Modeling and Forecasting Volatility of the Malaysian Stock Markets

Zaidi Isa,Ahmed Shamiri

doi:10.3844/jmssp.2009.234.240

Abstract

Problem statement: One of the main purposes of modeling variance is forecasting, which is crucial in many areas of finance. Despite the burgeoning interest in and evaluation of volatility forecasts, a clear consensus on witch volatility model/or distribution specification to use has not yet been reached. Therefore, the out of-sample forecasting ability should be a natural model selection criterion for volatility models. Approach: In this study, we used high-frequency to facilitate meaningful comparison of volatility forecast models. We compared the performance of symmetric GARCH, asymmetric EGARCH and non leaner asymmetric NAGARCH models with six error distributions (normal, skew normal, student-t, skew student-t, generalized error distribution and normal inverse Gaussian). Results: The results suggested that allowing for a heavy-tailed error distribution leads to significant improvements in variance forecasts compared to using normal distribution. It was also found that allowing for skewness in the higher moments of the distribution did not further improve forecasts. Conclusion: Successful volatility model forecast depended much more heavily on the choice of error distribution than the choice of GARCH models.

Highlights

Traditional regression tools have shown their limitation in the modeling of high-frequency data
GARCH: Bollerslev[3],introduced GARCH model known as the Generalized auto-regressive conditional heteroskedasticity model which suggest that the timevarying volatility process is a function of both past disturbances and past volatility
The comparison between models with each density shows that, according to the different measures used for modeling the volatility, the exponential GARCH model (EGARCH) model with skew-student-t provides the best in-sample estimation for Kuala Lumpur Composite Index (KLCI) compared to all other volatility models and distributions

Summary

Introduction

Traditional regression tools have shown their limitation in the modeling of high-frequency (weekly, daily or intra-daily) data. Assuming that only the mean response could be changing with covariates while the variance remains constant over time often revealed to be an unrealistic assumption in practice. This fact is obvious in series of financial data where clusters of volatility can be detected visually. The vast majority of variance forecasting articles have used squared daily returns as the proxy for ex post variance. This is, as shown by Andersen and Bollerslev[2], an unbiased but exceedingly noisy estimator

Methods

Discussion

Conclusion