Predicting financial volatility: High‐frequency time‐series forecasts vis‐à‐vis implied volatility

TLDR

Option implied volatilities are reported to forecast financial volatility better than historical daily return‑based time‑series models. The study aims to improve measurement and forecasting of financial volatility using high‑frequency data and long‑memory modeling. High‑frequency intraday returns and long‑memory volatility models are employed to generate forecasts. Forecasts derived from intraday returns outperform implied volatility across the S&P 500, YEN/USD, and Light Sweet Crude Oil, demonstrating the method’s effectiveness for equities, FX, and commodities. © 2004 Wiley Periodicals, Inc., Jrl Fut Mark 24:1005–1028.

Abstract

Abstract 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. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:1005–1028, 2004