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.

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.

Introduction Changes in occurrence and frequency of Extreme events can have more severe and damage effects than changes in the average climatic characteristics (Choi et al, 2008). Therefore, it is important to study the variability and change the behavior of Extreme atmospheric events. The main purpose of this paper is to investigate the temperature Extreme events using the distribution of generalized Extreme Value distribution (GEV) and non-parametric methods in Kermanshah province. The results of this study can be effective in providing the necessary context for assessing the extent of vulnerability and adaptation methods and strategies to deal with it. Methodology The study area in the present study is Kermanshah province. Because to study the Extreme events, the length of the statistical period should be long-term, so in this study, the data of Kermanshah synoptic station, which has a statistical period of 56 years (1961-2016), was used. First, the maximum and minimum daily temperature data for the study period were obtained from the Meteorological Organization of the country and after reconstructing the incomplete data, the quality of the data was checked. The data series were first analyzed by trend and then analyzed by frequency of boundary events. To study and analyze the trend of marginal events, the indicators presented by the National Climate Committee of the World Meteorological Organization and the Climate Change and Prediction Research Program, called ETCCDMI, have been used. In total, the group provided 16 main indices with a major emphasis on temperature limits that can be extracted from a series of recorded daily data (Zhang et al., 2006: 2014. ( Results and Discussion Generalized Extreme Value Distribution The present study aimed to analyze the changes in temperature Extreme events in the study period using generalized Extreme Value distribution in Kermanshah province. According to the statistics and information of meteorological stations, this region has a drastic change in terms of climate and is affected every year by dry days without successive rains on the one hand or sudden heavy rains on the other, with a sharp rise or fall in temperature. The results of the Maxima block methods showed that in the study area, the intensity and frequency of cold border events decreased and the intensity and frequency of hot border events increased. Warm nights mean an increase in the percentage of days when the minimum daily temperature is above 90 and hot days mean a percentage of days when the maximum daily temperature is above 90. The incremental trend is the highest annual Value of the minimum daily temperature at the 95% level. The slope of the trend line for the index is 0. 04 C in the decade. Conclusion The results showed that concerning cold Extreme indices such as frost days, ice days, cold days and nights, the direction of change is negative and with hot Extreme indices such as summer days, tropical nights, nights and Hot days the direction of change is positive with a confidence level of 99 percent. Since the rate of increase of the minimum temperature was higher than the maximum temperature, the range of the day and night temperature in the region has decreased. Also, graphs of the Values of minimum and maximum temperature polynomials in years of return T with a 95 percent confidence interval were plotted. According to the above diagrams, we can estimate the Extreme Values of the desired parameter for the specified return period.

Value relevance is one of the most important areas of focus in accounting, and due to the recent studies accounting measures’,Value relevance alleviate by the passage of time. One of factors that these studies relate to this event is conservatism. In other words, recent studies show that conservatism will drop Value relevance of earnings and other measures in accounting. In the other side, an important characteristic of earnings is non-linear behavior of this measure. Therefore, examining the effect of conservatism on Value relevance of earnings with this characteristic in mind, is disregarded in the literature. In this research, this area will be covered. The Khan & watts measure is used for operating conservatism and Easton & Harris model is take to account the Value relevance. The results of this research show that conservatism with no respect to Extreme and non-Extreme earnings has no relation to Value relevance of earnings.

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.

This paper examines the Extreme Value theory as a useful measure for evaluation of Extreme risk events (rare but high impact events). A common practice to calculate Value at Risk (VaR) is based on the assumption that changes in the Value of the portfolio are normally distributed. However, assets returns usually come from fat-tailed distributions. Therefore, computing VaR under the assumption of conditional normality can be an important source of error. Extreme Value theory does not follow from the central limit theorem in mathematics, and instead is focused on Extreme data. Therefore, this study examines the Extreme Value theory is a powerful framework for studying tail distributions. USD return and volatility is considered as a case study in this article. The normality assumption was rejected by examining the distribution of logarithmic returns. The results suggest that the application of EVT make better fit than the other models that are based on the assumption of normality.

Estimation of Extreme dependence between assets and financial markets plays a vital role in various aspects of financial risk. Extreme Value theory (EVT) focuses on modeling the tail behavior of distribution using Extreme Values. The purpose of this paper is to investigate asymptotic dependence and estimate the degree of tail dependence of the TSE daily returns with five other international markets (DFM, S&P-500, Nikkei-225, DAX and CAC All Shares) for right and left tails of the return distribution. The degree and type of Extreme dependence of these stock markets is investigated by nonparametric measures based on multivariate EVT (MEVT) for the period from 2006 to 2015. We used a vector autoregressive (VAR) and MGARCH to filter out any serial correlation and heteroskedasticity between any two return series. The results show that Tehran security exchange (TSE) is asymptotically independent from other stock markets. Furthermore, the highest degree of positive dependence is shown between TSE and DFM in both tails.