Cifter, Atilla and Ozun, Alper (2007): The Predictive Performance of Asymmetric Normal Mixture GARCH in Risk Management: Evidence from Turkey.

PDF
MPRA_paper_2489.pdf Download (429kB)  Preview 
Abstract
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 outofsample forecasting Christoffersen test where Garch with normal and studentt distribution perform better based on %95 Cl outofsample 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 these results shows 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.
Item Type:  MPRA Paper 

Institution:  Marmara University 
Original Title:  The Predictive Performance of Asymmetric Normal Mixture GARCH in Risk Management: Evidence from Turkey 
Language:  English 
Keywords:  Garch; Asymmetric Normal Mixture Garch; Kupiec Test; Christoffersen Test; Emerging markets 
Subjects:  G  Financial Economics > G0  General > G00  General C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C32  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models 
Item ID:  2489 
Depositing User:  Atilla Cifter 
Date Deposited:  02. Apr 2007 
Last Modified:  18. Feb 2013 09:17 
References:  Ackert, Lucy F., and Racine, Marie D. 1999. Time Varying Volatility in Canadian and US Stock Index and Index Futures Markets: A Multivariate Analysis, Journal of Financial Research Alexander, Carol and Lazar, Emese. 2003. Symmetric Normal Mixture Garch. ISMA Center Discussion Paper in Finance No.9 Alexander, Carol and Lazar, Emese. 2005. The Equity Index Skew, Market Crashes and Asymmetric Normal Mixture Garch. ISMA Center, Mimeo Alexander, Carol and Lazar, Emese. 2006. Normal Mixture GARCH(1,1):Applications to Exchange Rate Modeling. Journal of Applied Econometrics 21(3), pp.307336. Andersen, Torben G. and Bollerslev, Tim. 1998. DMDollar Volatility: Intraday Activity Patterns, Macroeconomic Announcements, and LongerRun Dependencies. Journal of Finance, Vol.53, No.1, pp.219265 Baillie, Richard T., Bollerslev, Tim, and Mikkelsen, Hans O. 1996. Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, Vol.74, pp.330. Baillie, Richard T. and Bollerslev, Tim. 1989. The Message in Daily Exchange Rates: A ConditionalVariance Tale. Journal of Business and Economic Statistics, 7, pp.297305 Basle Committee on Banking Supervision. 1996a. Amendment to the Capital Accord to Incorporate Market Risks. Basle, Switzerland: BIS. Basle Committee on Banking Supervision. 1996b. Supervisory Framework for the Use of ‘Backtesting’ in Conjunction with the Internal Models Approach to Market Risk Capital Requirements. Manuscript, Bank for International Settlements. Bakshi, Gurdip, Kapadia, Nikunj, and Madan, Dilip B. 2003. Stock Returns Characteristics, Skew, Laws, and the Differential Pricing for Individual Equity Options. The Review of Financial Studies, 16, pp.101143 Bates, David S. 2003. Empirical Options Pricing: A Retrospection. The Journal of Econometrics, 116, pp.387404 Bates, David S. 1991. The Crash of ’87: Was It Expected? The Evidence from Options Markets. Journal of Finance, 46, pp.10091044 Bekaert, Geert, and Wu, Goujun. 2000. Asymmetric Volatility and Risk Equity Markets. The Review of Financial Studies, 13(1), pp.142 Bollerslev, Tim and Woolridge, Jerey M. 1992. Quasimaximum Likelihood Estmation Inference in Dynamic Models with Timevarying Covariances. Econometric Theory, 11, pp.143172 Bollerslev, Tim. 1986. Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, pp.307–327. Bollerslev, Tim and E. Ghysels. 1996. Periodic Autoregressive Conditional Heteroskedasticity. Journal of Business and Economics Statistics, 14, pp.139–152. Bollerslev, Tim, Chou, Ray Y. and Kroner, Kenneth F. 1992. ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence. Journal of Economics and Statistics, 69, pp.542547 Bollerslev, Tim. 1987. A Conditional Heteroskedasticity Time Series Model for Speculative Prices and Rates of Return. Review of Economic and Statistics. 69, pp.542547 Bollerslev, Tim, and Mikkelsen, Hans O. 1996 Modelling and pricing long memory in stock market volatility. Journal of Econometrics 73, 15184 Çifter, Atilla. 2004. Asymmetric and Fractionally Integrated Garch Models with (Skewed) Studentt and Ged Distribution in Risk Management: An Application on Eurobond. Presented in VIII. National Finance Symposium, Istanbul Technical University (in Turkish) Christoffersen, Peter F. 1998. Evaluating Interval Forecasts. International Economic Review, (39): pp.841862. Christoffersen, Peter F., and Jacobs, Kris. 2004. Which Garch Model for Option Valuation?. Management Science, 50, pp.12041221 Christoffersen, Peter F., Heston, Steve, and Jacobs, Kris. 2004. Option Valuation with Conditional Skewness. Fortcoming in The Journal of Econometrics Cung, ChingFan. 1999. Estimating the Fractionally Integrated GARCH Model. National Taiwan University, Working Paper Darrat, Ali., and Benkato, Omar. 2003. Interdependence and Volatility Spillovers under Market Liberalization: The case of Istanbul Stock Exchange. Journal of Business, Finance & Accounting 30:10891114. Ding, Zhuanxin, Clive W. J. Granger, and Robert F. Engle 1993. A Long Memory Property of Stock Market Returns and a New Model. Journal of Empirical Finance, 1, pp.83–106. Dickey, David A., Fuller,Wayne A. 1981. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, pp.1057–1072. Davidson, James. 2001. Moment and Memory Properties of Linear Conditional Heteroskedasticity Models. Manuscript, Cardiff University Davidson, James. 2002. Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, Working Paper at http://www.cf.ac.uk/carbs/econ/davidsonje Doornik, J.A. 1999. An Object Oriented Programming Language. Timberlake Consultant, Third Ed. Engle Robert F. 1982. Autoregressive Conditional Heteroscedasticity with Estimate of the Variance of United Kingdom Inflation. Econometrica,50, pp.9871007 Engle, Robert and Tim, Bollerslev. 1986. Modeling the Persistence of Conditional Variances. Econometric Reviews, 5, pp.150. Engle, Robert and Ng, Victory K. 1993. Measuring and Testing the Impact of News on Volatility. Journal of Finance, 48, pp.17491778 Fernandez, Carmen, and Mark Stell. 1998. On Bayesian Modeling of fat tails and skewness. Journal of the American Statistical Association, 93, pp.359371 Glosten, Lawrence R., Jagahannathan, Ravi, and Runkle, David E. 1993. On the Relationship between the Expected Value and The Volatility of the Nominal Excess Return on Stocks. Journal of Finance, 48, pp.17791801 Hass, Markus, Mittnik, Stefan, and Paolella, Marc S. 2004. Mixed normal conditional heteroskedasticity. Journal of Financial Econometrics 2(2), pp. 211250 Hamilton, James D., and Susmel Raul.1994. Autoregressive Conditional Heteroskedasticity and Changes in Regime. Journal of Econometrics, 64, pp.307333 Harris, Richard and Sollis, Robert. 2003. Applied Time Series Modeling and Forecasting, Wiley Press Hsieh, David A. 1989. Modeling Heteroskedasticity in Daily Foreign Exchange Rates. Journal of Business and Economic Statistics, 7, pp.307317 Kupiec, Paul H. 1995. Techniques for Verifying the Accuracy of Risk Measurement Models. Journal of Derivatives, winter, pp. 7384 Lambert, Peter, and Laurent, Sebastien. 2001. Modelling Financial Time Series Using GARCHtype Models with a Skewed Student Distribution for the Iinnovations. Working paper, Univ. Li`ege, Belgium. Laurent, Sebastien and Peters, JeanPhilippe. 2002. G@rch 2.30: An Ox Package for Estimating and Forecasting Various ARCH Models. Université de Liège, Working Paper Nelson, Daniel B. 1991. Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59:2, pp.347370 Nyblom, Jukka. 1989. Testing for the Constancy of Parameters Over Time. Journal of the American Statistical Association, 84, pp.223230 Palm, Franz. 1996. Garch Models of Volatility, in Handbook of Statistics, ed. By G.Maddala, and C.Rao, pp.209240, Elsevier Science, Amsterdam Palm, Franz, and Vlaar, Peter JG. 1997. Simple Diagnostics Procedures for Modeling Financial Time Series, Allgemeines Statistisches Archiv, 81, pp.85101 Pagan, Adrian. 1996. The Econometrics of Financial Markets. Journal of Empirical Finance, 3, pp.15102 Peters, JeanPhilipe. 2001. Estimating and Forecasting Volatility of Stock Indices Using Asymmetric Garch Models and (Skewed) Studentt Densities, Mimeo, Ecole d’Admin. des Affaires, Unv.of Li`ege Puttonen, Vesa. 1995, International Transmission of Volatility between Stock and Stock İndex Future Markets. Journal of International Financial Markets, Institutions & Money, Vol 5.(2/3). Saltoğlu, Burak. 2003. A High Frequency Analysis of Financial Risk and Crisis: An Empirical Study on Turkish Financial Market, Yaylım Publishing, Istanbul Sarma, Mandira, Thomas, Susan and Shah, Ajay. 2001. Selection of ValueatRisk Models. Mimeo Tang, TaLun and Shieh, ShwuJane. 2006. LongMemory in Stock Index Futures Markets: A ValueatRisk Approach, Phsica A, Vol.366, pp.437448 Tse, Yk. 1998. The Conditional Heteroscedasticity of the YenDollar Exchange Rate, Journal of Applied Econometrics, 193, pp.4955 Taylor, Stephen, 1986. Modelling Financial Time Series. Wiley, New York Wu, Goujun. 2001. The Determinants of Asymmetric Volatility. The Review of Financial Studies. 14(3), pp.837859 Zakoian, JeanMichel. 1994. Threshold heteroskedascity Models. Journal of Economic Dynamics and Control, 15, pp.931955 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/2489 