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
Given the weaknesses of the parametric VaR (Value-at-Risk) calculated by normality assumptions, this...
International audienceThe recent financial crisis has highlighted the necessity to introduce mixture...
From option pricing using the Black and Scholes model, to determining the signi cance of regression ...
Value at Risk has emerged as a useful tool to risk management. A relevant driving force has been the...
Value at Risk (VaR) has emerged as a useful tool to risk management. A relevant driving force has b...
This paper provides a selected review of the recent developments and applications of mixtures of nor...
The main subject of this paper is to develop a heuristic method to identify returns distribution of ...
This study investigates the assumption that stock riskiness, captured by the market global beta, is ...
Value-at-Risk (VaR) is a widely used statistical measure in financial risk management for quantifyin...
The normal probability distribution as assumption for financial returns have been recognized as inap...
Most monthly return distributions of alternative assets are in general not normally distributed. Fur...
This paper considers Value at Risk measures constructed under a discrete mixture of normal distribut...
The estimation of asset return distributions is crucial for determining optimal trading strategies. ...
Several two component mixture models from the transformed gamma and transformed beta families are de...
Paper presented at the 5th Strathmore International Mathematics Conference (SIMC 2019), 12 - 16 Augu...
Given the weaknesses of the parametric VaR (Value-at-Risk) calculated by normality assumptions, this...
International audienceThe recent financial crisis has highlighted the necessity to introduce mixture...
From option pricing using the Black and Scholes model, to determining the signi cance of regression ...
Value at Risk has emerged as a useful tool to risk management. A relevant driving force has been the...
Value at Risk (VaR) has emerged as a useful tool to risk management. A relevant driving force has b...
This paper provides a selected review of the recent developments and applications of mixtures of nor...
The main subject of this paper is to develop a heuristic method to identify returns distribution of ...
This study investigates the assumption that stock riskiness, captured by the market global beta, is ...
Value-at-Risk (VaR) is a widely used statistical measure in financial risk management for quantifyin...
The normal probability distribution as assumption for financial returns have been recognized as inap...
Most monthly return distributions of alternative assets are in general not normally distributed. Fur...
This paper considers Value at Risk measures constructed under a discrete mixture of normal distribut...
The estimation of asset return distributions is crucial for determining optimal trading strategies. ...
Several two component mixture models from the transformed gamma and transformed beta families are de...
Paper presented at the 5th Strathmore International Mathematics Conference (SIMC 2019), 12 - 16 Augu...
Given the weaknesses of the parametric VaR (Value-at-Risk) calculated by normality assumptions, this...
International audienceThe recent financial crisis has highlighted the necessity to introduce mixture...
From option pricing using the Black and Scholes model, to determining the signi cance of regression ...