Market risk forecasting for high dimensional portfolios via factor copulas with GAS dynamics |
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| Institution: | 1. Cass Business School, City, University of London, Faculty of Finance, EC1Y 8TZ, London, UK;2. Università degli Studi del Piemonte Orientale, Dipartimento SEI, I-28100 Novara, Italy;3. Politecnico di Milano, Dipartimento di Matematica, I-20133 Milano, Italy;1. Department of Industrial and Systems Engineering, University of Florida, Gainesville FL 32611, USA;2. School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta GA 30332-0205, USA;1. Department of Economics, Universidade de Santiago de Compostela, Spain;2. Dipartimento di Statistica, Informatica, Applicazioni “G. Parenti”, University of Florence, Italy |
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| Abstract: | In this paper we propose forecasting market risk measures, such as Value at Risk (VaR) and Expected Shortfall (ES), for large dimensional portfolios via copula modeling. For that we compare several high dimensional copula models, from naive ones to complex factor copulas, which are able to simultaneously tackle the curse of dimensionality and introduce a high level of complexity into the model. We explore both static and dynamic copula fitting. In the dynamic case we allow different levels of flexibility for the dependence parameters which are driven by a GAS (Generalized Autoregressive Scores) model, in the spirit of Oh and Patton (2015). Our empirical results, for assets negotiated at Brazilian BOVESPA stock market from January, 2008 to December, 2014, suggest that, compared to the other copula models, the GAS dynamic factor copula approach has a superior performance in terms of AIC (Akaike Information Criterion) and a non-inferior performance with respect to VaR and ES forecasting. |
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| Keywords: | Dynamic factor copula GAS Value at Risk Expected Shortfall Forecasting |
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