Stochastic Volatility Model with Burr Distribution Error: Evidence from Australian Stock Returns

Authors

  • Gopalan Nair School of Mathematics and Statistics, The University of Western Australia, Perth, Australia
  • Khreshna Syuhada Statistics Research Group, Institut Teknologi Bandung, Bandung, Indonesia

Keywords:

Autoregressive process, Burr distribution, time series forecasting

Abstract

The Stochastic Volatility (SV) models have been extensively used as alternative models to the well known ARCH and GARCH models in order to represent the volatility behavior in financial return series. In this paper, we study the SV models with error distribution following a class of thick-tailed distributions, called Mode-Centered Burr distribution, in the place of Normal distribution. Through empirical analysis on Australian stock returns data we illustrate that the SV model with error as Mode-Center Burr distribution is more appropriate than the basic SV model. Furthermore, an extension of the basic SV model is investigated, in the direction of allowing the volatility to follow a second-order autoregressive process. Properties of this model such as the kurtosis and autocorrelation function are derived.

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Published

2016-01-01

Issue

Section

Articles