Value at Risk Estimation in a Mixture Normality Framework

Riccardo Bramante; Diego Zappa

Value at Risk Estimation in a Mixture Normality Framework

Riccardo Bramante, Diego Zappa

Risultato della ricerca: Contributo in libro › Contributo a convegno

Abstract

Value at Risk (VaR) has emerged as a useful tool to risk management. A relevant driving force has been the diffusion as a benchmark of JP Morgan RiskMetricsTM methodology and the subsequent BIS adoption of VaR for all trading portfolios of financial institutions. In this paper we analyze the use of mixture of truncated normal distributions in VaR modelling along with an optimization algorithm to identify the optimal thresholds. The approach gives evidence to capture the extreme tails much better than the standard VaR RiskMetricsTM method completely maintaining local normality properties in the model. Simulation results applied to international equity portfolios are presented

Lingua originale	English
Titolo della pubblicazione ospite	Proceedings of the Eighteenth International Conference “Forecasting Financial Markets - Advances for Exchange Rates, Interest Rates and Asset Management”
Pagine	1-18
Numero di pagine	18
Stato di pubblicazione	Pubblicato - 2011
Evento	Forecasting Financial Markets - Advances for Exchange Rates, Interest Rates and Asset Management - Marsiglia Durata: 25 mag 2011 → 27 mag 2011

Convegno

Convegno	Forecasting Financial Markets - Advances for Exchange Rates, Interest Rates and Asset Management
Città	Marsiglia
Periodo	25/5/11 → 27/5/11

Keywords

mixtures of distributions
tail probability
truncated normal
value at risk

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title = "Value at Risk Estimation in a Mixture Normality Framework",

abstract = "Value at Risk (VaR) has emerged as a useful tool to risk management. A relevant driving force has been the diffusion as a benchmark of JP Morgan RiskMetricsTM methodology and the subsequent BIS adoption of VaR for all trading portfolios of financial institutions. In this paper we analyze the use of mixture of truncated normal distributions in VaR modelling along with an optimization algorithm to identify the optimal thresholds. The approach gives evidence to capture the extreme tails much better than the standard VaR RiskMetricsTM method completely maintaining local normality properties in the model. Simulation results applied to international equity portfolios are presented",

keywords = "mixtures of distributions, tail probability, truncated normal, value at risk, mixtures of distributions, tail probability, truncated normal, value at risk",

author = "Riccardo Bramante and Diego Zappa",

year = "2011",

language = "English",

pages = "1--18",

booktitle = "Proceedings of the Eighteenth International Conference “Forecasting Financial Markets - Advances for Exchange Rates, Interest Rates and Asset Management”",

note = "Forecasting Financial Markets - Advances for Exchange Rates, Interest Rates and Asset Management ; Conference date: 25-05-2011 Through 27-05-2011",

}

Bramante, R & Zappa, D 2011, Value at Risk Estimation in a Mixture Normality Framework. in Proceedings of the Eighteenth International Conference “Forecasting Financial Markets - Advances for Exchange Rates, Interest Rates and Asset Management”. pagg. 1-18, Forecasting Financial Markets - Advances for Exchange Rates, Interest Rates and Asset Management, Marsiglia, 25/5/11.

TY - GEN

T1 - Value at Risk Estimation in a Mixture Normality Framework

AU - Bramante, Riccardo

AU - Zappa, Diego

PY - 2011

Y1 - 2011

N2 - Value at Risk (VaR) has emerged as a useful tool to risk management. A relevant driving force has been the diffusion as a benchmark of JP Morgan RiskMetricsTM methodology and the subsequent BIS adoption of VaR for all trading portfolios of financial institutions. In this paper we analyze the use of mixture of truncated normal distributions in VaR modelling along with an optimization algorithm to identify the optimal thresholds. The approach gives evidence to capture the extreme tails much better than the standard VaR RiskMetricsTM method completely maintaining local normality properties in the model. Simulation results applied to international equity portfolios are presented

AB - Value at Risk (VaR) has emerged as a useful tool to risk management. A relevant driving force has been the diffusion as a benchmark of JP Morgan RiskMetricsTM methodology and the subsequent BIS adoption of VaR for all trading portfolios of financial institutions. In this paper we analyze the use of mixture of truncated normal distributions in VaR modelling along with an optimization algorithm to identify the optimal thresholds. The approach gives evidence to capture the extreme tails much better than the standard VaR RiskMetricsTM method completely maintaining local normality properties in the model. Simulation results applied to international equity portfolios are presented

KW - mixtures of distributions

KW - tail probability

KW - truncated normal

KW - value at risk

KW - mixtures of distributions

KW - tail probability

KW - truncated normal

KW - value at risk

UR - http://hdl.handle.net/10807/10208

M3 - Conference contribution

SP - 1

EP - 18

BT - Proceedings of the Eighteenth International Conference “Forecasting Financial Markets - Advances for Exchange Rates, Interest Rates and Asset Management”

T2 - Forecasting Financial Markets - Advances for Exchange Rates, Interest Rates and Asset Management

Y2 - 25 May 2011 through 27 May 2011

ER -

Value at Risk Estimation in a Mixture Normality Framework

Abstract

Convegno

Keywords

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