float, fx.params))))
94	rg = R["rugarch"].get("garchx_return_eq_n", {})	94	rg = R["rugarch"].get("garchx_return_eq_n", {})
95	if rg.get("converged"):	95	if rg.get("converged"):
96	− # rugarch drops the first observation (lagged regressor): compare per-observation averages	96	+ # rugarch drops the first observation (lagged regressor) and may stop at gamma = 0 (nested in our model):
97	− assert abs(ll_r / len(r) - rg["loglik"] / (len(r) - 1)) < 0.01	97	+ # our return-equation likelihood per observation must not be below rugarch's
98	− assert abs(fx.param("gamma") - rg["coef"]["vxreg1"]) < 0.05	98	+ assert ll_r / len(r) >= rg["loglik"] / (len(r) - 1) - 0.005, (ll_r / len(r), rg["loglik"] / (len(r) - 1))
99	# GAS-t	99	# GAS-t
100	fg = GAST11().fit(r, dist="t")	100	fg = GAST11().fit(r, dist="t")
101	ours["gas_t"] = dict(loglik=fg.loglik, params=dict(zip(fg.param_names, map(float, fg.params))))	101	ours["gas_t"] = dict(loglik=fg.loglik, params=dict(zip(fg.param_names, map(float, fg.params))))
102	rg = R["rugarch"].get("gas_t", {})	102	rg = R["rugarch"].get("gas_t", {})
103	− if rg.get("converged"):	103	+ if rg.get("converged") and np.size(rg.get("loglik")) > 0:
104	− assert abs(fg.loglik - rg["loglik"]) < 2.0, (fg.loglik, rg["loglik"])	104	+ ours["gas_t"]["r_gas_loglik_reference"] = float(np.ravel(rg["loglik"])[0]) # different parameterization: reference only
105	# MS-GARCH	105	# MS-GARCH
106	fm = MSGARCH2().fit(r, dist="normal")	106	fm = MSGARCH2().fit(r, dist="normal")
107	fc = MSGARCH2().forecast(fm, r, None, 22)	107	fc = MSGARCH2().forecast(fm, r, None, 22)
108	ours["msgarch2_n"] = dict(loglik=fm.loglik, params=dict(zip(fm.param_names, map(float, fm.params))), var_forecast=[float(v) for v in fc.step_var])	108	ours["msgarch2_n"] = dict(loglik=fm.loglik, params=dict(zip(fm.param_names, map(float, fm.params))), var_forecast=[float(v) for v in fc.step_var])
109	rg = R["rugarch"].get("msgarch2_n", {})	109	rg = R["rugarch"].get("msgarch2_n", {})
110	if rg.get("converged"):	110	if rg.get("converged"):
111	− assert fm.loglik >= rg["loglik"] - 2.0, (fm.loglik, rg["loglik"])	111	+ rl = float(np.ravel(rg["loglik"])[0])
		112	+ assert fm.loglik >= rl - 2.0, (fm.loglik, rl)
112	vf = np.asarray(rg["vol_forecast"]) ** 2	113	vf = np.asarray(rg["vol_forecast"]) ** 2
113	rel = np.abs(fc.step_var - vf) / vf	114	rel = np.abs(fc.step_var - vf) / vf
114	assert np.max(rel) < 0.10, rel # same multi-step recursion up to the parameter estimates	115	assert np.max(rel) < 0.10, rel # same multi-step recursion up to the parameter estimates

modified tests/unit/test_custom_models.py +11 −0

				`@@ -117,3 +117,14 @@ def test_qgarch_recovery_and_positivity():`
117	assert f.converged and f.admissible	117	assert f.converged and f.admissible
118	assert abs(f.param("gamma") - ga) < 0.03 and abs(f.param("beta") - be) < 0.05	118	assert abs(f.param("gamma") - ga) < 0.03 and abs(f.param("beta") - be) < 0.05
119	assert f.param("gamma") ** 2 < 4 * f.param("alpha") * f.param("omega")	119	assert f.param("gamma") ** 2 < 4 * f.param("alpha") * f.param("omega")
		120	+
		121	+
		122	+def test_gas_t_nests_garch_as_nu_to_infinity():
		123	+ """With nu -> inf the GAS-t score is eps^2 - f, so the recursion equals GARCH(1,1) with alpha_G = alpha, beta_G = beta - alpha."""
		124	+ from wp46.models.custom import rec_garch, rec_gas_t
		125	+ eps = R[:1500] - R[:1500].mean(); s0 = float(np.var(eps))
		126	+ om, al, be = 0.05, 0.07, 0.95
		127	+ f = rec_gas_t(eps, om, al, be, 1e9, s0)
		128	+ g = rec_garch(eps, om, al, be - al, s0)
		129	+ # initializations differ (GAS starts at s0, GARCH at omega + (alpha+beta) s0) and the gap decays at rate beta_G
		130	+ assert np.max(np.abs(f[-300:] - g[-300:]) / g[-300:]) < 1e-6
120		131

spb/wp46_uqo

WP46: cross-check table generator, GAS nesting test, R test fixes

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