Recent evidence suggests option implied volatilities provide better forecasts of financial volatility than time-series models based on historical daily returns. In this study both the measurement and the forecasting of financial volatility is improved using high-frequency data and long memory modeling, the latest proposed method to model volatility. This is the first study to extract results for three separate asset classes, equity, foreign exchange, and commodities. The results for the S&P 500, YEN/USD, and Light, Sweet Crude Oil provide a robust indication that volatility forecasts based on historical intraday returns do provide good volatility forecasts that can compete with and even outperform implied volatility.

doi.org/10.1002/fut.20126, hdl.handle.net/1765/56691
The Journal of Futures Markets
Erasmus Research Institute of Management

Martens, M., & Zein, J. (2004). Predicting financial volatility: High-frequency time-series forecasts vis-à-vis implied volatility. The Journal of Futures Markets, 24(11), 1005–1028. doi:10.1002/fut.20126