Fitting financial time series returns distributions: a mixture normality approach

Value at Risk has emerged as a useful tool to risk management. A relevant driving force has been the diffusion of JP Morgan RiskMetricsTM 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