A Generalized Heterogeneous Autoregressive Model using the Market Index
47 Pages Posted: 18 Dec 2019
Date Written: December 2, 2019
This paper shows that generalizing the heterogeneous autoregressive model (HAR) with realized (co)variances and semi-(co)variances from the index leads to more accurate volatility forecasts. To circumvent the effects of the market microstructure noise arising from using high sampling frequencies, we adopt noise-robust estimators for the realized (co)variances and develop novel noise-robust estimators for the semi-(co)variances. To explore the sampling frequency at which the forecasting gains are maximized, we adopt a mixed-sampling approach that iterates over several sampling frequencies of the stock and the index. Our analysis shows that gains are maximized at the combination of a low (high) frequency on the stock (index). We illustrate that the observed forecasting gains translates into economic gains such that a risk-averse investor is willing to pay up to 57 annual basis points by adopting a model specification that utilizes the index information.
Keywords: Realized Volatility, Micro-structure Noise, Pre-Averaged Estimators, Semi-variances, Semicovariances, Volatility Forecasting, Economic Value, Volatility-Timing Strategy
JEL Classification: C22, C51, C53, C58, G11, G14
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