Evaluating Vector Multiplicative Error Models with the Hosking–Ljung–Box Portmanteau Test and Kernel-Based Test Statistics
Abstract Multivariate nonlinear time series models have experienced many developments for modeling data coming from financial applications. Several financial time series are realizations from nonnegative processes. An important class of models is composed of vector multiplicative error models (vMEM)...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sango, Joël [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Grace Scientific Publishing 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of statistical theory and practice - Cham : Springer International Publishing, 2007, 13(2019), 2 vom: 14. Feb. |
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| Übergeordnetes Werk: |
volume:13 ; year:2019 ; number:2 ; day:14 ; month:02 |
| Links: |
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| DOI / URN: |
10.1007/s42519-018-0036-1 |
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| Katalog-ID: |
SPR038622734 |
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| 100 | 1 | |a Sango, Joël |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Evaluating Vector Multiplicative Error Models with the Hosking–Ljung–Box Portmanteau Test and Kernel-Based Test Statistics |
| 264 | 1 | |c 2019 | |
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| 520 | |a Abstract Multivariate nonlinear time series models have experienced many developments for modeling data coming from financial applications. Several financial time series are realizations from nonnegative processes. An important class of models is composed of vector multiplicative error models (vMEM), which can describe contemporaneous correlations among innovations and the dynamic interdependencies among variables. Modeling and estimation issues have been addressed, but few diagnostic checking procedures are available. Here, new tests are proposed to check vMEM models. The asymptotic distributions of the popular Hosking–Ljung–Box (HLB) test statistics are found to converge in distribution to weighted sums of independent Chi-squared random variables under the null hypothesis of adequacy. A generalized HLB test statistic is motivated by comparing a vector spectral density estimator of the residuals with the spectral density calculated under the null hypothesis. Under general conditions, that kind of approach leads to consistent procedures and to powerful measures of lack of fit. To improve the finite sample properties, the spectral test statistics rely on the power transformation of Chen and Deo (J R Stat Soc Ser B 66:117–130, 2004). Appealing properties of the spectral procedures include the distribution-free property and the fact that they converge in distribution to convenient standard normal distributions under the null hypothesis. Simulation experiments are reported to appreciate the properties of the methods. An application using financial data previously analyzed by Cipollini et al. (J Appl Econom 28:1067–1086, 2013) illustrates the merits of our procedures. | ||
| 650 | 4 | |a Multivariate time series |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Nonnegative processes |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Nonlinear time series |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Portmanteau test statistic |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Spectral density |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Duchesne, Pierre |4 aut | |
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10.1007/s42519-018-0036-1 doi (DE-627)SPR038622734 (SPR)s42519-018-0036-1-e DE-627 ger DE-627 rakwb eng Sango, Joël verfasserin aut Evaluating Vector Multiplicative Error Models with the Hosking–Ljung–Box Portmanteau Test and Kernel-Based Test Statistics 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Grace Scientific Publishing 2019 Abstract Multivariate nonlinear time series models have experienced many developments for modeling data coming from financial applications. Several financial time series are realizations from nonnegative processes. An important class of models is composed of vector multiplicative error models (vMEM), which can describe contemporaneous correlations among innovations and the dynamic interdependencies among variables. Modeling and estimation issues have been addressed, but few diagnostic checking procedures are available. Here, new tests are proposed to check vMEM models. The asymptotic distributions of the popular Hosking–Ljung–Box (HLB) test statistics are found to converge in distribution to weighted sums of independent Chi-squared random variables under the null hypothesis of adequacy. A generalized HLB test statistic is motivated by comparing a vector spectral density estimator of the residuals with the spectral density calculated under the null hypothesis. Under general conditions, that kind of approach leads to consistent procedures and to powerful measures of lack of fit. To improve the finite sample properties, the spectral test statistics rely on the power transformation of Chen and Deo (J R Stat Soc Ser B 66:117–130, 2004). Appealing properties of the spectral procedures include the distribution-free property and the fact that they converge in distribution to convenient standard normal distributions under the null hypothesis. Simulation experiments are reported to appreciate the properties of the methods. An application using financial data previously analyzed by Cipollini et al. (J Appl Econom 28:1067–1086, 2013) illustrates the merits of our procedures. Multivariate time series (dpeaa)DE-He213 Nonnegative processes (dpeaa)DE-He213 Nonlinear time series (dpeaa)DE-He213 Portmanteau test statistic (dpeaa)DE-He213 Spectral density (dpeaa)DE-He213 Duchesne, Pierre aut Enthalten in Journal of statistical theory and practice Cham : Springer International Publishing, 2007 13(2019), 2 vom: 14. Feb. (DE-627)771398247 (DE-600)2740991-0 1559-8616 nnns volume:13 year:2019 number:2 day:14 month:02 https://dx.doi.org/10.1007/s42519-018-0036-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2019 2 14 02 |
| spelling |
10.1007/s42519-018-0036-1 doi (DE-627)SPR038622734 (SPR)s42519-018-0036-1-e DE-627 ger DE-627 rakwb eng Sango, Joël verfasserin aut Evaluating Vector Multiplicative Error Models with the Hosking–Ljung–Box Portmanteau Test and Kernel-Based Test Statistics 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Grace Scientific Publishing 2019 Abstract Multivariate nonlinear time series models have experienced many developments for modeling data coming from financial applications. Several financial time series are realizations from nonnegative processes. An important class of models is composed of vector multiplicative error models (vMEM), which can describe contemporaneous correlations among innovations and the dynamic interdependencies among variables. Modeling and estimation issues have been addressed, but few diagnostic checking procedures are available. Here, new tests are proposed to check vMEM models. The asymptotic distributions of the popular Hosking–Ljung–Box (HLB) test statistics are found to converge in distribution to weighted sums of independent Chi-squared random variables under the null hypothesis of adequacy. A generalized HLB test statistic is motivated by comparing a vector spectral density estimator of the residuals with the spectral density calculated under the null hypothesis. Under general conditions, that kind of approach leads to consistent procedures and to powerful measures of lack of fit. To improve the finite sample properties, the spectral test statistics rely on the power transformation of Chen and Deo (J R Stat Soc Ser B 66:117–130, 2004). Appealing properties of the spectral procedures include the distribution-free property and the fact that they converge in distribution to convenient standard normal distributions under the null hypothesis. Simulation experiments are reported to appreciate the properties of the methods. An application using financial data previously analyzed by Cipollini et al. (J Appl Econom 28:1067–1086, 2013) illustrates the merits of our procedures. Multivariate time series (dpeaa)DE-He213 Nonnegative processes (dpeaa)DE-He213 Nonlinear time series (dpeaa)DE-He213 Portmanteau test statistic (dpeaa)DE-He213 Spectral density (dpeaa)DE-He213 Duchesne, Pierre aut Enthalten in Journal of statistical theory and practice Cham : Springer International Publishing, 2007 13(2019), 2 vom: 14. Feb. (DE-627)771398247 (DE-600)2740991-0 1559-8616 nnns volume:13 year:2019 number:2 day:14 month:02 https://dx.doi.org/10.1007/s42519-018-0036-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2019 2 14 02 |
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10.1007/s42519-018-0036-1 doi (DE-627)SPR038622734 (SPR)s42519-018-0036-1-e DE-627 ger DE-627 rakwb eng Sango, Joël verfasserin aut Evaluating Vector Multiplicative Error Models with the Hosking–Ljung–Box Portmanteau Test and Kernel-Based Test Statistics 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Grace Scientific Publishing 2019 Abstract Multivariate nonlinear time series models have experienced many developments for modeling data coming from financial applications. Several financial time series are realizations from nonnegative processes. An important class of models is composed of vector multiplicative error models (vMEM), which can describe contemporaneous correlations among innovations and the dynamic interdependencies among variables. Modeling and estimation issues have been addressed, but few diagnostic checking procedures are available. Here, new tests are proposed to check vMEM models. The asymptotic distributions of the popular Hosking–Ljung–Box (HLB) test statistics are found to converge in distribution to weighted sums of independent Chi-squared random variables under the null hypothesis of adequacy. A generalized HLB test statistic is motivated by comparing a vector spectral density estimator of the residuals with the spectral density calculated under the null hypothesis. Under general conditions, that kind of approach leads to consistent procedures and to powerful measures of lack of fit. To improve the finite sample properties, the spectral test statistics rely on the power transformation of Chen and Deo (J R Stat Soc Ser B 66:117–130, 2004). Appealing properties of the spectral procedures include the distribution-free property and the fact that they converge in distribution to convenient standard normal distributions under the null hypothesis. Simulation experiments are reported to appreciate the properties of the methods. An application using financial data previously analyzed by Cipollini et al. (J Appl Econom 28:1067–1086, 2013) illustrates the merits of our procedures. Multivariate time series (dpeaa)DE-He213 Nonnegative processes (dpeaa)DE-He213 Nonlinear time series (dpeaa)DE-He213 Portmanteau test statistic (dpeaa)DE-He213 Spectral density (dpeaa)DE-He213 Duchesne, Pierre aut Enthalten in Journal of statistical theory and practice Cham : Springer International Publishing, 2007 13(2019), 2 vom: 14. Feb. (DE-627)771398247 (DE-600)2740991-0 1559-8616 nnns volume:13 year:2019 number:2 day:14 month:02 https://dx.doi.org/10.1007/s42519-018-0036-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2019 2 14 02 |
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10.1007/s42519-018-0036-1 doi (DE-627)SPR038622734 (SPR)s42519-018-0036-1-e DE-627 ger DE-627 rakwb eng Sango, Joël verfasserin aut Evaluating Vector Multiplicative Error Models with the Hosking–Ljung–Box Portmanteau Test and Kernel-Based Test Statistics 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Grace Scientific Publishing 2019 Abstract Multivariate nonlinear time series models have experienced many developments for modeling data coming from financial applications. Several financial time series are realizations from nonnegative processes. An important class of models is composed of vector multiplicative error models (vMEM), which can describe contemporaneous correlations among innovations and the dynamic interdependencies among variables. Modeling and estimation issues have been addressed, but few diagnostic checking procedures are available. Here, new tests are proposed to check vMEM models. The asymptotic distributions of the popular Hosking–Ljung–Box (HLB) test statistics are found to converge in distribution to weighted sums of independent Chi-squared random variables under the null hypothesis of adequacy. A generalized HLB test statistic is motivated by comparing a vector spectral density estimator of the residuals with the spectral density calculated under the null hypothesis. Under general conditions, that kind of approach leads to consistent procedures and to powerful measures of lack of fit. To improve the finite sample properties, the spectral test statistics rely on the power transformation of Chen and Deo (J R Stat Soc Ser B 66:117–130, 2004). Appealing properties of the spectral procedures include the distribution-free property and the fact that they converge in distribution to convenient standard normal distributions under the null hypothesis. Simulation experiments are reported to appreciate the properties of the methods. An application using financial data previously analyzed by Cipollini et al. (J Appl Econom 28:1067–1086, 2013) illustrates the merits of our procedures. Multivariate time series (dpeaa)DE-He213 Nonnegative processes (dpeaa)DE-He213 Nonlinear time series (dpeaa)DE-He213 Portmanteau test statistic (dpeaa)DE-He213 Spectral density (dpeaa)DE-He213 Duchesne, Pierre aut Enthalten in Journal of statistical theory and practice Cham : Springer International Publishing, 2007 13(2019), 2 vom: 14. Feb. (DE-627)771398247 (DE-600)2740991-0 1559-8616 nnns volume:13 year:2019 number:2 day:14 month:02 https://dx.doi.org/10.1007/s42519-018-0036-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2019 2 14 02 |
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10.1007/s42519-018-0036-1 doi (DE-627)SPR038622734 (SPR)s42519-018-0036-1-e DE-627 ger DE-627 rakwb eng Sango, Joël verfasserin aut Evaluating Vector Multiplicative Error Models with the Hosking–Ljung–Box Portmanteau Test and Kernel-Based Test Statistics 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Grace Scientific Publishing 2019 Abstract Multivariate nonlinear time series models have experienced many developments for modeling data coming from financial applications. Several financial time series are realizations from nonnegative processes. An important class of models is composed of vector multiplicative error models (vMEM), which can describe contemporaneous correlations among innovations and the dynamic interdependencies among variables. Modeling and estimation issues have been addressed, but few diagnostic checking procedures are available. Here, new tests are proposed to check vMEM models. The asymptotic distributions of the popular Hosking–Ljung–Box (HLB) test statistics are found to converge in distribution to weighted sums of independent Chi-squared random variables under the null hypothesis of adequacy. A generalized HLB test statistic is motivated by comparing a vector spectral density estimator of the residuals with the spectral density calculated under the null hypothesis. Under general conditions, that kind of approach leads to consistent procedures and to powerful measures of lack of fit. To improve the finite sample properties, the spectral test statistics rely on the power transformation of Chen and Deo (J R Stat Soc Ser B 66:117–130, 2004). Appealing properties of the spectral procedures include the distribution-free property and the fact that they converge in distribution to convenient standard normal distributions under the null hypothesis. Simulation experiments are reported to appreciate the properties of the methods. An application using financial data previously analyzed by Cipollini et al. (J Appl Econom 28:1067–1086, 2013) illustrates the merits of our procedures. Multivariate time series (dpeaa)DE-He213 Nonnegative processes (dpeaa)DE-He213 Nonlinear time series (dpeaa)DE-He213 Portmanteau test statistic (dpeaa)DE-He213 Spectral density (dpeaa)DE-He213 Duchesne, Pierre aut Enthalten in Journal of statistical theory and practice Cham : Springer International Publishing, 2007 13(2019), 2 vom: 14. Feb. (DE-627)771398247 (DE-600)2740991-0 1559-8616 nnns volume:13 year:2019 number:2 day:14 month:02 https://dx.doi.org/10.1007/s42519-018-0036-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2019 2 14 02 |
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Sango, Joël |
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Sango, Joël misc Multivariate time series misc Nonnegative processes misc Nonlinear time series misc Portmanteau test statistic misc Spectral density Evaluating Vector Multiplicative Error Models with the Hosking–Ljung–Box Portmanteau Test and Kernel-Based Test Statistics |
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Evaluating Vector Multiplicative Error Models with the Hosking–Ljung–Box Portmanteau Test and Kernel-Based Test Statistics Multivariate time series (dpeaa)DE-He213 Nonnegative processes (dpeaa)DE-He213 Nonlinear time series (dpeaa)DE-He213 Portmanteau test statistic (dpeaa)DE-He213 Spectral density (dpeaa)DE-He213 |
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Evaluating Vector Multiplicative Error Models with the Hosking–Ljung–Box Portmanteau Test and Kernel-Based Test Statistics |
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Evaluating Vector Multiplicative Error Models with the Hosking–Ljung–Box Portmanteau Test and Kernel-Based Test Statistics |
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evaluating vector multiplicative error models with the hosking–ljung–box portmanteau test and kernel-based test statistics |
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Evaluating Vector Multiplicative Error Models with the Hosking–Ljung–Box Portmanteau Test and Kernel-Based Test Statistics |
| abstract |
Abstract Multivariate nonlinear time series models have experienced many developments for modeling data coming from financial applications. Several financial time series are realizations from nonnegative processes. An important class of models is composed of vector multiplicative error models (vMEM), which can describe contemporaneous correlations among innovations and the dynamic interdependencies among variables. Modeling and estimation issues have been addressed, but few diagnostic checking procedures are available. Here, new tests are proposed to check vMEM models. The asymptotic distributions of the popular Hosking–Ljung–Box (HLB) test statistics are found to converge in distribution to weighted sums of independent Chi-squared random variables under the null hypothesis of adequacy. A generalized HLB test statistic is motivated by comparing a vector spectral density estimator of the residuals with the spectral density calculated under the null hypothesis. Under general conditions, that kind of approach leads to consistent procedures and to powerful measures of lack of fit. To improve the finite sample properties, the spectral test statistics rely on the power transformation of Chen and Deo (J R Stat Soc Ser B 66:117–130, 2004). Appealing properties of the spectral procedures include the distribution-free property and the fact that they converge in distribution to convenient standard normal distributions under the null hypothesis. Simulation experiments are reported to appreciate the properties of the methods. An application using financial data previously analyzed by Cipollini et al. (J Appl Econom 28:1067–1086, 2013) illustrates the merits of our procedures. © Grace Scientific Publishing 2019 |
| abstractGer |
Abstract Multivariate nonlinear time series models have experienced many developments for modeling data coming from financial applications. Several financial time series are realizations from nonnegative processes. An important class of models is composed of vector multiplicative error models (vMEM), which can describe contemporaneous correlations among innovations and the dynamic interdependencies among variables. Modeling and estimation issues have been addressed, but few diagnostic checking procedures are available. Here, new tests are proposed to check vMEM models. The asymptotic distributions of the popular Hosking–Ljung–Box (HLB) test statistics are found to converge in distribution to weighted sums of independent Chi-squared random variables under the null hypothesis of adequacy. A generalized HLB test statistic is motivated by comparing a vector spectral density estimator of the residuals with the spectral density calculated under the null hypothesis. Under general conditions, that kind of approach leads to consistent procedures and to powerful measures of lack of fit. To improve the finite sample properties, the spectral test statistics rely on the power transformation of Chen and Deo (J R Stat Soc Ser B 66:117–130, 2004). Appealing properties of the spectral procedures include the distribution-free property and the fact that they converge in distribution to convenient standard normal distributions under the null hypothesis. Simulation experiments are reported to appreciate the properties of the methods. An application using financial data previously analyzed by Cipollini et al. (J Appl Econom 28:1067–1086, 2013) illustrates the merits of our procedures. © Grace Scientific Publishing 2019 |
| abstract_unstemmed |
Abstract Multivariate nonlinear time series models have experienced many developments for modeling data coming from financial applications. Several financial time series are realizations from nonnegative processes. An important class of models is composed of vector multiplicative error models (vMEM), which can describe contemporaneous correlations among innovations and the dynamic interdependencies among variables. Modeling and estimation issues have been addressed, but few diagnostic checking procedures are available. Here, new tests are proposed to check vMEM models. The asymptotic distributions of the popular Hosking–Ljung–Box (HLB) test statistics are found to converge in distribution to weighted sums of independent Chi-squared random variables under the null hypothesis of adequacy. A generalized HLB test statistic is motivated by comparing a vector spectral density estimator of the residuals with the spectral density calculated under the null hypothesis. Under general conditions, that kind of approach leads to consistent procedures and to powerful measures of lack of fit. To improve the finite sample properties, the spectral test statistics rely on the power transformation of Chen and Deo (J R Stat Soc Ser B 66:117–130, 2004). Appealing properties of the spectral procedures include the distribution-free property and the fact that they converge in distribution to convenient standard normal distributions under the null hypothesis. Simulation experiments are reported to appreciate the properties of the methods. An application using financial data previously analyzed by Cipollini et al. (J Appl Econom 28:1067–1086, 2013) illustrates the merits of our procedures. © Grace Scientific Publishing 2019 |
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Evaluating Vector Multiplicative Error Models with the Hosking–Ljung–Box Portmanteau Test and Kernel-Based Test Statistics |
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