Abstract
AbstractWe forecast realized volatility extending the heterogeneous autoregressive model (HAR) to include implied volatility (IV), the leverage effect, overnight returns, and the volatility of realized volatility. We analyze 10 international stock indices finding that, although a simple HAR model augmented with IV (HAR‐IV) is more accurate than any HAR model excluding it, all markets support further extensions of the HAR‐IV model. More accurate forecasts are found using overnight returns in all markets except the UK, the volatility of realized volatility in the US, and the leverage effect in five markets. A value‐at‐risk exercise supports the economic significance of our findings.
Highlights
Volatility plays an important role in both theoretical and empirical finance
Comparing the forecast error of the heterogeneous autoregressive (HAR)-IV with more sophisticated models that include implied volatility, and leverage effects, overnight returns and/or the volatility of realized volatility we find that the latter are marginally favored across all indices
This paper examines the role of implied volatility, leverage effect, overnight returns and volatility of realized volatility in forecasting future realized volatility in 10 international stock markets
Summary
Volatility plays an important role in both theoretical and empirical finance. Since volatility is a key input in asset pricing, derivative pricing and risk management, forecasting volatility is a critical task. The availability of high-frequency data has enabled the development of non-parametric daily estimators of volatility known as realized volatility (RV) based on the sum of squared intraday returns (Andersen & Bollerslev, 1998). Empirical results in Andersen et al (2003) clearly show that reduced form time series models for RV outperform the popular GARCH and related stochastic volatility models in forecasting future volatility.. Several studies have suggested the importance of explicitly incorporating implied volatility (IV) in the traditional GARCH and stochastic volatility models for the purpose of forecasting. It appears that IV contains additional information about future volatility beyond that captured in model based volatility forecasts.. It appears that IV contains additional information about future volatility beyond that captured in model based volatility forecasts. There are a few studies that suggest that IV forecasts outperform GARCH based forecasts, suggesting that IV forecasts are strong at least when the alternative is historical models based only on lower frequency returns. little attention has been paid on whether extracting information from option prices is useful in forecasting realized volatility
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