scholarly journals The Risk Analysis and Modeling of Byco Petroleum in Pakistan Using Extreme Value Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zishan Ali Syed ◽  
Mohammad Mohammad Ahmed Almazah ◽  
Zahid Iqbal ◽  
Ghulam Raza Khan
Keyword(s):  
Extreme Value Theory ◽  
Value At Risk ◽  
Goodness Of Fit ◽  
Stock Exchange ◽  
Probability Distributions ◽  
Value Theory ◽  
Extreme Value ◽  
Goodness Of Fit Test ◽  
Traditional Methods ◽  
Petroleum Company

The extreme value theory (EVT) has been used to model and measure the distribution of extreme minima of Byco Petroleum in the Pakistan stock market over the period from 2005 to 2012. This paper covers the investigation of distributions that are mostly used in finance including the generalized extreme value (GEV), generalized logistics (GL), and generalized Pareto (GPA) distribution. L-moment ratio diagram is being used to find the appropriate distributions among the distributions. L-moment diagram depicts that GEV and GL distributions are suitable to represent the extremes of Byco Petroleum Pakistan Limited. Thereafter, the probability weighted moment (PWM) method has been used in order to estimate the parameters of probability distributions. Furthermore, Anderson–Darling (AD) goodness-of-fit test is employed to test the goodness of fit among GEV and GL distributions, and it is clear from the results that the GL distribution is more reliable and applicable for extreme minima of Byco Petroleum Company in the Pakistan stock exchange market. EVT and traditional methods are used for value-at-risk (VaR) analysis. The analysis indicates that EVT methods are more suitable for risk measurement in comparison with traditional methods.

scholarly journals Portfolio Tail Risk: A Multivariate Extreme Value Theory Approach

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1425
Author(s):  
Miloš Božović
Keyword(s):  
Extreme Value Theory ◽  
Value At Risk ◽  
Goodness Of Fit ◽  
Value Theory ◽  
Extreme Value ◽  
Theory Approach ◽  
Risk Modeling ◽  
Tail Risk ◽  
Goodness Of Fit Tests ◽  
Level Of Significance

This paper develops a method for assessing portfolio tail risk based on extreme value theory. The technique applies separate estimations of univariate series and allows for closed-form expressions for Value at Risk and Expected Shortfall. Its forecasting ability is tested on a portfolio of U.S. stocks. The in-sample goodness-of-fit tests indicate that the proposed approach is better suited for portfolio risk modeling under extreme market movements than comparable multivariate parametric methods. Backtesting across multiple quantiles demonstrates that the model cannot be rejected at any reasonable level of significance, even when periods of stress are included. Numerical simulations corroborate the empirical results.

scholarly journals Estimating Conditional Value at Risk in the Tehran Stock Exchange Based on the Extreme Value Theory Using GARCH Models

2019 ◽  
Vol 9 (2) ◽  
pp. 40 ◽  
Author(s):  
Hamed Tabasi ◽  
Vahidreza Yousefi ◽  
Jolanta Tamošaitienė ◽  
Foroogh Ghasemi
Keyword(s):  
At Risk ◽  
Distribution Function ◽  
Extreme Value Theory ◽  
Value At Risk ◽  
Stock Exchange ◽  
Value Theory ◽  
Extreme Value ◽  
Likelihood Method ◽  
Conditional Value At Risk ◽  
Garch Models

This paper attempted to calculate the market risk in the Tehran Stock Exchange by estimating the Conditional Value at Risk. Since the Conditional Value at Risk is a tail-related measure, Extreme Value Theory has been utilized to estimate the risk more accurately. Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models were used to model the volatility-clustering feature, and to estimate the parameters of the model, the Maximum Likelihood method was applied. The results of the study showed that in the estimation of model parameters, assuming T-student distribution function gave better results than the Normal distribution function. The Monte Carlo simulation method was used for backtesting the Conditional Value at Risk model, and in the end, the performance of different models, in the estimation of this measure, was compared.

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