Measurement conditional value at risk based on FIGARCH-EVT method at Tehran stock Exchange

Subject Areas : Financial engineering

mohammadreza Lotfalipour ¹ , Mahdiyeh Nosrati ² , abolfazl Ghadiri Moghaddam ³ , Mahdi Filsaraei ⁴

1 - Professor, Department of Economics, Non-Profit Institute of Hakiman, Bojnourd, Iran
2 - Graduate Student, Accounting, Non-Profit Institute Hakiman Bojnourd, Iran
3 - Associate Professor, Accounting Department, Non-profit Institute of Hakiman Bojnourd, Iran
4 - Ph.D Student, Accounting, Islamic Azad University of Bojnourd, Iran

Received: 2016-10-22 Accepted : 2017-01-17 Published : 2017-06-22

Keywords: Portfolio, Conditional Value at Risk, Exterem value theory, FIGARCH-EVT,

Abstract :

An important factor in risk management is optimized conditional value at risk (CVaR) of the portfolio. Choose a model which calculates time depended to variance rather than the model with constant variance lead to improve data modeling. Using an appropriated method for measuring risk in financial asset returns distribution has a great utility. The main purpose of this study is implementing a hybrid procedure to calculate CVaR which, models, volatility and dynamics in clusters, and calculates CVaR value based on fat tail feature. In this case, using Extreme value theory (EVT) leads to calculate CVaR more precisely. In addition to, using some ARCH (autoregressive conditional heteroskedasticity) family models result to dynamic feature in estimating CVaR. Data were used in this study related to TEDPIX during 2001-2015. Total 2781 data were derived from Rahavard Novinand & TseCline softwares as daily. For analysis this TEDPIX data, MATLAB software and EXCELL were used. This result represented, return data distribution has fat tail. The historical simulation (HS) at 95% confidence level isn’t accurate, while the accuracy Generalized Auto-Regressive Conditional Heteroskedasticity-EVT (GARCH-EVT) model at 95% is more suitable. Using (Fractionally integrated generalized autoregressive conditional heteroskedasticity -EVT) FIGARCH-EVT method leads accurate estimates of CVaR in comparison with HS procedure. Calculating CVaR by FIGARCH-EVT-CVaR was more accurate than the GARCH-EVT-CVaR. This model has considered to both GARCH-EVT features and long memory property. The FIGARCH-EVT-CVaR model had acceptable accuracy and its exceptions are independent. In General, models which considered heteroscedastic, had an acceptable accuracy in comparing HS