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Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns

Rasmus Varneskov () and Pierre Perron
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Rasmus Varneskov: Aarhus University and CREATES

No WP2017-006, Boston University - Department of Economics - Working Papers Series from Boston University - Department of Economics

Abstract: 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.

Keywords: Forecasting; Kalman Filter; Long Memory Processes; State Space Modeling; Stochastic Volatility; Structural Change. (search for similar items in EconPapers)
JEL-codes: C13 C22 C53 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2017-01
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Related works:
Journal Article: Combining long memory and level shifts in modelling and forecasting the volatility of asset returns (2018) Downloads
Working Paper: Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns (2015) Downloads
Working Paper: Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns (2011) Downloads
Working Paper: Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns (2011)
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