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Bivariate GARCH models for single asset returns

Author

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  • Tomasz Skoczylas

    (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.

Suggested Citation

  • Tomasz Skoczylas, 2015. "Bivariate GARCH models for single asset returns," Working Papers 2015-03, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2015-03
    as

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    File URL: http://www.wne.uw.edu.pl/index.php/download_file/1487/
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    bivariate volatility models; joint distribution; range-based volatility estimators; Garman-Klass estimator; observed volatility; volatility modelling; GARCH; leverage; Value-at-Risk; volatility forecasting;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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