In this research, the VaR and ES measures is estimated and compared for Tehran security exchange (TSE) and international stock markets by using the Conditional EVT based peaks over threshold (POT). In order to obtain independent data, we use a vector autoregressive (VAR) and GARCH model to filter out any serial correlation and heteroskedasticity. Our data are the logarithmic daily returns for the period 2006-2015. We find that the Dubai financial market (DFMG) has the highest VaR and ES for both the lower tail and upper tail. In contrast, the Tehran security Exchange (TSE) has the lowest VaR and ES for both tails. Also, except to DFMG index, Shape parameter (ξ ) is positive for both tails of all indicies and indicate that tails on both sides of the return distribution are heavy.

The political and economic instability in recent years and followed by rapid changes in the realm of financial markets, has increased the risk of most financial institutions. So that risk managers at these institutions are worried about the decline in their asset Value over the coming days. In recent studies, generally the Conditional Value at Risk is used to measure and forecast the risks existing in financial markets. Therefore, in this research, it has been attempted to introduce, calculate and implement a nonlinear hybrid model for forecasting the Conditional Value at Risk. For this purpose, the new hybrid model based on the Extreme Value Theory and the Holt-Winters exponential smoothing (HWES-EVT) that, in addition to dynamics, cluster characteristics and broad data sequence, also takes into account the forecast Conditional Value at Risk of the industry and Tehran Stock Exchange Indices. For evaluating the accuracy the performance of proposed hybrid model, this modek is compared with the GARCH-EVT model. The results of backtesting show that the proposed hybrid approach provides a more accurate answer to the forecasting of Conditional Value at Risk for these indicators Indices.

Recent year’s financial crisis gives rise to pay attention to Extreme losses. Investors suffer from Extreme losses and since unusaull outcomes probability is not far, investors concern about Extreme tail of return distribution. This paper is aimed to examin Extreme downside risk (EDR) that is calculated by Extreme Value Theory (EVT) which is designed to explain uncommon events. For this purpose, a sample composed of 243 listed firms in Tehran Stock Exchange is examined for 1384 to 1394. Portfolio study approach and Fama-McBeth (1973) regression are used to EDR pricing test. The results confirm EDR pricing and statistical significancy of Extreme downside risk in TSE. This research shows that potential loss from Extreme downside returns, EDR, is captured by asset pricing as a risk factor. Also, the effects of other risk measures including volatility, valu at risk and right tail mesure are stronger than EDR and if their effectes is controlled, EDR risk premium is no longer statistically significant.

Objective: After the financial crisis in 2008, market practitioners and financial researchers began to attach more importance to risk measurement and modeling. Expected shortfall is recognized risk measures in financial literature. Methods: By the estimation of expected shortfall as a coherent risk measure, and by use of Conditional Extreme Value Theory and combining new volatility measures, this research attempts to introduce a new model for risk measurement. Intraday data has been used in this research in order to estimate mentioned risk measures. Results: The results show that in comparison with alternative models, such as GARCH Conditional peak over threshold models, multifractal Conditional peak over threshold models, which utilize intraday data, perform better in risk estimation. In addition, the use of Extreme Value Theory brings about more favorable results in risk estimation. In this research, we use a new back-testing models in order to back-test expected shortfall. Conclusion: The use of the normal distribution function for the disruption components to estimate the expected drop has not been successful, and has led to an estimate of the low risk category. The use of Student's t-distribution in estimating risk measures has been acceptable, although in some cases it has led to an estimate of high risk. Considering Extreme Value Theory of Value in the above models has in most cases led to improved model performance. This means that it has moderately adjusted the estimates of the upper hand and the estimates of the.

Value at Risk (VaR) measures risk exposure at a given probability level and is very important for risk management. In this paper, mainly EVT models are compared to other well-known models such as GARCH, Historical Simulation and Filtered Historical Simulation. Then evaluation their models with different back testing such as Kupiec test, Christoffersen test and Lopez Loss function.Our results indicate that using Conditional methods and Extreme Value Theory to forecast Value at Risk, is better than other models. And we should examine different methods for forecast Value at Risk, then select the best method for any tails of distributions.

This paper investigates the relative performance of Value-at-Risk (VaR) and expected shortfall (ES) models using daily overall index data from TSE for a period of 8 years from 2008 to 2016. The main emphasis of the study has been given to Conditional Extreme Value Theory (CEVT) and to evaluate how well Conditional EVT model performs in modeling tails of distributions and in estimating and forecasting VaR and ES measures. We also compare them with parametric approaches. We have compared the accuracy of Conditional EVT approach to VaR and ES estimation with other competing models. We use Bernoulli coverage and Independence of violation tests for backtesting the VaR models and McNeil & Frey’ s Backtest and Model Confidence Set to assess the performance of the ES models. The best performing VaR and ES models is found to be the Conditional EVT. MCS function result for ES also shows that the Conditional EV with student's t standardized residuals, Conditional EV with normal standardized residuals and GARCH with student's t residuals models are respectively ranked first to third.

The general trend to focus more on core competencies has forced companies to use outsourcing strategies and led to appearance of supply chain. Supply chain risks can arise from many sources, including political events, demand fluctuation, technological changes, financial instability and natural disasters. To be able to handle these risks, supply chain risk management is needed. Managing supply risk is an essential part of the supply chain risk management and in this research we focus on supply risk management. We use Value at risk, Extreme Value Theory and cash flow at risk to present a method for quantification of supply risks such as late delivery, quality risk, natural disasters risk and business risk (financial instability of suppliers). Furthermore, a model for supplier selection and order allocation based on these supply risks, has been proposed. Finally, a numerical example is presented and some computational result is reported.fluctuations of supply chain and market adjustments are considered one of the duties of this ministry. This study will help in removing barriers and presenting new innovative solutions for using scientific approaches based on system analysis in different governmental sectors.

Generally, "The biggest risk in the capital market or ina portfolio (capital market, Bank ...) occurswhena sudden large change occur towards its unfavorable basket. It's essential for financial risk management knowing the probability that such case sareveryrare and estimated its consequences. These Values (Extreme Movement) are located at the tail of the distribution function, and therefore they named "Extreme Values".In this study, we followed the distribution of Iran stock Exchange returns (TEDPIX and Industrial Index) in two different time periods. We are testing afat tail in two different time periods. Generalized Extreme Value Theory (GEV) results show there are fat tails in the distribution function of return for both indices and for both periods. Finally, the Back testing results for the VaR calculated with this approach show that the model for 100-daytime horizonhas better performance than the 50- daytime horizon. We use Statistics Lopez tocomparethe performance of these Approach models (GEV); with VaR calculated with model Risk metric models with assuming normal distribution for different confidence levels. We reached to this conclusion that the GEV has better performance, because focuses on tail distribution function more than others approaches.

Creating a balance between risk and return has always been the major criterion in investment decisions in stock exchange. All investors look for an optimized compromise for their investments so that the utility function of their investment is maximized. This study aims at arriving at a more efficient model for optimizing investment portfolio. This model is to consider uncertainty and investment risk on the way to create a bigger return for the investors. In this way, Extreme Value Theory for assessing investment risk, as one of the newest assessors of Value at risk (VaR) was utilized. The time of this study covers the period from 2013 to 2018. The sample includes top 50 companies in Tehran Stock Exchange. Using GARCH method and maximizing likelihood function, the type of return distribution of top companies of Tehran Stock Exchange was determined at the first step. Next, efficiency frontier for investment risk in Tehran Exchange was compared to Markovitz model`s efficient frontier, using a quadric planning model through a Extreme Value Theory approach. The results of this study signify that forming an optimized investment portfolio through a Extreme Value model would not make any significant difference with Morkovitz Variance-Mean model.

Portfolio selection is an important problem in area of finance. Researchers have always tried to work with a variety of methods and strategies to achieve this important issue.In this research has been trying to present a new approach for portfolio selection with use of lower tail dependence and Extreme Value Theory.We show theoretically that lower tail dependence (c), a measure of the probability that a portfolio will suffer large losses given that the market does, contains important information for risk-averse investors. We then estimate c for a sample of stocks and show that it differs systematically from other risk measures including variance, semi-variance, skewness, kurtosis, beta, and coskewness. In out-of-sample tests, portfolios constructed to have low Values of c outperform the market index, and portfolios with high Values of c Our results indicate that c is conceptually important for risk-averse investors, differs substantially from other risk measures, and provides useful information for portfolio selection.