Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

Leandro Dos Santos Maciel,Rosangela Ballini

doi:10.1590/1808-057x201704140

Abstract

ABSTRACT This article considers range-based volatility modeling for identifying and forecasting conditional volatility models based on returns. It suggests the inclusion of range measuring, defined as the difference between the maximum and minimum price of an asset within a time interval, as an exogenous variable in generalized autoregressive conditional heteroscedasticity (GARCH) models. The motivation is evaluating whether range provides additional information to the volatility process (intraday variability) and improves forecasting, when compared to GARCH-type approaches and the conditional autoregressive range (CARR) model. The empirical analysis uses data from the main stock market indexes for the U.S. and Brazilian economies, i.e. S&P 500 and IBOVESPA, respectively, within the period from January 2004 to December 2014. Performance is compared in terms of accuracy, by means of value-at-risk (VaR) modeling and forecasting. The out-of-sample results indicate that range-based volatility models provide more accurate VaR forecasts than GARCH models.

Highlights

Volatility modeling and forecasting play a significant role in derivatives pricing, risk management, portfolio selection, and trading strategies (Leite, Figueiredo Pinto, & Klotzle, 2016)
It is noteworthy for policy makers and regulators, since the volatility dynamics is closely related to stability in financial markets and the economy as a whole
When compared to other methods, the generalized autoregressive conditional heteroscedasticity (GARCH)-type approaches are the most widely used for modeling timevarying conditional volatility, due to their simple form, easy estimation, and flexible adaptation concerning the volatility dynamics

Summary

Introduction

Volatility modeling and forecasting play a significant role in derivatives pricing, risk management, portfolio selection, and trading strategies (Leite, Figueiredo Pinto, & Klotzle, 2016) It is noteworthy for policy makers and regulators, since the volatility dynamics is closely related to stability in financial markets and the economy as a whole. As the GARCH models rely on the moving averages with gradually decaying weights, they are slow to adapt to changing volatility levels (Andersen, Bollerslev, Diebold, & Labys, 2003; Sharma & Vipul, 2016). To overcome this issue, intraday volatility models emerge as alternative tools. Another simple procedure for modeling intraday variation is adopting price range

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Conclusion