R Under development (unstable) (2023-10-14 r85331 ucrt) -- "Unsuffered Consequences" Copyright (C) 2023 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > #### doRUnit.R --- Run RUnit tests > ####------------------------------------------------------------------------ > > ### Origianlly follows Gregor Gojanc's example in CRAN package 'gdata' > ### and the corresponding section in the R Wiki: > ### http://wiki.r-project.org/rwiki/doku.php?id=developers:runit > > ### MM: Vastly changed: This should also be "runnable" for *installed* > ## package which has no ./tests/ > ## ----> put the bulk of the code e.g. in ../inst/unitTests/runTests.R : > > if(require("RUnit", quietly = TRUE)) { + + ## --- Setup --- + + wd <- getwd() + pkg <- sub("\\.Rcheck$", '', basename(dirname(wd))) + + library(package=pkg, character.only = TRUE) + + path <- system.file("unitTests", package = pkg) + + stopifnot(file.exists(path), file.info(path.expand(path))$isdir) + + source(file.path(path, "runTests.R"), echo = TRUE) + } NOTE: Packages 'fBasics', 'timeDate', and 'timeSeries' are no longer attached to the search() path when 'fGarch' is attached. If needed attach them yourself in your R script by e.g., require("timeSeries") > pkg <- "fGarch" > if (require("RUnit", quietly = TRUE)) { + library(package = pkg, character.only = TRUE) + if (!(exists("path") && file.exists(path))) + .... [TRUNCATED] Executing test function test.bugfix_6061 ... done successfully. Executing test function test.formula.methods.multivariate ... GMT garch 2023-02-07 0.1840293 2023-02-08 -0.2523674 2023-02-09 0.1826768 2023-02-10 -0.2956628 2023-02-11 -0.1164576 2023-02-12 -0.2180087 GMT GARCH11 1970-01-01 0.1840293 1970-01-02 -0.2523674 1970-01-03 0.1826768 1970-01-04 -0.2956628 1970-01-05 -0.1164576 1970-01-06 -0.2180087 GMT GARCH11 R 2023-02-07 0.1840293 -1.3203348 2023-02-08 -0.2523674 0.2611401 2023-02-09 0.1826768 -0.9396917 2023-02-10 -0.2956628 0.4259576 2023-02-11 -0.1164576 0.4174039 2023-02-12 -0.2180087 0.9036160 GMT GARCH11 R 1970-01-01 0.1840293 -1.3203348 1970-01-02 -0.2523674 0.2611401 1970-01-03 0.1826768 -0.9396917 1970-01-04 -0.2956628 0.4259576 1970-01-05 -0.1164576 0.4174039 1970-01-06 -0.2180087 0.9036160 GARCH11 R [1,] 0.1840293 -1.3203348 [2,] -0.2523674 0.2611401 [3,] 0.1826768 -0.9396917 [4,] -0.2956628 0.4259576 [5,] -0.1164576 0.4174039 [6,] -0.2180087 0.9036160 GARCH11 ~ garch(1, 1) attr(,"data") [1] "data = X.mat" 100 * GARCH11 ~ garch(1, 1) attr(,"data") [1] "data = X.mat" GARCH11 ~ garch(1, 1) attr(,"data") [1] "data = X.tS" 100 * GARCH11 ~ garch(1, 1) attr(,"data") [1] "data = X.tS" GARCH11 ~ garch(1, 1) attr(,"data") [1] "data = X.mts" 100 * GARCH11 + R/100 ~ garch(1, 1) attr(,"data") [1] "data = X.mts" done successfully. Executing test function test.formula.methods.spread ... Loading required package: timeSeries Loading required package: timeDate GMT SBI SPI SII LMI MPI 2005-11-01 -0.000612745 0.008414595 -0.003190926 -0.001108882 0.001548062 2005-11-02 -0.002762009 0.002519342 -0.004117638 -0.001175939 0.000342876 2005-11-03 -0.001153092 0.012707292 -0.005209409 -0.000992456 0.010502959 2005-11-04 -0.003235750 -0.000702757 -0.001127165 -0.001198528 0.011679558 2005-11-07 0.001310970 0.006205226 -0.001795839 0.000360366 0.002709618 2005-11-08 0.000539312 0.000329260 0.002103374 0.002327040 0.000346843 ALT LPP25 LPP40 LPP60 2005-11-01 -0.002572971 -0.000130008 0.000199980 0.000809672 2005-11-02 -0.001141604 -0.001561421 -0.001120404 -0.000469730 2005-11-03 0.005007442 0.001541418 0.003317548 0.005731589 2005-11-04 0.009482677 0.000439969 0.002421248 0.004838735 2005-11-07 0.004723952 0.001638182 0.002246611 0.003012363 2005-11-08 0.000853619 0.001087309 0.000962708 0.000828043 SBI SPI SII LMI MPI 2005-11-01 -0.000612745 0.008414595 -0.003190926 -0.001108882 0.001548062 2005-11-02 -0.002762009 0.002519342 -0.004117638 -0.001175939 0.000342876 2005-11-03 -0.001153092 0.012707292 -0.005209409 -0.000992456 0.010502959 2005-11-04 -0.003235750 -0.000702757 -0.001127165 -0.001198528 0.011679558 2005-11-07 0.001310970 0.006205226 -0.001795839 0.000360366 0.002709618 2005-11-08 0.000539312 0.000329260 0.002103374 0.002327040 0.000346843 ALT LPP25 LPP40 LPP60 2005-11-01 -0.002572971 -0.000130008 0.000199980 0.000809672 2005-11-02 -0.001141604 -0.001561421 -0.001120404 -0.000469730 2005-11-03 0.005007442 0.001541418 0.003317548 0.005731589 2005-11-04 0.009482677 0.000439969 0.002421248 0.004838735 2005-11-07 0.004723952 0.001638182 0.002246611 0.003012363 2005-11-08 0.000853619 0.001087309 0.000962708 0.000828043 SBI SPI SII LMI MPI [1,] -0.000612745 0.008414595 -0.003190926 -0.001108882 0.001548062 [2,] -0.002762009 0.002519342 -0.004117638 -0.001175939 0.000342876 [3,] -0.001153092 0.012707292 -0.005209409 -0.000992456 0.010502959 [4,] -0.003235750 -0.000702757 -0.001127165 -0.001198528 0.011679558 [5,] 0.001310970 0.006205226 -0.001795839 0.000360366 0.002709618 [6,] 0.000539312 0.000329260 0.002103374 0.002327040 0.000346843 ALT LPP25 LPP40 LPP60 [1,] -0.002572971 -0.000130008 0.000199980 0.000809672 [2,] -0.001141604 -0.001561421 -0.001120404 -0.000469730 [3,] 0.005007442 0.001541418 0.003317548 0.005731589 [4,] 0.009482677 0.000439969 0.002421248 0.004838735 [5,] 0.004723952 0.001638182 0.002246611 0.003012363 [6,] 0.000853619 0.001087309 0.000962708 0.000828043 100 * (SPI - SBI) ~ garch(1, 1) attr(,"data") [1] "data = X.tS" Open ~ garch(1, 1) attr(,"data") [1] "data = returns(X.tS)" 100 * (High - Low) ~ garch(1, 1) attr(,"data") [1] "data = returns(X.tS)" done successfully. Executing test function test.formula.methods.univariate ... GMT garch 2023-02-07 0.1840293 2023-02-08 -0.2523674 2023-02-09 0.1826768 2023-02-10 -0.2956628 2023-02-11 -0.1164576 2023-02-12 -0.2180087 GMT GARCH11 1970-01-01 0.1840293 1970-01-02 -0.2523674 1970-01-03 0.1826768 1970-01-04 -0.2956628 1970-01-05 -0.1164576 1970-01-06 -0.2180087 [1] 0.1840293 -0.2523674 0.1826768 -0.2956628 -0.1164576 -0.2180087 data ~ garch(1, 1) attr(,"data") [1] "data = x.vec" data ~ garch(1, 1) attr(,"data") [1] "data = x.vec" data ~ garch(1, 1) attr(,"data") [1] "data = x.tS" data ~ garch(1, 1) attr(,"data") [1] "data = x.tS" data ~ garch(1, 1) attr(,"data") [1] "data = x.ts" data ~ garch(1, 1) attr(,"data") [1] "data = x.ts" done successfully. Executing test function test.garch.methods.show ... Title: GARCH Modelling Call: garchFit(formula = ~garch(1, 1), data = x, trace = FALSE) Mean and Variance Equation: data ~ garch(1, 1) [data = x] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 3.4446e-04 1.3988e-06 1.2088e-01 7.4609e-01 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu 3.445e-04 1.967e-04 1.751 0.079889 . omega 1.399e-06 1.538e-06 0.909 0.363113 alpha1 1.209e-01 8.083e-02 1.495 0.134807 beta1 7.461e-01 2.039e-01 3.658 0.000254 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: 1079.539 normalized: 4.318158 Description: Sun Oct 15 13:31:38 2023 by user: CRAN Title: GARCH Modelling Call: garchFit(formula = ~garch(1, 1), data = x, trace = FALSE) Mean and Variance Equation: data ~ garch(1, 1) [data = x] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 3.4446e-04 1.3988e-06 1.2088e-01 7.4609e-01 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu 3.445e-04 1.967e-04 1.751 0.079889 . omega 1.399e-06 1.538e-06 0.909 0.363113 alpha1 1.209e-01 8.083e-02 1.495 0.134807 beta1 7.461e-01 2.039e-01 3.658 0.000254 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: 1079.539 normalized: 4.318158 Description: Sun Oct 15 13:31:38 2023 by user: CRAN Title: GARCH Modelling Call: garchFit(formula = ~garch(1, 1), data = x, trace = FALSE) Mean and Variance Equation: data ~ garch(1, 1) [data = x] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 3.4446e-04 1.3988e-06 1.2088e-01 7.4609e-01 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu 3.445e-04 1.967e-04 1.751 0.079889 . omega 1.399e-06 1.538e-06 0.909 0.363113 alpha1 1.209e-01 8.083e-02 1.495 0.134807 beta1 7.461e-01 2.039e-01 3.658 0.000254 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: 1079.539 normalized: 4.318158 Description: Sun Oct 15 13:31:38 2023 by user: CRAN Standardised Residuals Tests: Statistic p-Value Jarque-Bera Test R Chi^2 3.0640530 0.2160973 Shapiro-Wilk Test R W 0.9932282 0.3162761 Ljung-Box Test R Q(10) 12.7535572 0.2377790 Ljung-Box Test R Q(15) 19.3366477 0.1988820 Ljung-Box Test R Q(20) 24.2412489 0.2320127 Ljung-Box Test R^2 Q(10) 4.2957406 0.9330210 Ljung-Box Test R^2 Q(15) 6.2620365 0.9750020 Ljung-Box Test R^2 Q(20) 10.3789117 0.9607694 LM Arch Test R TR^2 4.5776065 0.9706225 Information Criterion Statistics: AIC BIC SIC HQIC -8.604315 -8.547972 -8.604817 -8.581639 done successfully. Executing test function test.garchInputSeries ... Formula: ~ garch(1, 1) Model: omega: 1e-06 alpha: 0.1 beta: 0.8 Distribution: norm Presample: time z h y 1 0 -0.606304 1e-05 0 [1] 0.1840293 -0.2523674 0.1826768 -0.2956628 -0.1164576 -0.2180087 GMT GARCH11 1970-01-01 0.1840293 1970-01-02 -0.2523674 1970-01-03 0.1826768 1970-01-04 -0.2956628 1970-01-05 -0.1164576 1970-01-06 -0.2180087 [1] 0.1840293 -0.2523674 0.1826768 -0.2956628 -0.1164576 -0.2180087 GARCH11 R [1,] 0.1840293 0.7118340 [2,] -0.2523674 -0.4170536 [3,] 0.1826768 1.4003370 [4,] -0.2956628 -1.9527378 [5,] -0.1164576 0.7057794 [6,] -0.2180087 -0.4192943 GMT GARCH11 R 1970-01-01 0.1840293 0.7118340 1970-01-02 -0.2523674 -0.4170536 1970-01-03 0.1826768 1.4003370 1970-01-04 -0.2956628 -1.9527378 1970-01-05 -0.1164576 0.7057794 1970-01-06 -0.2180087 -0.4192943 GARCH11 R [1,] 0.1840293 0.7118340 [2,] -0.2523674 -0.4170536 [3,] 0.1826768 1.4003370 [4,] -0.2956628 -1.9527378 [5,] -0.1164576 0.7057794 [6,] -0.2180087 -0.4192943 done successfully. Executing test function test.garchFit.lbfgsb ... done successfully. Executing test function test.garchFit.lbfgsb.nm ... done successfully. Executing test function test.garchFit.nlmin.nm ... done successfully. Executing test function test.garchFit.nlminb ... mu omega alpha1 beta1 -1.478877e-04 1.131017e-06 8.538328e-02 8.134630e-01 done successfully. Executing test function test.garchFit.sqp ... done successfully. Executing test function test.aparch11 ... mu omega alpha1 gamma1 beta1 1.156311e-04 2.623872e-06 1.013249e-01 4.998621e-01 6.571474e-01 done successfully. Executing test function test.aparch11delta ... garch garch 4.656913e-06 mu omega alpha1 gamma1 beta1 delta 6.439639e-05 3.274349e-02 1.138219e-01 6.824104e-01 6.397859e-01 3.412369e-01 done successfully. Executing test function test.ar1aparch21 ... done successfully. Executing test function test.garchFit.ged ... mu omega alpha1 beta1 shape 1.798431e-04 1.625963e-06 1.855061e-01 6.386305e-01 2.515326e+00 done successfully. Executing test function test.garchFit.sged ... mu omega alpha1 beta1 skew shape 4.145514e-04 6.448695e-06 1.133708e-01 8.219373e-01 9.935170e-01 5.123191e+00 done successfully. Executing test function test.garchFit.snorm ... mu omega alpha1 beta1 skew 2.705584e-03 7.008233e-05 8.198866e-02 8.441942e-01 9.910038e-01 done successfully. Executing test function test.garchFit.snorm.fixed ... mu omega alpha1 beta1 2.791978e-03 7.687046e-05 7.985478e-02 8.395612e-01 done successfully. Executing test function test.garchFit.sstd ... mu omega alpha1 beta1 skew shape 1.152242e-04 2.383963e-06 2.545436e-01 6.684025e-01 9.429394e-01 2.765762e+00 done successfully. Executing test function test.garchFit.std ... mu omega alpha1 beta1 shape 2.086073e-04 2.365161e-06 2.609745e-01 6.620547e-01 2.789744e+00 done successfully. Executing test function test.garchFit.faked ... Formula: ~ garch(1, 1) Model: omega: 1e-06 alpha: 0.1 beta: 0.8 Distribution: norm Presample: time z h y 1 0 -0.606304 1e-05 0 [1] 0.1840293 -0.2523674 0.1826768 -0.2956628 -0.1164576 -0.2180087 GMT GARCH11 1970-01-01 0.1840293 1970-01-02 -0.2523674 1970-01-03 0.1826768 1970-01-04 -0.2956628 1970-01-05 -0.1164576 1970-01-06 -0.2180087 [1] 0.1840293 -0.2523674 0.1826768 -0.2956628 -0.1164576 -0.2180087 Title: GARCH Modelling Call: garchFit(formula = ~garch(1, 1), data = x.tS, trace = FALSE) Mean and Variance Equation: data ~ garch(1, 1) [data = x.tS] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 0.018707 0.021379 0.106185 0.713555 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu 0.01871 0.02080 0.899 0.36849 omega 0.02138 0.01707 1.252 0.21052 alpha1 0.10619 0.06915 1.536 0.12466 beta1 0.71356 0.18549 3.847 0.00012 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: -84.64652 normalized: -0.3385861 Description: Sun Oct 15 13:31:39 2023 by user: CRAN Title: GARCH Modelling Call: garchFit(formula = ~garch(1, 1), data = as.vector(x.tS), trace = FALSE) Mean and Variance Equation: data ~ garch(1, 1) [data = as.vector(x.tS)] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 0.018707 0.021379 0.106185 0.713555 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu 0.01871 0.02080 0.899 0.36849 omega 0.02138 0.01707 1.252 0.21052 alpha1 0.10619 0.06915 1.536 0.12466 beta1 0.71356 0.18549 3.847 0.00012 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: -84.64652 normalized: -0.3385861 Description: Sun Oct 15 13:31:39 2023 by user: CRAN Title: GARCH Modelling Call: garchFit(formula = ~garch(1, 1), data = a * as.vector(0 + b * x.tS), trace = FALSE) Mean and Variance Equation: data ~ garch(1, 1) [data = a * as.vector(0 + b * x.tS)] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 0.074829 0.342059 0.106185 0.713555 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu 0.07483 0.08321 0.899 0.36849 omega 0.34206 0.27318 1.252 0.21052 alpha1 0.10619 0.06915 1.536 0.12466 beta1 0.71356 0.18549 3.847 0.00012 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: -431.2201 normalized: -1.72488 Description: Sun Oct 15 13:31:39 2023 by user: CRAN Title: GARCH Modelling Call: garchFit(formula = any ~ garch(1, 1), data = x.vec, trace = FALSE) Mean and Variance Equation: data ~ garch(1, 1) [data = x.vec] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 0.018707 0.021379 0.106185 0.713555 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu 0.01871 0.02080 0.899 0.36849 omega 0.02138 0.01707 1.252 0.21052 alpha1 0.10619 0.06915 1.536 0.12466 beta1 0.71356 0.18549 3.847 0.00012 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: -84.64652 normalized: -0.3385861 Description: Sun Oct 15 13:31:39 2023 by user: CRAN done successfully. Executing test function test.garchFit.mult.faked ... Formula: ~ garch(1, 1) Model: omega: 1e-06 alpha: 0.1 beta: 0.8 Distribution: norm Presample: time z h y 1 0 -0.606304 1e-05 0 [1] 0.1840293 -0.2523674 0.1826768 -0.2956628 -0.1164576 -0.2180087 GMT GARCH11 1970-01-01 0.1840293 1970-01-02 -0.2523674 1970-01-03 0.1826768 1970-01-04 -0.2956628 1970-01-05 -0.1164576 1970-01-06 -0.2180087 [1] 0.1840293 -0.2523674 0.1826768 -0.2956628 -0.1164576 -0.2180087 GARCH11 R [1,] 0.1840293 -0.0013203348 [2,] -0.2523674 0.0002611401 [3,] 0.1826768 -0.0009396917 [4,] -0.2956628 0.0004259576 [5,] -0.1164576 0.0004174039 [6,] -0.2180087 0.0009036160 GMT GARCH11 R 1970-01-01 0.1840293 -0.0013203348 1970-01-02 -0.2523674 0.0002611401 1970-01-03 0.1826768 -0.0009396917 1970-01-04 -0.2956628 0.0004259576 1970-01-05 -0.1164576 0.0004174039 1970-01-06 -0.2180087 0.0009036160 GARCH11 R [1,] 0.1840293 -0.0013203348 [2,] -0.2523674 0.0002611401 [3,] 0.1826768 -0.0009396917 [4,] -0.2956628 0.0004259576 [5,] -0.1164576 0.0004174039 [6,] -0.2180087 0.0009036160 Title: GARCH Modelling Call: garchFit(formula = GARCH11 ~ garch(1, 1), data = X.tS, trace = FALSE) Mean and Variance Equation: GARCH11 ~ garch(1, 1) [data = X.tS] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 0.018707 0.021379 0.106185 0.713555 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu 0.01871 0.02080 0.899 0.36849 omega 0.02138 0.01707 1.252 0.21052 alpha1 0.10619 0.06915 1.536 0.12466 beta1 0.71356 0.18549 3.847 0.00012 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: -84.64652 normalized: -0.3385861 Description: Sun Oct 15 13:31:39 2023 by user: CRAN Title: GARCH Modelling Call: garchFit(formula = GARCH11 ~ garch(1, 1), data = as.matrix(X.tS), trace = FALSE) Mean and Variance Equation: GARCH11 ~ garch(1, 1) [data = as.matrix(X.tS)] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 0.018707 0.021379 0.106185 0.713555 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu 0.01871 0.02080 0.899 0.36849 omega 0.02138 0.01707 1.252 0.21052 alpha1 0.10619 0.06915 1.536 0.12466 beta1 0.71356 0.18549 3.847 0.00012 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: -84.64652 normalized: -0.3385861 Description: Sun Oct 15 13:31:39 2023 by user: CRAN Title: GARCH Modelling Call: garchFit(formula = GARCH11 ~ garch(1, 1), data = a * as.matrix(0 + b * X.tS), trace = FALSE) Mean and Variance Equation: GARCH11 ~ garch(1, 1) [data = a * as.matrix(0 + b * X.tS)] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 0.074829 0.342059 0.106185 0.713555 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu 0.07483 0.08321 0.899 0.36849 omega 0.34206 0.27318 1.252 0.21052 alpha1 0.10619 0.06915 1.536 0.12466 beta1 0.71356 0.18549 3.847 0.00012 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: -431.2201 normalized: -1.72488 Description: Sun Oct 15 13:31:40 2023 by user: CRAN Title: GARCH Modelling Call: garchFit(formula = GARCH11 ~ garch(1, 1), data = a * as.matrix(0 + b * X.tS), trace = FALSE) Mean and Variance Equation: GARCH11 ~ garch(1, 1) [data = a * as.matrix(0 + b * X.tS)] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 0.074829 0.342059 0.106185 0.713555 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu 0.07483 0.08321 0.899 0.36849 omega 0.34206 0.27318 1.252 0.21052 alpha1 0.10619 0.06915 1.536 0.12466 beta1 0.71356 0.18549 3.847 0.00012 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: -431.2201 normalized: -1.72488 Description: Sun Oct 15 13:31:40 2023 by user: CRAN done successfully. Executing test function test.garchFit.mult.lhs.faked ... Formula: ~ garch(1, 1) Model: omega: 1e-06 alpha: 0.1 beta: 0.8 Distribution: norm Presample: time z h y 1 0 -0.606304 1e-05 0 [1] 0.1840293 -0.2523674 0.1826768 -0.2956628 -0.1164576 -0.2180087 GMT GARCH11 1970-01-01 0.1840293 1970-01-02 -0.2523674 1970-01-03 0.1826768 1970-01-04 -0.2956628 1970-01-05 -0.1164576 1970-01-06 -0.2180087 [1] 0.1840293 -0.2523674 0.1826768 -0.2956628 -0.1164576 -0.2180087 GARCH11 R [1,] 0.1840293 -0.0013203348 [2,] -0.2523674 0.0002611401 [3,] 0.1826768 -0.0009396917 [4,] -0.2956628 0.0004259576 [5,] -0.1164576 0.0004174039 [6,] -0.2180087 0.0009036160 GMT GARCH11 R 1970-01-01 0.1840293 -0.0013203348 1970-01-02 -0.2523674 0.0002611401 1970-01-03 0.1826768 -0.0009396917 1970-01-04 -0.2956628 0.0004259576 1970-01-05 -0.1164576 0.0004174039 1970-01-06 -0.2180087 0.0009036160 GARCH11 R [1,] 0.1840293 -0.0013203348 [2,] -0.2523674 0.0002611401 [3,] 0.1826768 -0.0009396917 [4,] -0.2956628 0.0004259576 [5,] -0.1164576 0.0004174039 [6,] -0.2180087 0.0009036160 Title: GARCH Modelling Call: garchFit(formula = GARCH11 + R ~ garch(1, 1), data = X.tS, trace = FALSE) Mean and Variance Equation: GARCH11 + R ~ garch(1, 1) [data = X.tS] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 0.018571 0.021267 0.105564 0.715064 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu 0.01857 0.02081 0.893 0.372104 omega 0.02127 0.01700 1.251 0.210995 alpha1 0.10556 0.06882 1.534 0.125041 beta1 0.71506 0.18470 3.872 0.000108 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: -84.62666 normalized: -0.3385066 Description: Sun Oct 15 13:31:40 2023 by user: CRAN Title: GARCH Modelling Call: garchFit(formula = GARCH11 + R ~ garch(1, 1), data = as.matrix(X.tS), trace = FALSE) Mean and Variance Equation: GARCH11 + R ~ garch(1, 1) [data = as.matrix(X.tS)] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 0.018571 0.021267 0.105564 0.715064 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu 0.01857 0.02081 0.893 0.372104 omega 0.02127 0.01700 1.251 0.210995 alpha1 0.10556 0.06882 1.534 0.125041 beta1 0.71506 0.18470 3.872 0.000108 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: -84.62666 normalized: -0.3385066 Description: Sun Oct 15 13:31:40 2023 by user: CRAN done successfully. Executing test function test.garchFit.ar1garch11 ... mu ar1 omega alpha1 beta1 1.640954e-04 -1.476143e-01 1.300292e-06 2.460234e-01 6.526160e-01 done successfully. Executing test function test.garchFit.garch11 ... mu omega alpha1 beta1 1.870726e-04 2.137866e-06 1.061852e-01 7.135555e-01 done successfully. Executing test function test.garchFit.garch21 ... mu omega alpha1 alpha2 beta1 1.329333e-04 1.599579e-06 1.026368e-01 2.119612e-01 5.613662e-01 done successfully. Executing test function test.garchFit.init ... mu omega alpha1 beta1 1.870726e-04 2.137866e-06 1.061852e-01 7.135555e-01 done successfully. Executing test function test.garchSim.aparch ... done successfully. Executing test function test.garchSim.arch ... done successfully. Executing test function test.garchSim.arma.arch ... done successfully. Executing test function test.garchSim.dist.arch ... done successfully. Executing test function test.garchSim.dist.garch ... done successfully. Executing test function test.garchSim.garch ... done successfully. Executing test function test.garchSolver.sp500dge ... Series Initialization: ARMA Model: arma Formula Mean: ~ arma(0, 1) GARCH Model: aparch Formula Variance: ~ aparch(1, 1) ARMA Order: 0 1 Max ARMA Order: 1 GARCH Order: 1 1 Max GARCH Order: 1 Maximum Order: 1 Conditional Dist: norm h.start: 2 llh.start: 1 Length of Series: 17055 Recursion Init: mci Series Scale: 1.150485 Parameter Initialization: Initial Parameters: $params Limits of Transformations: $U, $V Which Parameters are Fixed? $includes Parameter Matrix: U V params includes mu -0.15814399 0.158144 0.01581437 TRUE ma1 -0.99999999 1.000000 0.06841867 TRUE omega 0.00000100 100.000000 0.10000000 TRUE alpha1 0.00000001 1.000000 0.10000000 TRUE gamma1 -0.99999999 1.000000 0.10000000 TRUE beta1 0.00000001 1.000000 0.80000000 TRUE delta 0.00000000 2.000000 2.00000000 TRUE skew 0.10000000 10.000000 1.00000000 FALSE shape 1.00000000 10.000000 4.00000000 FALSE Index List of Parameters to be Optimized: mu ma1 omega alpha1 gamma1 beta1 delta 1 2 3 4 5 6 7 Persistence: 0.901 --- START OF TRACE --- Selected Algorithm: nlminb R coded nlminb Solver: 0: 20507.776: 0.0158144 0.0684187 0.100000 0.100000 0.100000 0.800000 2.00000 1: 20031.866: 0.0158144 0.0691747 0.0750556 0.0991969 0.100684 0.787546 1.99966 2: 19786.778: 0.0158147 0.0707866 0.0495773 0.110227 0.102319 0.785800 1.99965 3: 19729.670: 0.0158152 0.0728072 0.0553961 0.131239 0.104307 0.802748 2.00000 4: 19555.088: 0.0158158 0.0761767 0.0284064 0.136217 0.106893 0.799947 2.00000 5: 19417.033: 0.0158180 0.0871097 0.0284076 0.148563 0.114447 0.821162 2.00000 6: 19343.197: 0.0158200 0.0967461 0.0131556 0.143398 0.120706 0.840854 2.00000 7: 19297.860: 0.0158225 0.109240 0.0141223 0.132304 0.128907 0.861632 2.00000 8: 19256.427: 0.0158255 0.122354 0.00862955 0.116683 0.138742 0.876929 1.99853 9: 19234.443: 0.0158298 0.134239 0.00964847 0.101888 0.154965 0.888867 1.99504 10: 19225.125: 0.0158342 0.124911 0.00519671 0.0916416 0.171968 0.905106 1.99124 11: 19215.411: 0.0158416 0.126845 0.00787052 0.0906714 0.198946 0.901762 1.98594 12: 19213.439: 0.0158446 0.131051 0.00761455 0.0897496 0.208486 0.898960 1.98364 13: 19209.144: 0.0158481 0.138306 0.00816025 0.0924186 0.214828 0.897870 1.97903 14: 19202.172: 0.0158647 0.137543 0.00645501 0.0877518 0.234089 0.906052 1.94951 15: 19201.919: 0.0158647 0.137560 0.00670248 0.0877190 0.234102 0.906085 1.94950 16: 19201.778: 0.0158647 0.137598 0.00665832 0.0875079 0.234129 0.905964 1.94948 17: 19201.680: 0.0158648 0.137626 0.00687089 0.0874070 0.234183 0.905985 1.94941 18: 19201.557: 0.0158649 0.137642 0.00676343 0.0872584 0.234274 0.905942 1.94927 19: 19201.470: 0.0158649 0.137674 0.00692288 0.0871350 0.234362 0.905969 1.94915 20: 19201.377: 0.0158650 0.137690 0.00683076 0.0869962 0.234465 0.905946 1.94899 21: 19201.302: 0.0158651 0.137714 0.00695219 0.0868952 0.234573 0.905996 1.94884 22: 19201.228: 0.0158652 0.137733 0.00687986 0.0867649 0.234685 0.905991 1.94867 23: 19201.161: 0.0158653 0.137755 0.00698013 0.0866764 0.234801 0.906049 1.94850 24: 19201.097: 0.0158654 0.137774 0.00691696 0.0865566 0.234918 0.906057 1.94832 25: 19201.036: 0.0158655 0.137795 0.00700195 0.0864763 0.235039 0.906121 1.94814 26: 19200.978: 0.0158656 0.137814 0.00694391 0.0863669 0.235160 0.906138 1.94796 27: 19200.922: 0.0158658 0.137835 0.00701755 0.0862933 0.235284 0.906204 1.94778 28: 19200.867: 0.0158659 0.137854 0.00696263 0.0861928 0.235409 0.906228 1.94760 29: 19200.814: 0.0158660 0.137875 0.00702793 0.0861253 0.235535 0.906296 1.94741 30: 19200.762: 0.0158661 0.137894 0.00697491 0.0860325 0.235662 0.906325 1.94722 31: 19200.712: 0.0158662 0.137915 0.00703411 0.0859705 0.235791 0.906394 1.94704 32: 19200.662: 0.0158663 0.137935 0.00698224 0.0858843 0.235920 0.906425 1.94685 33: 19200.613: 0.0158664 0.137955 0.00703700 0.0858271 0.236050 0.906494 1.94666 34: 19200.564: 0.0158666 0.137975 0.00698586 0.0857465 0.236181 0.906527 1.94647 35: 19200.516: 0.0158667 0.137995 0.00703737 0.0856937 0.236313 0.906596 1.94628 36: 19192.613: 0.0159192 0.135598 0.00611406 0.0704169 0.290039 0.922189 1.85805 37: 19188.499: 0.0159732 0.156879 0.00826789 0.0754006 0.349077 0.917203 1.77343 38: 19185.290: 0.0159732 0.156865 0.00756899 0.0752662 0.349073 0.916978 1.77342 39: 19185.033: 0.0159732 0.156762 0.00747217 0.0758231 0.349066 0.917453 1.77339 40: 19184.738: 0.0159736 0.156590 0.00718809 0.0758036 0.349179 0.917371 1.77273 41: 19184.606: 0.0159739 0.156414 0.00727292 0.0758808 0.349317 0.917423 1.77203 42: 19184.514: 0.0159743 0.156209 0.00713098 0.0760002 0.349421 0.917484 1.77135 43: 19184.418: 0.0159747 0.156048 0.00720407 0.0760625 0.349538 0.917525 1.77064 44: 19179.571: 0.0160367 0.133723 0.00782486 0.0793723 0.367498 0.916892 1.65669 45: 19175.829: 0.0160918 0.135259 0.00752135 0.0807995 0.345945 0.921353 1.54124 46: 19174.629: 0.0160918 0.135269 0.00715769 0.0806664 0.345945 0.921155 1.54123 47: 19174.590: 0.0160921 0.135315 0.00723324 0.0806773 0.346085 0.921131 1.54083 48: 19174.555: 0.0160924 0.135362 0.00721378 0.0806594 0.346224 0.921064 1.54043 49: 19174.523: 0.0160928 0.135407 0.00727802 0.0806744 0.346363 0.921045 1.54002 50: 19174.493: 0.0160931 0.135454 0.00726136 0.0806647 0.346502 0.920987 1.53962 51: 19174.465: 0.0160934 0.135499 0.00731598 0.0806820 0.346641 0.920969 1.53921 52: 19174.438: 0.0160937 0.135545 0.00730092 0.0806785 0.346779 0.920919 1.53881 53: 19174.413: 0.0160941 0.135590 0.00734816 0.0806978 0.346917 0.920902 1.53840 54: 19174.389: 0.0160944 0.135636 0.00733407 0.0806990 0.347054 0.920857 1.53799 55: 19174.365: 0.0160947 0.135681 0.00737571 0.0807200 0.347192 0.920842 1.53759 56: 19172.373: 0.0161748 0.142247 0.00806809 0.0842190 0.379824 0.919123 1.43905 57: 19172.338: 0.0163271 0.142363 0.00814819 0.0843100 0.376179 0.919683 1.42539 58: 19172.257: 0.0165836 0.142432 0.00798767 0.0842786 0.374456 0.919792 1.42020 59: 19172.204: 0.0168437 0.142447 0.00807683 0.0842383 0.375241 0.919617 1.42487 60: 19172.184: 0.0171115 0.142551 0.00820089 0.0839664 0.375411 0.919311 1.42733 61: 19172.142: 0.0173760 0.142683 0.00824949 0.0839649 0.374125 0.919453 1.42391 62: 19172.128: 0.0179190 0.142930 0.00821390 0.0839576 0.374728 0.919376 1.42903 63: 19172.116: 0.0184663 0.143829 0.00815562 0.0838616 0.373110 0.919537 1.42701 64: 19172.103: 0.0179200 0.144915 0.00816067 0.0837872 0.373904 0.919534 1.43021 65: 19172.103: 0.0178977 0.144708 0.00817652 0.0837895 0.374213 0.919530 1.42974 66: 19172.103: 0.0179010 0.144708 0.00817646 0.0837927 0.374178 0.919526 1.42977 Final Estimate of the Negative LLH: LLH: 21563.41 norm LLH: 1.264345 mu ma1 omega alpha1 gamma1 beta1 0.020594815 0.144708220 0.009991051 0.083792696 0.374177942 0.919526026 delta 1.429773768 R-optimhess Difference Approximated Hessian Matrix: mu ma1 omega alpha1 gamma1 beta1 mu -31981.811 -1142.1171 -203487.63 -96616.498 -2074.2154 -133087.92 ma1 -1142.117 -14635.1440 -20996.49 -4616.604 520.4828 -10912.43 omega -203487.633 -20996.4891 -7494572.84 -2824847.852 -12233.0794 -4229324.64 alpha1 -96616.498 -4616.6037 -2824847.85 -1598373.242 -23891.3955 -2091828.67 gamma1 -2074.215 520.4828 -12233.08 -23891.396 -2466.8680 -21345.47 beta1 -133087.916 -10912.4263 -4229324.64 -2091828.675 -21345.4739 -2923834.05 delta -3428.692 -59.6952 -107365.44 -54718.604 -958.0267 -73817.29 delta mu -3428.6922 ma1 -59.6952 omega -107365.4439 alpha1 -54718.6043 gamma1 -958.0267 beta1 -73817.2887 delta -2187.1247 attr(,"time") Time difference of 1.394107 secs --- END OF TRACE --- Time to Estimate Parameters: Time difference of 5.900747 secs done successfully. Executing test function test.garchSolver2.dem2gbp ... Series Initialization: ARMA Model: arma Formula Mean: ~ arma(0, 0) GARCH Model: garch Formula Variance: ~ garch(1, 1) ARMA Order: 0 0 Max ARMA Order: 0 GARCH Order: 1 1 Max GARCH Order: 1 Maximum Order: 1 Conditional Dist: norm h.start: 2 llh.start: 1 Length of Series: 1974 Recursion Init: mci Series Scale: 0.4702445 Parameter Initialization: Initial Parameters: $params Limits of Transformations: $U, $V Which Parameters are Fixed? $includes Parameter Matrix: U V params includes mu -0.34932441 0.3493244 -0.03493244 TRUE omega 0.00000100 100.0000000 0.10000000 TRUE alpha1 0.00000001 1.0000000 0.10000000 TRUE gamma1 -0.99999999 1.0000000 0.10000000 FALSE beta1 0.00000001 1.0000000 0.80000000 TRUE delta 0.00000000 2.0000000 2.00000000 FALSE skew 0.10000000 10.0000000 1.00000000 FALSE shape 1.00000000 10.0000000 4.00000000 FALSE Index List of Parameters to be Optimized: mu omega alpha1 beta1 1 2 3 5 Persistence: 0.9 --- START OF TRACE --- Selected Algorithm: nlminb R coded nlminb Solver: 0: 2622.6409: -0.0349324 0.100000 0.100000 0.800000 1: 2614.8796: -0.0349308 0.0787306 0.102589 0.788660 2: 2605.2292: -0.0349281 0.0782926 0.125313 0.797093 3: 2601.7318: -0.0349238 0.0568750 0.135482 0.792039 4: 2597.6895: -0.0349097 0.0539007 0.156083 0.804462 5: 2596.9036: -0.0348922 0.0473569 0.156813 0.802551 6: 2596.7683: -0.0347736 0.0521482 0.155781 0.799131 7: 2596.7303: -0.0346756 0.0487563 0.155754 0.804412 8: 2596.7269: -0.0346743 0.0486814 0.155476 0.804241 9: 2596.7243: -0.0346727 0.0489783 0.155350 0.804324 10: 2596.7220: -0.0346690 0.0488591 0.155070 0.804228 11: 2596.7197: -0.0346632 0.0490450 0.154936 0.804411 12: 2596.7179: -0.0346567 0.0489446 0.154674 0.804413 13: 2596.7162: -0.0346493 0.0490712 0.154542 0.804600 14: 2596.7147: -0.0346413 0.0489730 0.154323 0.804645 15: 2596.7133: -0.0346327 0.0490576 0.154201 0.804819 16: 2596.7120: -0.0346235 0.0489665 0.154020 0.804876 17: 2596.7108: -0.0346139 0.0490258 0.153919 0.805031 18: 2596.7097: -0.0346040 0.0489443 0.153771 0.805087 19: 2596.6538: -0.0334766 0.0456414 0.147037 0.814727 20: 2596.0506: -0.0141969 0.0488922 0.157482 0.804072 21: 2595.9988: -0.0136279 0.0495018 0.154705 0.803825 22: 2595.9977: -0.0135277 0.0490581 0.154436 0.804439 23: 2595.9962: -0.0132567 0.0487947 0.153483 0.805501 24: 2595.9960: -0.0131597 0.0486669 0.153136 0.805971 25: 2595.9960: -0.0131642 0.0486655 0.153134 0.805974 Final Estimate of the Negative LLH: LLH: 1106.608 norm LLH: 0.5605916 mu omega alpha1 beta1 -0.006190415 0.010761392 0.153133908 0.805973777 Central Difference Approximated Hessian Matrix: mu omega alpha1 beta1 mu -14021.12200 511.7978 276.4211 31.06426 omega 511.79784 -1458569.5033 -118904.0315 -197784.03663 alpha1 276.42108 -118904.0315 -18056.4475 -22142.80696 beta1 31.06426 -197784.0366 -22142.8070 -32043.34909 attr(,"time") Time difference of 0.026299 secs --- END OF TRACE --- Time to Estimate Parameters: Time difference of 0.1113541 secs Series Initialization: ARMA Model: arma Formula Mean: ~ arma(0, 0) GARCH Model: garch Formula Variance: ~ garch(1, 1) ARMA Order: 0 0 Max ARMA Order: 0 GARCH Order: 1 1 Max GARCH Order: 1 Maximum Order: 1 Conditional Dist: norm h.start: 2 llh.start: 1 Length of Series: 1974 Recursion Init: mci Series Scale: 0.4702445 Parameter Initialization: Initial Parameters: $params Limits of Transformations: $U, $V Which Parameters are Fixed? $includes Parameter Matrix: U V params includes mu -0.34932441 0.3493244 -0.03493244 TRUE omega 0.00000100 100.0000000 0.10000000 TRUE alpha1 0.00000001 1.0000000 0.10000000 TRUE gamma1 -0.99999999 1.0000000 0.10000000 FALSE beta1 0.00000001 1.0000000 0.80000000 TRUE delta 0.00000000 2.0000000 2.00000000 FALSE skew 0.10000000 10.0000000 1.00000000 FALSE shape 1.00000000 10.0000000 4.00000000 FALSE Index List of Parameters to be Optimized: mu omega alpha1 beta1 1 2 3 5 Persistence: 0.9 --- START OF TRACE --- Selected Algorithm: lbfgsb R coded optim[L-BFGS-B] Solver: iter 10 value 2595.996094 final value 2595.996058 stopped after 12 iterations Final Estimate of the Negative LLH: LLH: 1106.608 norm LLH: 0.5605917 mu omega alpha1 beta1 -0.006186002 0.010787960 0.153338066 0.805667157 R-optimhess Difference Approximated Hessian Matrix: mu omega alpha1 beta1 mu -14019.22548 512.4193 276.0531 30.69046 omega 512.41932 -1454339.3454 -118510.1155 -197166.80029 alpha1 276.05310 -118510.1155 -17996.6193 -22068.95551 beta1 30.69046 -197166.8003 -22068.9555 -31947.20824 attr(,"time") Time difference of 0.02577996 secs --- END OF TRACE --- Time to Estimate Parameters: Time difference of 0.380466 secs Series Initialization: ARMA Model: arma Formula Mean: ~ arma(0, 0) GARCH Model: garch Formula Variance: ~ garch(1, 1) ARMA Order: 0 0 Max ARMA Order: 0 GARCH Order: 1 1 Max GARCH Order: 1 Maximum Order: 1 Conditional Dist: norm h.start: 2 llh.start: 1 Length of Series: 1974 Recursion Init: mci Series Scale: 0.4702445 Parameter Initialization: Initial Parameters: $params Limits of Transformations: $U, $V Which Parameters are Fixed? $includes Parameter Matrix: U V params includes mu -0.34932441 0.3493244 -0.03493244 TRUE omega 0.00000100 100.0000000 0.10000000 TRUE alpha1 0.00000001 1.0000000 0.10000000 TRUE gamma1 -0.99999999 1.0000000 0.10000000 FALSE beta1 0.00000001 1.0000000 0.80000000 TRUE delta 0.00000000 2.0000000 2.00000000 FALSE skew 0.10000000 10.0000000 1.00000000 FALSE shape 1.00000000 10.0000000 4.00000000 FALSE Index List of Parameters to be Optimized: mu omega alpha1 beta1 1 2 3 5 Persistence: 0.9 --- START OF TRACE --- Selected Algorithm: lbfgsb+nm R coded optim[L-BFGS-B] Solver: iter 10 value 2595.996094 final value 2595.996058 stopped after 12 iterations R coded Nelder-Mead Hybrid Solver: Nelder-Mead direct search function minimizer function value for initial parameters = 1.000000 Scaled convergence tolerance is 1e-11 Stepsize computed as 0.100000 BUILD 5 1.053671 1.000000 LO-REDUCTION 7 1.006989 1.000000 HI-REDUCTION 9 1.005947 1.000000 LO-REDUCTION 11 1.001830 1.000000 HI-REDUCTION 13 1.000907 1.000000 HI-REDUCTION 15 1.000742 1.000000 LO-REDUCTION 17 1.000304 1.000000 HI-REDUCTION 19 1.000227 1.000000 HI-REDUCTION 21 1.000227 1.000000 HI-REDUCTION 23 1.000143 1.000000 LO-REDUCTION 25 1.000057 1.000000 HI-REDUCTION 27 1.000055 1.000000 LO-REDUCTION 29 1.000045 1.000000 LO-REDUCTION 31 1.000028 1.000000 HI-REDUCTION 33 1.000012 1.000000 HI-REDUCTION 35 1.000008 1.000000 HI-REDUCTION 37 1.000008 1.000000 HI-REDUCTION 39 1.000007 1.000000 HI-REDUCTION 41 1.000004 1.000000 LO-REDUCTION 43 1.000003 1.000000 LO-REDUCTION 45 1.000003 1.000000 LO-REDUCTION 47 1.000003 1.000000 LO-REDUCTION 49 1.000002 1.000000 LO-REDUCTION 51 1.000001 1.000000 LO-REDUCTION 53 1.000001 1.000000 LO-REDUCTION 55 1.000000 1.000000 LO-REDUCTION 57 1.000000 1.000000 LO-REDUCTION 59 1.000000 1.000000 LO-REDUCTION 61 1.000000 1.000000 LO-REDUCTION 63 1.000000 1.000000 LO-REDUCTION 65 1.000000 1.000000 LO-REDUCTION 67 1.000000 1.000000 HI-REDUCTION 69 1.000000 1.000000 HI-REDUCTION 71 1.000000 1.000000 HI-REDUCTION 73 1.000000 1.000000 HI-REDUCTION 75 1.000000 1.000000 HI-REDUCTION 77 1.000000 1.000000 HI-REDUCTION 79 1.000000 1.000000 HI-REDUCTION 81 1.000000 1.000000 LO-REDUCTION 83 1.000000 1.000000 LO-REDUCTION 85 1.000000 1.000000 REFLECTION 87 1.000000 1.000000 LO-REDUCTION 89 1.000000 1.000000 HI-REDUCTION 91 1.000000 1.000000 HI-REDUCTION 93 1.000000 1.000000 REFLECTION 95 1.000000 1.000000 REFLECTION 97 1.000000 1.000000 REFLECTION 99 1.000000 1.000000 REFLECTION 101 1.000000 1.000000 LO-REDUCTION 103 1.000000 1.000000 HI-REDUCTION 105 1.000000 1.000000 REFLECTION 107 1.000000 1.000000 LO-REDUCTION 109 1.000000 1.000000 REFLECTION 111 1.000000 1.000000 LO-REDUCTION 113 1.000000 1.000000 LO-REDUCTION 115 1.000000 1.000000 LO-REDUCTION 117 1.000000 1.000000 HI-REDUCTION 119 1.000000 1.000000 REFLECTION 121 1.000000 1.000000 HI-REDUCTION 123 1.000000 1.000000 HI-REDUCTION 125 1.000000 1.000000 LO-REDUCTION 127 1.000000 1.000000 REFLECTION 129 1.000000 1.000000 HI-REDUCTION 131 1.000000 1.000000 Exiting from Nelder Mead minimizer 133 function evaluations used Final Estimate of the Negative LLH: LLH: 1106.608 norm LLH: 0.5605916 mu omega alpha1 beta1 -0.006190636 0.010761657 0.153132905 0.805973202 Central Difference Approximated Hessian Matrix: mu omega alpha1 beta1 mu -14018.04245 511.5963 276.5089 31.08139 omega 511.59626 -1458516.5611 -118901.7377 -197780.38085 alpha1 276.50894 -118901.7377 -18056.4066 -22142.63021 beta1 31.08139 -197780.3808 -22142.6302 -32043.05419 attr(,"time") Time difference of 0.02616906 secs --- END OF TRACE --- Time to Estimate Parameters: Time difference of 0.3304999 secs done successfully. Executing test function test.garchSpec ... done successfully. Executing test function test.plot.methods1 ... Title: GARCH Modelling Call: garchFit(formula = ~garch(1, 1), data = dem2gbp, trace = FALSE) Mean and Variance Equation: data ~ garch(1, 1) [data = dem2gbp] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 -0.0061904 0.0107614 0.1531339 0.8059738 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu -0.006190 0.008462 -0.732 0.464440 omega 0.010761 0.002838 3.793 0.000149 *** alpha1 0.153134 0.026422 5.796 6.8e-09 *** beta1 0.805974 0.033381 24.144 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: -1106.608 normalized: -0.5605916 Description: Sun Oct 15 13:31:47 2023 by user: CRAN done successfully. Executing test function test.plot.methods2 ... Title: GARCH Modelling Call: garchFit(formula = ~garch(1, 1), data = dem2gbp, trace = FALSE) Mean and Variance Equation: data ~ garch(1, 1) [data = dem2gbp] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 -0.0061904 0.0107614 0.1531339 0.8059738 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu -0.006190 0.008462 -0.732 0.464440 omega 0.010761 0.002838 3.793 0.000149 *** alpha1 0.153134 0.026422 5.796 6.8e-09 *** beta1 0.805974 0.033381 24.144 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: -1106.608 normalized: -0.5605916 Description: Sun Oct 15 13:31:47 2023 by user: CRAN done successfully. Executing test function test.plot.methods3 ... Title: GARCH Modelling Call: garchFit(formula = ~garch(1, 1), data = dem2gbp, trace = FALSE) Mean and Variance Equation: data ~ garch(1, 1) [data = dem2gbp] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 -0.0061904 0.0107614 0.1531339 0.8059738 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu -0.006190 0.008462 -0.732 0.464440 omega 0.010761 0.002838 3.793 0.000149 *** alpha1 0.153134 0.026422 5.796 6.8e-09 *** beta1 0.805974 0.033381 24.144 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: -1106.608 normalized: -0.5605916 Description: Sun Oct 15 13:31:47 2023 by user: CRAN done successfully. Executing test function test.plot.methods4 ... Title: GARCH Modelling Call: garchFit(formula = ~garch(1, 1), data = dem2gbp, trace = FALSE) Mean and Variance Equation: data ~ garch(1, 1) [data = dem2gbp] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 -0.0061904 0.0107614 0.1531339 0.8059738 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu -0.006190 0.008462 -0.732 0.464440 omega 0.010761 0.002838 3.793 0.000149 *** alpha1 0.153134 0.026422 5.796 6.8e-09 *** beta1 0.805974 0.033381 24.144 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: -1106.608 normalized: -0.5605916 Description: Sun Oct 15 13:31:47 2023 by user: CRAN done successfully. Executing test function test.plot.methods5 ... Title: GARCH Modelling Call: garchFit(formula = ~garch(1, 1), data = dem2gbp, trace = FALSE) Mean and Variance Equation: data ~ garch(1, 1) [data = dem2gbp] Conditional Distribution: norm Coefficient(s): mu omega alpha1 beta1 -0.0061904 0.0107614 0.1531339 0.8059738 Std. Errors: based on Hessian Error Analysis: Estimate Std. Error t value Pr(>|t|) mu -0.006190 0.008462 -0.732 0.464440 omega 0.010761 0.002838 3.793 0.000149 *** alpha1 0.153134 0.026422 5.796 6.8e-09 *** beta1 0.805974 0.033381 24.144 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Log Likelihood: -1106.608 normalized: -0.5605916 Description: Sun Oct 15 13:31:47 2023 by user: CRAN done successfully. Executing test function test.garchKappa ... done successfully. Executing test function test.predict.methods ... [1] NA done successfully. Executing test function test.sgedDis ... Distribution Check for: ged Call: fBasics::distCheck(fun = "ged", robust = FALSE, mean = 0, sd = 1, nu = 2) 1. Normalization Check: NORM 1 with absolute error < 9.4e-05 2. [p-pfun(qfun(p))]^2 Check: [,1] [,2] [,3] [,4] [,5] [,6] [,7] p 0.001 0.01 0.1 0.5 0.9 0.99 0.999 P 0.001 0.01 0.1 0.5 0.9 0.99 0.999 RMSE 1.15395e-17 3. r(1000) Check: MEAN VAR SAMPLE 0.0266 1.06 X 0 with absolute error < 0 X^2 1 with absolute error < 1.2e-07 MEAN VAR EXACT 0 1 normCheck rmseCheck meanvarCheck TRUE TRUE TRUE Distribution Check for: sged Call: fBasics::distCheck(fun = "sged", robust = FALSE, mean = 0, sd = 1, nu = 2, xi = 0.8) 1. Normalization Check: NORM 1 with absolute error < 1.2e-05 2. [p-pfun(qfun(p))]^2 Check: [,1] [,2] [,3] [,4] [,5] [,6] [,7] p 0.001 0.01 0.1 0.5 0.9 0.99 0.999 P 0.001 0.01 0.1 0.5 0.9 0.99 0.999 RMSE 1.570032e-17 3. r(1000) Check: MEAN VAR SAMPLE -0.0013 0.977 X -4.69377e-09 with absolute error < 3.1e-07 X^2 0.9999996 with absolute error < 2.6e-05 MEAN VAR EXACT -4.69e-09 1 normCheck rmseCheck meanvarCheck TRUE TRUE TRUE done successfully. Executing test function test.sgedFit ... $par mean sd nu 0.02695751 1.02949622 2.40997001 $objective [1] 1444.882 $convergence [1] 0 $iterations [1] 26 $evaluations function gradient 35 108 $message [1] "relative convergence (4)" $par mean sd nu xi 0.01280376 1.01275092 2.34990358 1.53277584 $objective [1] 1400.279 $convergence [1] 0 $iterations [1] 31 $evaluations function gradient 41 160 $message [1] "relative convergence (4)" done successfully. Executing test function test.sgedSlider ... done successfully. Executing test function test.snormDist ... Distribution Check for: norm Call: fBasics::distCheck(fun = "norm", robust = FALSE, mean = 0, sd = 1) 1. Normalization Check: NORM 1 with absolute error < 9.4e-05 2. [p-pfun(qfun(p))]^2 Check: [,1] [,2] [,3] [,4] [,5] [,6] [,7] p 0.001 0.01 0.1 0.5 0.9 0.99 0.999 P 0.001 0.01 0.1 0.5 0.9 0.99 0.999 RMSE 2.205081e-17 3. r(1000) Check: MEAN VAR SAMPLE 0.0114 0.982 X 0 with absolute error < 0 X^2 1 with absolute error < 1.2e-07 MEAN VAR EXACT 0 1 normCheck rmseCheck meanvarCheck TRUE TRUE TRUE Distribution Check for: snorm Call: fBasics::distCheck(fun = "snorm", robust = FALSE, mean = 0, sd = 1, xi = 1.5) 1. Normalization Check: NORM 1.000001 with absolute error < 4.6e-05 2. [p-pfun(qfun(p))]^2 Check: [,1] [,2] [,3] [,4] [,5] [,6] [,7] p 0.001 0.01 0.1 0.5 0.9 0.99 0.999 P 0.001 0.01 0.1 0.5 0.9 0.99 0.999 RMSE 4.211024e-17 3. r(1000) Check: MEAN VAR SAMPLE 0.000765 1.05 X -7.348097e-07 with absolute error < 3.6e-06 X^2 1 with absolute error < 7.1e-06 MEAN VAR EXACT -7.35e-07 1 normCheck rmseCheck meanvarCheck TRUE TRUE TRUE done successfully. Executing test function test.snormFit ... $par mean sd xi -0.001664759 1.025889879 1.563155605 $objective [1] 1406.161 $convergence [1] 0 $iterations [1] 26 $evaluations function gradient 32 97 $message [1] "relative convergence (4)" done successfully. Executing test function test.snormSlider ... done successfully. Executing test function test.sstdDist ... Distribution Check for: std Call: fBasics::distCheck(fun = "std", robust = FALSE, mean = 0, sd = 1, nu = 5) 1. Normalization Check: NORM 1 with absolute error < 1.2e-06 2. [p-pfun(qfun(p))]^2 Check: [,1] [,2] [,3] [,4] [,5] [,6] [,7] p 0.001 0.01 0.1 0.5 0.9 0.99 0.999 P 0.001 0.01 0.1 0.5 0.9 0.99 0.999 RMSE 3.278319e-18 3. r(1000) Check: MEAN VAR SAMPLE 0.0139 0.957 X 0 with absolute error < 0 X^2 1 with absolute error < 3.4e-06 MEAN VAR EXACT 0 1 normCheck rmseCheck meanvarCheck TRUE TRUE TRUE Distribution Check for: sstd Call: fBasics::distCheck(fun = "sstd", robust = FALSE, mean = 0, sd = 1, nu = 5, xi = 1.5) 1. Normalization Check: NORM 1.000001 with absolute error < 7.2e-05 2. [p-pfun(qfun(p))]^2 Check: [,1] [,2] [,3] [,4] [,5] [,6] [,7] p 0.001 0.01 0.1 0.5 0.9 0.99 0.999 P 0.001 0.01 0.1 0.5 0.9 0.99 0.999 RMSE 1.599469e-17 3. r(1000) Check: MEAN VAR SAMPLE -0.0339 0.992 X -1.547865e-07 with absolute error < 6.2e-05 X^2 1 with absolute error < 7.4e-05 MEAN VAR EXACT -1.55e-07 1 normCheck rmseCheck meanvarCheck TRUE TRUE TRUE done successfully. Executing test function test.sstdSlider ... done successfully. Executing test function test.stdFit ... $par mean sd nu 0.03212615 1.00737098 4.97683596 $objective [1] 3447.21 $convergence [1] 0 $iterations [1] 16 $evaluations function gradient 23 61 $message [1] "relative convergence (4)" $minimum [1] 3335.718 $estimate mean sd nu xi -0.0184617 1.0158571 4.9879985 1.4675170 $gradient mean sd nu xi 1.182343e-04 -4.942044e-04 -9.025662e-06 1.112453e-04 $code [1] 1 $iterations [1] 20 done successfully. RUNIT TEST PROTOCOL -- Sun Oct 15 13:31:48 2023 *********************************************** Number of test functions: 52 Number of errors: 0 Number of failures: 0 1 Test Suite : fGarch unit testing - 52 test functions, 0 errors, 0 failures There were 50 or more warnings (use warnings() to see the first 50) > > proc.time() user system elapsed 12.14 0.53 12.65