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.
Keywords: power variation, bipower variation, Gibbs sampling, model risk
JEL Classification: C11; C22; G12