Purpose - In this article, we use filtered extreme value theory model to forecast one of the main emerging market stock returns and compare the predictive performance of this model with other conditional volatility models. Design/methodology/approach – This paper employs eight filtered extreme value theory models created with conditional quantile to estimate value-at-risk for the Istanbul Stock Exchange (ISE). The performances of the filtered extreme value theory models are compared to those of GARCH, GARCH with student-t distribution, GARCH with skewed student-t distribution and FIGARCH by using alternative back-testing algorithms, namely, Kupiec test (1995), Christoffersen test (1998), Lopez test (1999), Diebold and Marino(1995) test, RMSE and h-step ahead forecasting RMSE. Findings - The result indicate that filtered extreme value theory performs better in terms of capturing fat-tails in stock returns than parametric value-at-risk models. An increase in the conditional quantile decreases h-step ahead number of exceptions and this shows that filtered extreme value theory with higher conditional quantile such as 40 days should be used for forward looking forecasting. Originality/value - The research results show that emerging market stock return should be forecasted with filtered extreme value theory and conditional quantile days lag length should also be estimated based on forecasting performance.
Filtered Extreme Value Theory for Value-At-Risk Estimation: Evidence from TurkeyThe Journal of Risk Finance (2010)
Citation InformationSait YILMAZER. "Filtered Extreme Value Theory for Value-At-Risk Estimation: Evidence from Turkey" The Journal of Risk Finance 11.2 (2010): 164-179.