SavedAdd your journal

Journals / BDDK Bankacılık ve Finansal Piyasalar Dergisi / 2007 / Cilt: 1 - Sayı: 1

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

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

Atilla ÇİFTER
Alper ÖZÜN
Pages
7–33
Publication Date
2007-06-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The empirical results show that the NMAGARCH perform better based on %99 CI out-of-sample forecasting Christoffersen testwhere GARCH with normal and student-t distribution perform better based on %95 Cl out-of-sample forecasting Christoffersen test and Kupiec test.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data:The empirical results show that the NMAGARCH perform better based on %99 CI out-of-sample forecasting Christoffersen testwhere GARCH with normal and student-t distribution perform better based on %95 Cl out-of-sample forecasting Christoffersen test and Kupiec test.
💡
Conclusion & Impact:Besides, NMAGARCH increases predictive performance for higher confidence internal as Basel requires.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:GARCH · Asimetrik Normal Karma GARCH · Christoffersen Testi

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 out-of-sample forecasting Christoffersen testwhere GARCH with normal and student-t distribution perform better based on %95 Cl out-of-sample forecasting Christoffersen test and Kupiec test. These results showthat 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 forValue-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.

Özet

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.

🔖

Cite This Article

ÇİFTER, A., ÖZÜN, A. (2007). The Predictive Performance of Asymmetric Normal Mixture GARCH in Risk Management: Evidence from Turkey. BDDK Bankacılık ve Finansal Piyasalar Dergisi, Cilt: 1 - Sayı: 1, 7–33. https://ajindex.com/articles/473888

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. Bates, D. S. (2003). Empirical Options Pricing: A Retrospection, The Journal of Econometrics, 116: 387-404.
  2. Bates, D. S. (1991). The Crash of ’87: Was It Expected? The Evidence from Op- tions Markets, Journal of Finance, 46: 1009-1044.
  3. Bekaert, G., and Wu, G. (2000). Asymmetric Volatility and Risk Equity Markets, The Review of Financial Studies, 13(1): 1-42.
  4. 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.
  5. Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasti- city, Journal of Econometrics, 31: 307–327.
  6. Bollerslev, T., and Ghysels, E. (1996). Periodic Autoregressive Conditional Hete- roskedasticity, Journal of Business and Economics Statistics, 14: 139–152.
  7. 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.
  8. Bollerslev, T. (1987). A Conditional Heteroskedasticity Time Series Model for Speculative Prices and Rates of Return, Review of Economic and Statistics. 69: 542-547.
  9. Bollerslev, T., and Mikkelsen, H. O. (1996). Modelling and pricing long memory in stock market volatility, Journal of Econometrics, 73: 151-84.
  10. 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).
  11. Evaluating Interval Forecasts, International Econo- mic Review, (39): 841-862.
  12. Christoffersen, P. F., and Jacobs, Kris. (2004). Which GARCH Model for Option Valuation?, Management Science, 50: 1204-1221.
  13. 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.
  14. 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.
  15. Dickey, D. A., and Fuller, W. A. (1981). Likelihood ratio statistics for autoreg- ress