Untitled Document
Volume 11, No.1
Journal Information
  • - Year : 2007 Jun.
  • - Issue :2007 Vol. 11, No. 1
  • - Date : 2007-06-30

EAER > Journal > Volumes > Contents >Article

  • Journal title : East Asian Economic Review
  • PISSN : 1598-2769   EISSN : 2508-1667
  • Vol. 11, No. 1, 2007. pp. 211-240.
  • Publisher : Korean institute for international Economic Policy





print excel
A Skewed Student-t Value-at-Risk Approach for Long Memory Volatility Processes in Japanese Financial Markets   


Seong¡-Min Yoon


Pukyong National University

Sang-Hoon Kang


University of South Australia


This paper investigates the relevance of skewed Student-t distributions in capturing long memory volatility properties in the daily return series of Japanese financial data (Nikkei 225 Index and JPY-USD exchange rate). For this purpose, we assess the performance of two long memory Value-at-Risk (VaR) models (FIGARCH and FIAPARCH VaR model) with three different distribution innovations: the normal, Student-t, and skewed Student-t distributions. From our results, we find that the skewed Student-t distribution model produces more accurate VaR estimations than normal and Student-t distribution models. Thus, accounting for skewness and excess kurtosis in the asset return distribution can provide suitable criteria for VaR model selection in the context of long memory volatility and enhance the performance of risk management in Japanese financial markets.


Value-at-Risk, Japanese Financial Markets, Volatility, Asymmetry, Long Memory, Skewed Student-t Distribution


E58, F21, G15, G38



1 Baillie, Richard T. 1996. "Long Memory Processes and Fractional Integration in Econometrics," Journal of Econometrics, vol. 73, no. 1, pp. 5-59. 
2 Baillie, Richard T., Bollerslev, T. and Hans Ole Mikkelsen. 1996. "Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity," Journal of Econometrics, vol. 74, no. 1, pp. 3-30. 
3 Bali, Turan G., and Panayiotis Theodossiou. 2007. "A Conditional-SGT-VaR Approach with Alternative GARCH Models," Annals of Operations Research, vol. 151, no. 1, pp. 241-267. 
4 Black, Fischer. 1976. "Studies in Stock Price Volatility Changes," In Proceedings of the 1976 Business and Economic Statistics Section, American Statistical Association, pp. 177-181. 
5 Bollerslev, Tim. 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," Review of Economics and Statistics, vol. 69, no. 3, pp. 542-547. 
6 Bollerslev, Tim, and Hans Ole Mikkelsen. 1996. "Modeling and Pricing Long Memory in Stock Market Volatility," Journal of Econometrics, vol. 73, no. 1, pp. 151-184. 
7 Degiannakis, Stavros. 2004. "Volatility Forecasting, Evidence from a Fractional Integrated Asymmetric Power ARCH Skewed-t Model," Journal Applied Financial Economics, vol. 14, no. 18, pp. 1333-1342. 
8 Ding, Zhuanxin., Granger, Clive W. J., and Robert F. Engle. 1993. "A Long Memory Property of Stock Returns and a New Model," Journal of Empirical Finance, vol. 1, no. 1, pp. 83-106. 
9 Duffie,Darell, and Jun Pan. 1997. "An Overview of Value at Risk," Journal of Derivatives, vol. 4, no. 3, pp. 7-49. 
10 Engle, Robert F. 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, vol. 50, no. 4, pp. 987-1007. 
11 Engle, Robert F. and Victor K. Ng. 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, vol. 48, no. 5, pp. 1749-1778. 
12 Fang, Hsing, and Tsong-Yue Lai. 1997. "Co-Kurtosis and Capital Asset Pricing," Financial Review, vol. 32 no. 2, pp. 293-307. 
13 Giot, Pierre, and Sebastien Laurent. 2003. "Value-at-Risk for Long and Short Trading Positions," Journal of Applied Econometrics, vol. 18, no. 6, pp. 641-664. 
14 Granger, C. W. J. 1980. "Long Memory Relationships and the Aggregation of Dynamic Models," Journal of Econometrics, vol. 14, no. 2, pp. 227-238. 
15 Granger, C. W. J., and R. Joyeux. 1980. "An Introduction to Long Memory Time Series Models and Fractional Differencing," Journal of Time Series Analysis, vol. 1, pp. 5-39. 
16 Hansen, Bruce E. 1994. "Autoregressive Conditional Density Estimation," International Economic Review, vol. 35, no. 3, pp. 705-730. 
17 Harvey, Campbell R., and Akhtar Siddique. 2000. "Time-Varying Conditional Skewness and the Market Risk Premium," Research in Banking and Finance, vol. 1, pp. 25-58. 
18 Hentschel, Ludger. 1995. "All in the Family Nesting Symmetric and Asymmetric GARCH Models," Journal of Financial Economics, vol. 39, no. 1, pp. 71-104. 
19 Hogg, Robert V., and Stuart A. Klugman. 1983. "On the Estimation of Long Tailed Skewed Distributions with Actuarial Applications," Journal of Econometrics, vol. 23, no. 1, pp. 91-102. 
20 Hosking, J. R. M. 1981. "Fractional Differencing," Biometrika, vol. 68, no. 1, pp. 165-176. 
21 Hwang, Y. 2001. "Asymmetric Long Memory GARCH in Exchange Return," Economics Letters, vol. 73, no. 1, pp. 1-5. 
22 Jacobsen, Ben. 1996. "Long Term Dependence in Stock Returns," Journal of Empirical Finance, vol. 3, no. 4, pp. 393-417. 
23 Kupiec, Paul H. 1995. "Techniques for Verifying the Accuracy of Risk Measurement Models," Journal of Derivatives, vol. 3, no. 2, pp. 73-84. 
24 Lambert, P., and S. Laurent. 2001. "Modelling Financial Time Series Using GARCH-Type Models and a Skewed Student Density," Mimeo, Universite de Liege. 
25 Laurent, S., and J. P. Peters. 2004. GARCH 4.0, Estimating and Forecasting ARCH Models. London: Timberlake Consultants Press. 
26 Lo, Andrew W. 1991. "Long-Term Memory in Stock Market Prices," Econometrica, vol. 59, no. 5, pp. 1279-1313. 
27 Maheu, John M. 2005. "Can GARCH Models Capture Long-Range Dependence?." Studies in Nonlinear Dynamics & Econometrics, vol. 9, no. 4, pp. 1-41. 
28 Nelson, Daniel B. 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, vol. 59, no. 2, pp. 347-370. 
29 Palm, Franz C., and Peter J. G. Vlaar. 1997. "Simple Diagnostic Procedures for Modeling Financial Time Series," Allgemeines Statistisches Archiv, vol. 81, pp. 85-101. 
30 Poon, Ser-Huang, and Clive W. J. Granger. 2003. "Forecasting Volatility in Financial Markets, A Review," Journal of Economic Literature, vol. 41, no. 2, pp. 478-539. 
31 Premaratne, Gamini, and Anil K. Bera. 2005. "Modeling Asymmetry and Excess Kurtosis in Stock Return Data," Paper presented at Econometric Society 2005 World Congress, University College London. 
32 RiskMetrics Group. 1996. RiskMetrics-Technical Document (4th Edition). New York: J. P. Morgan/Reuters. 
33 Smith, Daniel R. 2006. "Conditional Coskewness and Asset Pricing," Journal of Empirical Finance, vol. 14, no. 1, pp. 91-119. 
34 So, Mike K. P., and Philip L. H. Yu. 2006. "Empirical Analysis of GARCH Models in Value at Risk Estimation," Journal of International Financial Markets, Institutions and Money, vol. 16, no. 2, pp. 180-197. 
35 Tang, Ta-Lun, and Shwu-Jane Shieh. 2006. "Long Memory in Stock Index Futures Markets: A Value-at-Risk Approach," Physica A, vol. 366, pp. 437-448. 
36 Theodossiou, P. 1998. "Financial Data and the Skewed Generalized T Distribution," Management Science, vol. 44, no. 2, pp. 1650-1661. 
37 Tse, Y. K. 1998. "The Conditional Heteroscedasticity of the Yen-Dollar Exchange Rate," Journal of Applied Econometrics, vol. 13, no. 1, pp. 49-55. 
38 Wu, Ping-Tsung and Shwu-Jane Shieh. 2007. "Value-at-Risk Analysis for Long-term Interest Rate Futures, Fat-tail and Long Memory in Return Innovations," Journal of Empirical Finance, vol. 14, no. 2, pp. 248-259.