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: Department of Economics and Business, Aarhus University and CREATES, Postal: Bartholins Allé 10, Aarhus, Denmark

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: We propose a parametric state space model with accompanying estimation and forecasting framework that combines long memory and level shifts by decomposing the underlying process into a simple mixture model and ARFIMA dynamics. The Kalman filter is used to construct the likelihood function after augmenting the probability of states by a mixture of normally distributed processes. The forecasts are constructed by exploiting the information in the Kalman recursions. The validity of the estimation methodology is shown through a comprehensive simulation study. Besides being able to identify the true memory of a process, the model consistently belongs to the 10% Model Confidence Set when considering out-of-sample forecasting performance as the only one among four competing dynamic models for all forecasting horizons when applied to high frequency stock- and bond market data together with time series of daily returns on stock market and exchange rate data. As a by-product, we provide simulation and empirical evidence of the "Spurious Break" phenomenon when estimating the number of level shifts in structural models for I(d) processes.

Keywords: Forecasting; Kalman Filter; Long Memory Processes; State Space Modeling; Structural Change. (search for similar items in EconPapers)
JEL-codes: C13 C15 C22 C51 C53 (search for similar items in EconPapers)
Pages: 42
Date: 2011-06-30
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Citations: View citations in EconPapers (4)

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https://repec.econ.au.dk/repec/creates/rp/11/rp11_26.pdf (application/pdf)

Related works:
Journal Article: Combining long memory and level shifts in modelling and forecasting the volatility of asset returns (2018)
Working Paper: Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns (2017)
Working Paper: Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns (2015)
Working Paper: Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns (2011)
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