Are There Structural Breaks in Realized Volatility?

Chong Liu · John M. Maheu

86 citations0/0 files storedJournal of Financial Econometrics

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Market Dynamics and Volatility

Abstract

Constructed from high-frequency data, realized volatility (RV) provides an efficient estimate of the unobserved volatility of financial markets. This paper uses a Bayesian approach to investigate the evidence for structural breaks in reduced form time-series models of RV. We focus on the popular heterogeneous autoregressive (HAR) models of the logarithm of realized volatility. Using Monte Carlo simulations we demonstrate that our estimation approach is effective in identifying and dating structural breaks. Applied to daily S&P 500 data from 1993-2004, we find strong evidence of a structural break in early 1997. The main effect of the break is a reduction in the variance of log-volatility. The evidence of a break is robust to different models including a GARCH specification for the conditional variance of log(RV).

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References

1991Conditional Heteroskedasticity in Asset Returns: A New Approach 1989A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle 2003Modeling and Forecasting Realized Volatility 1998Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts 2001The distribution of realized stock return volatility 2002Econometric Analysis of Realized Volatility and its Use in Estimating Stochastic Volatility Models 2004Power and Bipower Variation with Stochastic Volatility and Jumps 2007Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility

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