Value at Risk has emerged as a useful tool to risk management. A relevant driving force has been the diffusion of JP Morgan RiskMetrics methodology and the subsequent BIS adoption for all trading portfolios of financial institutions. To improve the accuracy of VaR estimates in this paper we propose the use of mixture of truncated normal distributions in modelling returns. An optimization algorithm has been developed to obtain the best fit by using the minimum distance approach. Results show evidence to fit return distributions at a satisfactory level, completely maintaining local normality properties in the model.
Bramante, R., Zappa, D., Fitting Financial Returns Distributions: A Mixture Normality Approach ., in Aa.Vv, A. (ed.), Mathematical and Statistical Methods for Actuarial Sciencesand Finance, Springer, Heidelberg 2014: 81- 88. 10.1007/978-3-319-02499-8_7 [http://hdl.handle.net/10807/61433]
Fitting Financial Returns Distributions: A Mixture Normality Approach .
Bramante, Riccardo;Zappa, Diego
2014
Abstract
Value at Risk has emerged as a useful tool to risk management. A relevant driving force has been the diffusion of JP Morgan RiskMetrics methodology and the subsequent BIS adoption for all trading portfolios of financial institutions. To improve the accuracy of VaR estimates in this paper we propose the use of mixture of truncated normal distributions in modelling returns. An optimization algorithm has been developed to obtain the best fit by using the minimum distance approach. Results show evidence to fit return distributions at a satisfactory level, completely maintaining local normality properties in the model.
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