Forecasting volatility with interacting multiple models

Jiri Svec and Xerxis Katrak

Finance Research Letters, 2017, vol. 20, issue C, 245-252

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.

Keywords: Forecasting; Volatility; Kalman filter; Interacting multiple models (search for similar items in EconPapers)
JEL-codes: C53 G17 (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:20:y:2017:i:c:p:245-252

DOI: 10.1016/j.frl.2016.10.005

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