An Empirical Investigation of Value at Risk (VaR) Forecasting Based on Range-Based Conditional Volatility Models


  • Lakshmi Padmakumari IFMR GSB, Krea University, India
  • Muneer Shaik School of Management, Mahindra University, India



Value-at-Risk (VaR), Volatility models, Range-based estimators, Backtesting, Forecasting


Value at Risk (VaR) is a widely used measure of market risk. Precision in the estimation of volatility leads to accurate VaR forecasts. As volatility is time-varying and has a clustering effect, GARCH class of volatility models is helpful in modeling volatility more precisely. Studies have also shown that range-based volatility estimates are more efficient than traditional models that use only closing prices. Therefore, this study uses the GARCH family of volatility models to model and forecast VaR. The study compares conventional models that use closing prices alone, like GARCH and TARCH, with range-based models like RGARCH and RTARCH models, where the range defined as daily high price minus low price is introduced as an exogenous variable, to explore if the latter provides better predictive accuracy. All the models are back-tested using the Kupiec (1995) unconditional coverage and Christoffersen (1998) conditional coverage tests. The data period in the study ranges from 2003-2021, and we consider five BRICS indices and three major developed economies, namely, the USA, the UK, and Germany. An empirical investigation shows that range-based models do a better job in VaR forecasting as it has more information content than daily closing prices, thereby giving more accurate VaR estimates. The study hopes this finding will greatly help stakeholders like financial institutions, regulators, and practitioners in more effective market risk management.

Author Biographies

Lakshmi Padmakumari, IFMR GSB, Krea University, India

Lakshmi Padmakumari, Dr., is currently serving as an Assistant Professor at IFMR Graduate School of Business, Krea University, India. She holds PhD in Finance, PGDBA (Finance) and also master’s and bachelor’s in commerce (Taxation Law and Practice). Her research interests include asset pricing, volatility estimation, risk management, corporate governance and financial markets.

Muneer Shaik, School of Management, Mahindra University, India

Muneer Shaik, Dr., is currently working as Assistant Professor at Mahindra University, School of Management. Prior to that he has teaching experience in the area of Quantitative Finance and Data Science area at IFMR Graduate School of Business, Krea University, India. He has industry experience with J.P.Morgan in a managerial position in the equity and fixed income department. He holds PhD in Economics-Finance (Interdisciplinary), Masters in Economics and Bachelors in Mechanical Engineering. He has participated and presented his research papers at multiple conferences both in India and abroad. Some of his research papers have won the best paper awards.

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