Risk Forecasting of Karachi Stock Exchange: A Comparison of Classical and Bayesian GARCH Models

Farhat Iqbal

Abstract


This paper is concerned with the estimation, forecasting and evaluation of Value-at-Risk (VaR) of Karachi Stock Exchange before and after the global financial crisis of 2008 using Bayesian method. The generalized autoregressive conditional heteroscedastic (GARCH) models under the assumption of normal and heavy-tailed errors are used to forecast one-day-ahead risk estimates. Various measures and backtesting methods are employed to evaluate VaR forecasts. The observed number of VaR violations using Bayesian method is found close to the expected number of violations. The losses are also found smaller than the competing Maximum Likelihood method. The results showed that the Bayesian method produce accurate and reliable VaR forecasts and can be preferred over other methods. 


Keywords


GARCH, Volatility, Value-at-Risk, MCMC

Full Text:

PDF


DOI: http://dx.doi.org/10.18187/pjsor.v12i3.1136

Refbacks

  • There are currently no refbacks.




Copyright (c) 2016 Pakistan Journal of Statistics and Operation Research

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Title

Risk Forecasting of Karachi Stock Exchange: A Comparison of Classical and Bayesian GARCH Models

Keywords

GARCH, Volatility, Value-at-Risk, MCMC

Description

This paper is concerned with the estimation, forecasting and evaluation of Value-at-Risk (VaR) of Karachi Stock Exchange before and after the global financial crisis of 2008 using Bayesian method. The generalized autoregressive conditional heteroscedastic (GARCH) models under the assumption of normal and heavy-tailed errors are used to forecast one-day-ahead risk estimates. Various measures and backtesting methods are employed to evaluate VaR forecasts. The observed number of VaR violations using Bayesian method is found close to the expected number of violations. The losses are also found smaller than the competing Maximum Likelihood method. The results showed that the Bayesian method produce accurate and reliable VaR forecasts and can be preferred over other methods. 


Date

2016-09-01

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 12 No. 3, 2016



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810