Extreme Value Modelling for Measuring Financial Risk with Application to Selected Philippine Stocks

Journal of Applied & Computational Mathematics

article

Velasco AAF¹  Lapuz DKP¹ 

[1] Department of Mathematics, DE La Salle University

关键词: Extreme value theory;    Generalized Pareto distribution;    Peaks over threshold;    Value at risk;    Conditional value at risk;    Returnlevel;    Backtesting;   

DOI  :  10.4172/2168-9679.1000404

来源: Hilaris Publisher

【 摘 要 】

Extreme value theory (EVT) provides techniques for estimating models that predict events occurring at extremelylow probabilities. In this paper, Peaks Over Threshold (POT) method of Extreme Value Theory was utilized. Aconditional approach of the EVT was applied with the aid of ARMA-GARCH models to correct for the effects ofautocorrelation and conditional heteroscedastic terms. Maximum likelihood estimates of model parameters for thefitted Generalized Pareto Distribution (GPD) were computed. These techniques were applied to the daily returns ofBangko de Oro, Mega World Corporation, Semirara Mining and Power Corporation, SM Investments Corporation,and Universal Robina Corporation. A comparison of value at risk (VaR) estimates showed that as becomes smaller,VaR estimates under normal distribution tend to underestimate VaR while estimates under EVT approaches theempirical results. Backtesting using the Basel Committee three-zone approach to assess the accuracy of VaRmodels reveal that VaR models under normality are not able to capture extreme returns and therefore underestimatetail risk while VaR models under EVT have high probability of model accuracy.

【 授权许可】

Unknown