High-low range in GARCH models of stock return volatility

Péter Molnár

71 citations0/0 files storedApplied Economics

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Stock Market Forecasting Methods

Abstract

We suggest a simple and general way to improve the GARCH volatility models using the intraday range between the highest and the lowest price to proxy volatility. We illustrate the method by modifying a GARCH(1,1) model to a range-GARCH(1,1) model. Our empirical analysis conducted on stocks, stock indices and simulated data shows that the range-GARCH(1,1) model performs significantly better than the standard GARCH(1,1) model both in terms of in-sample fit and out-of-sample forecasting ability.

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References

1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1991Conditional Heteroskedasticity in Asset Returns: A New Approach 1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks 1995Comparing Predictive Accuracy 2010Volatility forecast comparison using imperfect volatility proxies 2002Range‐Based Estimation of Stochastic Volatility Models 2006Journal of Applied Econometrics

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