Bivariate GARCH models for single asset returns

Tomasz Skoczylas

No 2015-03, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: In this paper an alternative approach to modelling and forecasting single asset returns volatility is presented. A new, bivariate, flexible framework, which may be considered as a development of single-equation ARCH-type models, is proposed. This approach focuses on joint distribution of returns and observed volatility, measured by Garman-Klass variance estimator, and it enables to examine simultaneous dependencies between them. Proposed models are compared with benchmark GARCH and range-based GARCH (RGARCH) models in terms of prediction accuracy. All models are estimated with maximum likelihood method, using time series of EUR/PLN spot rate quotations and WIG20 index. Results are very encouraging especially for foreasting Value-at-Risk. Bivariate models achieved lesser rates of VaR exception, as well as lower coverage tests statistics, without being more conservative than its single-equation counterparts, as their forecasts errors measures are rather similar.

Keywords: bivariate volatility models; joint distribution; range-based volatility estimators; Garman-Klass estimator; observed volatility; volatility modelling; GARCH; leverage; Value-at-Risk; volatility forecasting (search for similar items in EconPapers)
JEL-codes: C13 C32 C53 C58 G10 G17 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2015
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-ore and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2015-03

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