Value-at-Risk Analysis for Measuring Stochastic Volatility of Stock Returns: Using GARCH-Based Dynamic Conditional Correlation Model

Fahim Afzal,Asif Mahmood,Pan Haiying,Amir Ikram,Farman Afzal

doi:10.1177/21582440211005758

Abstract

To assess the time-varying dynamics in value-at-risk (VaR) estimation, this study has employed an integrated approach of dynamic conditional correlation (DCC) and generalized autoregressive conditional heteroscedasticity (GARCH) models on daily stock return of the emerging markets. A daily log-returns of three leading indices such as KSE100, KSE30, and KSE-ALL from Pakistan Stock Exchange and SSE180, SSE50 and SSE-Composite from Shanghai Stock Exchange during the period of 2009–2019 are used in DCC-GARCH modeling. Joint DCC parametric results of stock indices show that even in the highly volatile stock markets, the bivariate time-varying DCC model provides better performance than traditional VaR models. Thus, the parametric results in the DCC-GRACH model indicate the effectiveness of the model in the dynamic stock markets. This study is helpful to the stockbrokers and investors to understand the actual behavior of stocks in dynamic markets. Subsequently, the results can also provide better insights into forecasting VaR while considering the combined correlational effect of all stocks.

Highlights

Nowadays, it has been a big challenge for the investors to predict the risk and return associated with the specific index or portfolio in dynamic markets
The dynamic conditional correlation (DCC) model is a much more effective model to address the volatility as the parameters estimated by the DCC model indicate the effectiveness of the model in the selected stock market
The nature of the model is critical in addressing the volatility of any stock market portfolio

Summary

Introduction

It has been a big challenge for the investors to predict the risk and return associated with the specific index or portfolio in dynamic markets. This study suggests that employing dynamic conditional models can capture better volatility of stock returns without any assumptions or problems of underestimation or overestimation of market risk, thereby maximizing the investors’ confidence. To address the appropriation of VaR with time-varying effects in dynamic conditions, one possible solution is to apply the DCC model for volatility measurement as proposed by Engle in 2002 and further modified in 2006. By using a conditional correlational and time-varying effect, this DCC model provides a better estimation of the dynamic correlation structure for capturing the volatilities and forecasting returns more efficiently than other models (Celik, 2012).

Key Findings

Findings

Conclusion