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.

Trying to identify an appropriate model to enhance measurement accuracy by using value at risk measures is of particular importance. Conditional Value at Risk (CVaR) with having some of the shortcomings of VaR, is a more reliable measure. In this study, the characteristics of the Tehran Stock Exchange index data usage FIGARCH-EVT model to calculate value at risk if states have been more accurate. GARCH-EVT hybrid implementation model and its development, FIGARCH-EVT model, we found that the effect of clustering, dynamic and long-term memory has been included in the modeling. FIGARCH model for log data output index, which will be modeled in terms of the above properties. In addition, the wide trail property index return data using extreme value theory (EVT) is used for residual FIGARCH model. To compare the results, NORMAL-GARCH models and t-Student-GARCH, historical simulation and GARCH-EVT indicator is used for data output. The results of the model using retrospective tests were evaluated. The results of this study indicate that the data distribution is skewed and asymmetrical index returns do not follow a normal distribution. The tests Standardized Exceedance Residuals and The Cumulative Violation Process and Expected shortfall backtesting and loss function Lopez FIGARCH-EVT model over other models is more accurate.

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 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.

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.

This study aims at optimizing the portfolio of financial assets and in particular focuses on the stock market with Conditional value at risk (CVaR) as the portfolio risk measure. This study uses generalized Conditional heterogeneity variance methods, the dependency structure, the extreme value theory, and with the GARCH-EVT-Vine-Copula approach to optimize the portfolio and minimize the CVaR of a stock portfolio during a certain period by the re-weighting method. Modeling is based on the performance data of 7 companies among the top 50 listed companies during the period 2015 to 2021. The results show that considering the extreme values and structural dependence between the examined time series improves the risk identification between these markets. In addition, among the studied models, the out-of-sample results for the accumulated wealth function of different models show that when considering the dependence structure, the EGARCH-EVT model based on the Coppola Vine function results outperforms other models.

In this research, the GARCH-EVT-COPULA method is investigated to determine the dependency structure and portfolio risk estimation on the foreign exchange market data in Iran. GARCH-EVT models are used to mariginal distribution of each of four currency returns series. For the joint model, we choose five copuls with different dependence structure such as Frank, Clayton, Gumble, Normal and t-Student copulas. In this research portfolio risk is measured using VaR and CVaR. The statistical sample of this study is the daily exchange rate of USD, EURO, Pound and AED for the free market with 5 working days from September to the end of 1396. Based on the results of the research, using the Akaike information criterion values, the t-student function is the best fitted copula model for investigating the dependency structure. Exchange rates have the same upper and lower tail dependencies. Accordingly, in the markets for boom (severe positive) and stagnation (severe negative), the dependence between the two exchange rates is the same.

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.

Contingency fee is one of the common institutions of common law legal system particularly in the united state. In this type of fees, the payment of fee is Conditional to the results of lawsuit. So if the lawsuit fails, no fee would be paid to the lawyer and if the lawsuit is successful, more than usual amount or a pecent of condemned amount will be paid. Despite accepting theses agreement in some legal systems, there are some limitations in order to make conformity with their own legal system which make different, the importance and functions of it in compare with the U. S. In some legal systems, other institutions with the same function are predicted too. In Iran legal system, despite of freedom of contract, according to article 80 of the law of attorney regulations, a type of contingency fees which is a part of a demand are prohibited. This paper with a comparative attitude and with insisting on U. S legal system and clarifiying the Conditional and contingency fees and its advantages and disadvantages, defend the accepting of it in Iran legal system.