Department of Economics and Business Economics

Combining long memory and level shifts in modelling and forecasting the volatility of asset returns

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We propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors. The Kalman filter is used to construct the state-augmented likelihood function and subsequently to generate forecasts, which are mean and path-corrected. We apply our model to eight daily volatility series constructed from both high-frequency and daily returns. Full sample parameter estimates reveal that random level shifts are present in all series. Genuine long memory is present in most high-frequency measures of volatility, whereas there is little remaining dynamics in the volatility measures constructed using daily returns. From extensive forecast evaluations, we find that our ARFIMA model with random level shifts consistently belongs to the 10% Model Confidence Set across a variety of forecast horizons, asset classes and volatility measures. The gains in forecast accuracy can be very pronounced, especially at longer horizons.

Original languageEnglish
JournalQuantitative Finance
Pages (from-to)371–393
Number of pages23
Publication statusPublished - 2018

    Research areas

  • ARFIMA, C13, C22, C53, Forecasting, INFERENCE, INFLATION, Kalman filter, Long memory processes, NONSTATIONARITIES, PARAMETER, PREDICTION, REALIZED VOLATILITY, State space modelling, Stochastic volatility, Structural change, TIME-SERIES MODELS, TRENDS, VARIANCE

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