Araştırma Makalesi
BibTex RIS Kaynak Göster

The Predictive Performance of Asymmetric Normal Mixture GARCH in Risk Management: Evidence from Turkey

Yıl 2007, Cilt: 1 Sayı: 1, 7 - 33, 01.06.2007

Öz

The purpose of this study is to test predictive performance of Asymmetric Normal Mixture GARCH (NMAGARCH) and other GARCH models based on Kupiec and Christoffersen tests for Turkish equity market. The empirical results show that the NMAGARCH perform better based on %99 CI out-of-sample forecasting Christoffersen test
where GARCH with normal and student-t distribution perform better based on %95 Cl out-of-sample forecasting Christoffersen test and Kupiec test. These results show
that none of the model including NMAGARCH outperforms other models in all cases as trading position or confidence intervals and the real implications of these results for
Value-at-Risk estimation is that volatility model should be chosen according to confidence interval and trading positions. Besides, NMAGARCH increases predictive performance for higher confidence internal as Basel requires. 

Kaynakça

  • Bates, D. S. (2003). Empirical Options Pricing: A Retrospection, The Journal of Econometrics, 116: 387-404.
  • Bates, D. S. (1991). The Crash of ’87: Was It Expected? The Evidence from Op- tions Markets, Journal of Finance, 46: 1009-1044.
  • Bekaert, G., and Wu, G. (2000). Asymmetric Volatility and Risk Equity Markets, The Review of Financial Studies, 13(1): 1-42.
  • Bollerslev, T. and Woolridge, J. M. (1992). Quasi-maximum Likelihood Estmati- on Inference in Dynamic Models with Time-varying Covariances, Econometric Theory, 11: 143-172.
  • Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasti- city, Journal of Econometrics, 31: 307–327.
  • Bollerslev, T., and Ghysels, E. (1996). Periodic Autoregressive Conditional Hete- roskedasticity, Journal of Business and Economics Statistics, 14: 139–152.
  • Bollerslev, T., Chou, R. Y. and Kroner, K. F. (1992). ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence, Journal of Economics and Sta- tistics, 69: 542-547.
  • Bollerslev, T. (1987). A Conditional Heteroskedasticity Time Series Model for Speculative Prices and Rates of Return, Review of Economic and Statistics. 69: 542-547.
  • Bollerslev, T., and Mikkelsen, H. O. (1996). Modelling and pricing long memory in stock market volatility, Journal of Econometrics, 73: 151-84.
  • Çifter, A. (2004). Asymmetric and Fractionally Integrated GARCH Models with (Skewed) Student-t and Ged Distribution in Risk Management: An Application on Eurobond, Presented in VIII. National Finance Symposium, Istanbul Techni- cal University (in Turkish).
  • Christoffersen, P. F. (1998). Evaluating Interval Forecasts, International Econo- mic Review, (39): 841-862.
  • Christoffersen, P. F., and Jacobs, Kris. (2004). Which GARCH Model for Option Valuation?, Management Science, 50: 1204-1221.
  • Christoffersen, P. F., Heston, S., and Jacobs, K. (2004). Option Valuation with Conditional Skewness. Fortcoming in The Journal of Econometrics.
  • Cung, C.-F. (1999). Estimating the Fractionally Integrated GARCH Model, Natio- nal Taiwan University, Working Paper.
  • Darrat, A., and Benkato, O. (2003). Interdependence and Volatility Spillovers under Market Liberalization: The case of Istanbul Stock Exchange, Journal of Business, Finance & Accounting, 30:1089-1114.
  • Ding, Z., Granger, C. W. J. and Engle, R. F. (1993). A Long Memory Property of Stock Market Returns and a New Model, Journal of Empirical Finance, 1: 83–106.
  • Dickey, D. A., and Fuller, W. A. (1981). Likelihood ratio statistics for autoreg- ressive time series with a unit root, Econometrica, 49: 1057–1072.
  • Nyblom, J. (1989). Testing for the Constancy of Parameters Over Time, Journal of the American Statistical Association, 84: 223-230.
  • Palm, F. (1996). GARCH Models of Volatility, in Handbook of Statistics, ed. By G.Maddala, and C.Rao, Amsterdam: Elsevier Science. 209-240.
  • Palm, F., and Vlaar, P. JG. (1997). Simple Diagnostics Procedures for Modeling Financial Time Series, Allgemeines Statistisches Archiv, 81: 85-101.
  • Pagan, A. (1996). The Econometrics of Financial Markets, Journal of Empirical Finance, 3: 15-102.
  • Peters, J.-P. (2001). Estimating and Forecasting Volatility of Stock Indices Using Asymmetric GARCH Models and (Skewed) Student-t Densities, Mimeo, Ecole d’Admin. des Affaires, Unv.of Li`ege
  • Puttonen, V. (1995). International Transmission of Volatility between Stock and Stock İndex Future Markets, Journal of International Financial Markets, Institu- tions & Money, 5.(2/3).
  • Saltoğlu, B. (2003). A High Frequency Analysis of Financial Risk and Crisis: An Empirical Study on Turkish Financial Market, Istanbul: Yaylım Publishing.
  • Sarma, M., Thomas, S. and Shah, A. (2001). Selection of Value-at-Risk Models, Mimeo
  • Tang, T.-L., and Shieh, S.-J. (2006). Long-Memory in Stock Index Futures Mar- kets: A Value-at-Risk Approach, Phsica A, 366: 437-448.
  • Tsay, R. S. (2005). Analysis of Financial Time Series, New York: John Wiley & Sons.
  • Tse, Y. (1998). The Conditional Heteroscedasticity of the Yen-Dollar Exchange Rate, Journal of Applied Econometrics, 193: 49-55.
  • Taylor, S. (1986). Modeling Financial Time Series, New York : John Wiley & Sons.
  • Wu, G. (2001). The Determinants of Asymmetric Volatility, The Review of Fi- nancial Studies, 14(3): 837-859.
  • Zakoian, J.-M. (1994). Threshold heteroskedascity Models, Journal of Economic Dynamics and Control, 15: 931-955.

Risk Yönetiminde Asimetrik Normal Karma GARCH Modelinin Öngörü Performansı: Türkiye Uygulaması

Yıl 2007, Cilt: 1 Sayı: 1, 7 - 33, 01.06.2007

Öz

The purpose of this study is to test predictive performance of Asymmetric Normal Mixture GARCH (NMAGARCH) and other GARCH models based on Kupiec and Christoffersen tests for Turkish equity market. The empirical results show that the NMAGARCH perform better based on %99 CI out-of-sample forecasting Christoffersen test where GARCH with normal and student-t distribution perform better based on %95 Cl out-of-sample forecasting Christoffersen test and Kupiec test. These results show that none of the model including NMAGARCH outperforms other models in all cases as trading position or confidence intervals and the real implications of these results for Value-at-Risk estimation is that volatility model should be chosen according to confidence interval and trading positions. Besides, NMAGARCH increases predictive performance for higher confidence internal as Basel requires. 

Kaynakça

  • Bates, D. S. (2003). Empirical Options Pricing: A Retrospection, The Journal of Econometrics, 116: 387-404.
  • Bates, D. S. (1991). The Crash of ’87: Was It Expected? The Evidence from Op- tions Markets, Journal of Finance, 46: 1009-1044.
  • Bekaert, G., and Wu, G. (2000). Asymmetric Volatility and Risk Equity Markets, The Review of Financial Studies, 13(1): 1-42.
  • Bollerslev, T. and Woolridge, J. M. (1992). Quasi-maximum Likelihood Estmati- on Inference in Dynamic Models with Time-varying Covariances, Econometric Theory, 11: 143-172.
  • Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasti- city, Journal of Econometrics, 31: 307–327.
  • Bollerslev, T., and Ghysels, E. (1996). Periodic Autoregressive Conditional Hete- roskedasticity, Journal of Business and Economics Statistics, 14: 139–152.
  • Bollerslev, T., Chou, R. Y. and Kroner, K. F. (1992). ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence, Journal of Economics and Sta- tistics, 69: 542-547.
  • Bollerslev, T. (1987). A Conditional Heteroskedasticity Time Series Model for Speculative Prices and Rates of Return, Review of Economic and Statistics. 69: 542-547.
  • Bollerslev, T., and Mikkelsen, H. O. (1996). Modelling and pricing long memory in stock market volatility, Journal of Econometrics, 73: 151-84.
  • Çifter, A. (2004). Asymmetric and Fractionally Integrated GARCH Models with (Skewed) Student-t and Ged Distribution in Risk Management: An Application on Eurobond, Presented in VIII. National Finance Symposium, Istanbul Techni- cal University (in Turkish).
  • Christoffersen, P. F. (1998). Evaluating Interval Forecasts, International Econo- mic Review, (39): 841-862.
  • Christoffersen, P. F., and Jacobs, Kris. (2004). Which GARCH Model for Option Valuation?, Management Science, 50: 1204-1221.
  • Christoffersen, P. F., Heston, S., and Jacobs, K. (2004). Option Valuation with Conditional Skewness. Fortcoming in The Journal of Econometrics.
  • Cung, C.-F. (1999). Estimating the Fractionally Integrated GARCH Model, Natio- nal Taiwan University, Working Paper.
  • Darrat, A., and Benkato, O. (2003). Interdependence and Volatility Spillovers under Market Liberalization: The case of Istanbul Stock Exchange, Journal of Business, Finance & Accounting, 30:1089-1114.
  • Ding, Z., Granger, C. W. J. and Engle, R. F. (1993). A Long Memory Property of Stock Market Returns and a New Model, Journal of Empirical Finance, 1: 83–106.
  • Dickey, D. A., and Fuller, W. A. (1981). Likelihood ratio statistics for autoreg- ressive time series with a unit root, Econometrica, 49: 1057–1072.
  • Nyblom, J. (1989). Testing for the Constancy of Parameters Over Time, Journal of the American Statistical Association, 84: 223-230.
  • Palm, F. (1996). GARCH Models of Volatility, in Handbook of Statistics, ed. By G.Maddala, and C.Rao, Amsterdam: Elsevier Science. 209-240.
  • Palm, F., and Vlaar, P. JG. (1997). Simple Diagnostics Procedures for Modeling Financial Time Series, Allgemeines Statistisches Archiv, 81: 85-101.
  • Pagan, A. (1996). The Econometrics of Financial Markets, Journal of Empirical Finance, 3: 15-102.
  • Peters, J.-P. (2001). Estimating and Forecasting Volatility of Stock Indices Using Asymmetric GARCH Models and (Skewed) Student-t Densities, Mimeo, Ecole d’Admin. des Affaires, Unv.of Li`ege
  • Puttonen, V. (1995). International Transmission of Volatility between Stock and Stock İndex Future Markets, Journal of International Financial Markets, Institu- tions & Money, 5.(2/3).
  • Saltoğlu, B. (2003). A High Frequency Analysis of Financial Risk and Crisis: An Empirical Study on Turkish Financial Market, Istanbul: Yaylım Publishing.
  • Sarma, M., Thomas, S. and Shah, A. (2001). Selection of Value-at-Risk Models, Mimeo
  • Tang, T.-L., and Shieh, S.-J. (2006). Long-Memory in Stock Index Futures Mar- kets: A Value-at-Risk Approach, Phsica A, 366: 437-448.
  • Tsay, R. S. (2005). Analysis of Financial Time Series, New York: John Wiley & Sons.
  • Tse, Y. (1998). The Conditional Heteroscedasticity of the Yen-Dollar Exchange Rate, Journal of Applied Econometrics, 193: 49-55.
  • Taylor, S. (1986). Modeling Financial Time Series, New York : John Wiley & Sons.
  • Wu, G. (2001). The Determinants of Asymmetric Volatility, The Review of Fi- nancial Studies, 14(3): 837-859.
  • Zakoian, J.-M. (1994). Threshold heteroskedascity Models, Journal of Economic Dynamics and Control, 15: 931-955.
Toplam 31 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Finans
Bölüm Research Article
Yazarlar

Atilla Çifter Bu kişi benim

Alper Özün Bu kişi benim

Yayımlanma Tarihi 1 Haziran 2007
Yayımlandığı Sayı Yıl 2007 Cilt: 1 Sayı: 1

Kaynak Göster

APA Çifter, A., & Özün, A. (2007). The Predictive Performance of Asymmetric Normal Mixture GARCH in Risk Management: Evidence from Turkey. BDDK Bankacılık Ve Finansal Piyasalar Dergisi, 1(1), 7-33.