جستجوی مقالات مرتبط با کلیدواژه « رویکرد فراتر از آستانه » در نشریات گروه « مالی »

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.

The occurrence of financial crises in recent decades has caused a lot of damage to the economy as well as economic enterprises in many countries. The Extreme Value Approach is a new approach to the phenomenon of financial crisis, which has been able to analyze the events that are less likely to occur but the damage caused by them is significant. In this study, we use the Extreme Value theory and Stochastic differential equations to find a new method for estimating the more precisely the value at risk. For this purpose, after estimating the parameters of the Stochastic differential equations, which includes the geometric Brownian motion, the geometric Brownian motion with the jump, the nonlinear GARCH model, and the Heston model, simulate the Monte Carlo simulations of future paths and then use peak over threshold approach, to estimate the value We at risk. The results of the simultaneous use of Stochastic differential equations and Extreme value theory ​​are compared with historical simulations and variance-covariance approaches for value at risk. The results of Back-test techniques on value at risk indicate the superiority of the Heston model in estimation of value at risk.

The expansion of the capital market and the reduction of interest rates on commercial banks has made that investing in dominate shares as one of the most important opportunities for obtain gain on investment, which requires risk acceptance. In this paper, the goal is to extract the residual values of the logarithmic return of the Tehran stock exchange index using the CIPRA model. Then, using the extreme value theory, the extreme value model was obtained for these residual. Extreme value theory is a good approach to the estimation of high and low tails and measure such as Value Risk (VaR). In order to determine the performance of this method, another model was compared with this model using the two indexes include Tehran Stock Exchange and the Top 50 Industry Index at 99 and 99.5% for the estimated value of risk. The results of the backtesting show that the EVT-CIPRA approach works better.

In this paper, we deal with the ranking of different VaR and ES approaches using daily banking industry index data over the period 2008 to 2016, with an emphasis on Conditional Extreme Value approach. In the first stage, we use Bernoulli coverage and independence of violation tests for VaR models and McNeil & Frey’s backtest for ES models to examine the validity of these models. In the second stage, we import the loss functions of the valid models remained from the first stage into the MCS function and rank statistically them. The loss function used for VaR models is Dowd loss function and the one used for ES models is Olsen loss function. The results show that the in both VaR and ES models, the conditional EV with normal standardized residuals, the conditional EV with student's t standardized residuals and GARCH with student's t residuals models are respectively ranked first to third.

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.

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.

Quantifying the uncertainty is one of the most important subject in financial issues, so nowadays in each financial and investment activity risk assessment and management is required. Value-at-risk (VaR) has become a popular risk measure since it was adopted by the International agencies in 1988. Precise prediction of VaR provides proper evaluation criteria in areas such as investment decisionmaking and risk management. Due to the fat-tailed distribution in most real financial time-series, extreme value theory (EVT) is a powerful tool in determining the VaR by concentrating on the shape of the fat-tailed probability distribution. In This study, Peak Over Threshold (POT) approach used for value at risk forecasting by Tehran Stock Exchange (TSE) data. The results show this approach is better than traditional approaches such as historical simulation and variance-covariance methods.