The long memory HEAVY process: modeling and forecasting financial volatility

M. Karanasos (), S. Yfanti and A. Christopoulos
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M. Karanasos: Brunel University London
S. Yfanti: Loughborough University
A. Christopoulos: National and Kapodistrian University of Athens

Annals of Operations Research, 2021, vol. 306, issue 1, No 6, 130 pages

Abstract: Abstract This paper studies the bivariate HEAVY system of volatility regression equations and its various extensions that are directly applicable to the day-to-day business treasury operations of trading in foreign exchange and commodities, investing in bond and stock markets, hedging out market risk, and capital budgeting. We enrich the HEAVY framework with powers, asymmetries, and long memory that improve its forecasting accuracy significantly. Other findings are as follows. First, hyperbolic memory fits the realized measure better, whereas fractional integration is more suitable for the powered returns. Second, the structural breaks applied to the bivariate system capture the time-varying behavior of the parameters, in particular during and after the global financial crisis of 2007/2008.

Keywords: Asymmetries; Financial crisis; Forecasting; HEAVY model; High-frequency data; Long memory; Power transformations; Realized variance; Risk management; Structural breaks (search for similar items in EconPapers)
JEL-codes: C22 C52 C58 G01 G15 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10479-019-03493-8

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