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

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.

Asymmetric Impact of Investor Sentiment on Brazilian Stock Market Volatility

“…The most common ways to estimate volatility in finance are through the models of: 1. conditional volatility (Generalized Autoregressive Conditional Heteroskedasticity -Garch); 2. stochastic volatility; 3. implied volatility; and 4. direct measures, such as realized volatility; among these models, Garchtype approaches are the most used, since they have easy estimation, precision in modeling and have flexible adaptation in relation to volatility dynamics over time (Maciel & Ballini, 2017). In this sense, we obtained the conditional volatility by the estimation of Garch (1.1) based on the time series of the IBRX100 daily historical returns, as in equations 1 and 2: in which r t = ln(P t ) -ln (P t-1 ) is the logarithmic return in t; P t is the price of the asset in t; ε t ~ i.i.d.…”

A Comparative Analysis on Probability of Volatility Clusters on Cryptocurrencies, and FOREX Currencies

“…Total volatility links react dynamically to changes and growth in global economic fundamentals during moments of crisis. (Maciel and Ballini 2017) introduced the range-based modelling of volatility to define and estimate return-based models of conditional volatility. It involves the inclusion of the range calculation defined as the distance among the maximum and the leased asset's prices within a time frame as an exogenous variable in General Autoregressive Conditional Heteroscedasticity (GARCH) models.…”

Does corporate governance matter for stock returns volatility in the Brazilian context?

“…Engle et al (2013) report that in the literature GARCH models are most often used for estimate volatility and forecasting. This kind of model is well known due to its accuracy and treatment of financial results and stylized facts modeling as volatility clustering and autocorrelation (Maciel and Ballini, 2017). Thus, to measure the behavior and the term structure of the volatility it was used auto regressive models with conditional heteroskedasticity, the GARCH models.…”