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http://bura.brunel.ac.uk/handle/2438/19955| Title: | The long memory HEAVY process: modeling and forecasting financial volatility |
| Authors: | Karanasos, M Yfanti, S Christopoulos, A |
| Keywords: | asymmetries;structural breaks;risk management;realized variance;power transformations;financial crisis;forecasting;long memory;high-frequency data;HEAVY model |
| Issue Date: | 4-Jan-2020 |
| Publisher: | Springer |
| Citation: | Karanasos, M., Yfanti, S. and Christopoulos, A. (2021) 'The long memory HEAVY process: modeling and forecasting financial volatility', Annals of Operations Research, 306 (1-2), pp. 111 - 130. doi: 10.1007/s10479-019-03493-8. |
| Abstract: | Copyright © The Author(s) 2020. 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. |
| URI: | https://bura.brunel.ac.uk/handle/2438/19955 |
| DOI: | https://doi.org/10.1007/s10479-019-03493-8 |
| ISSN: | 0254-5330 |
| Other Identifiers: | ORCID iD: Menelaos Karanasos https://orcid.org/0000-0001-5442-3509 |
| Appears in Collections: | Dept of Economics and Finance Research Papers |
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