Forecasting volatility with interacting multiple models

Abstract

We examine the performance of Kalman filter techniques in forecasting volatility. We find that the simple implementation of an online Kalman filtering procedure that combines commonly used forecasting models with market-based estimates improves the accuracy of volatility forecasts. Furthermore, we demonstrate that the Interacting Multiple Model algorithm, which combines multiple Kalman filters, provides the most accurate volatility forecasts overall.