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Forecasting Realized Volatility: A Bayesian Model Averaging Approach

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  • Chun Liu
  • John M Maheu

Abstract

How to measure and model volatility is an important issue in finance. Recent research uses high frequency intraday data to construct ex post measures of daily volatility. This paper uses a Bayesian model averaging approach to forecast realized volatility. Candidate models include autoregressive and heterogeneous autoregressive (HAR) specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and an asymmetric term. Applied to equity and exchange rate volatility over several forecast horizons, Bayesian model averaging provides very competitive density forecasts and modest improvements in point forecasts compared to benchmark models. We discuss the reasons for this, including the importance of using realized power variation as a predictor. Bayesian model averaging provides further improvements to density forecasts when we move away from linear models and average over specifications that allow for GARCH effects in the innovations to log-volatility.

Suggested Citation

  • Chun Liu & John M Maheu, 2008. "Forecasting Realized Volatility: A Bayesian Model Averaging Approach," Working Papers tecipa-313, University of Toronto, Department of Economics.
    • Handle: RePEc:tor:tecipa:tecipa-313
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    More about this item

    Keywords

    power variation; bipower variation; Gibbs sampling; model risk;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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