Forecasting Realized Volatility: A Bayesian Model Averaging Approach
Chun Liu () and
John Maheu ()
Working Papers from University of Toronto, Department of Economics
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.
Keywords: power variation; bipower variation; Gibbs sampling; model risk (search for similar items in EconPapers)
JEL-codes: C11 C22 G12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets, nep-for, nep-mst and nep-rmg
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Journal Article: Forecasting realized volatility: a Bayesian model-averaging approach (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:tor:tecipa:tecipa-313
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