Volatility forecasting: combinations of realized volatility measures and forecasting models

ABSTRACT

This article examines financial time series volatility forecasting performance. Different from other studies which either focus on combining individual realized measures or combining forecasting models, we consider both. Specifically, we construct nine important individual realized measures and consider combinations including the mean, the median and the geometric means as well as an optimal combination. We also apply a simple AR(1) model, an SV model with contemporaneous dependence, an HAR model and three linear combinations of these models. Using the robust forecasting evaluation measures including RMSE and QLIKE, our empirical evidence from both equity market indices and exchange rates suggests that combinations of both volatility measures and forecasting models improve the forecast performance significantly.

KEYWORDS:

• Volatility forecasting
• volatility measures
• forecasting models
• combination
• robust evaluation

JEL CLASSIFICATION:

• G15
• C10
• C13
• C53

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ We also reestimate the models by adding one observation each time; the results are similar to those using rolling samples. Due to space limit, we only report the results using rolling samples.