جستجوی مقالات مرتبط با کلیدواژه "extreme value theory" در نشریات گروه "مالی"

In this study, systemic risk index (β) was measured and decomposed by using the extreme value theory in the banks listed in Tehran Stock Exchange (TSE) during 2013-2021. This index was divided into two dimensions: total bank risk (tail risk) and link of the bank to the system during the financial crisis (system linkage). Using autoregressive distributed lag (ARDL) regression, the relationships between systemic risk and its dimensions with the characteristics of the banks and the relationships between their economic value-added (EVA) and various financial risks, including systemic risk, were calculated. According to the results, Post-Bank, Tejarat, and Saderat banks imposed the highest systemic risks and Karafarin and Eghtesad-e Novin banks imposed the lowest systemic risks, respectively. The results showed that with increasing the bank size, the systemic and tail risks that are specific to each bank decreased and the systemic linkage increased. There was a positive and significant relationship between the EVAs of the banks and their ratios of regulatory capital to credit risk-weighted assets. Also, there was a negative and significant relationship between the EVAs of the banks and their ratios of core capital to operational risk-weighted assets, net sustainable financial resources, and interest rate risk variables.

This study determines the optimal investment portfolio of insurance companies by considering underwriting activities. investment decisions in insurance companies are affected by underwriting activities. In this paper, the investment optimization problem of insurers is modeled using the copula-based conditional risk value, taking into account the results of insurance activities. Also, since the emphasis is on tails of distribution, the probability distribution of variables in tails is estimated using Pareto distribution and in other parts of the distribution using the Empirical probability distribution. Data collected on monthly basis covers two periods in-sample, between 2006 to 2015 and out-of-sample, between 2016 to 2019.The findings show that the optimum portfolio includes eighty percent of risky assets (stock and real estate) and only twenty percent of risk-free assets (bank deposits) and it is outside the legal constraints set by Central Insurance Therefore, legal constraints prevent insurance companies from the optimal selection of investment portfolio. Also, the comparison of out-of-sample performance with in-sample performance of portfolios shows that portfolios based on copula functions have better and more robust performance than traditional models.

The purpose of this research is to model Value-at-Risk (VaR) of portfolio with EVT-Pair-Copulas approach. In the financial literature, a significant amount of empirical studies have been done on the characteristics of financial assets returns and researchers have found a set of stylized facts about this subject. In this regard leptokurtic, left-skewed, weak autocorrelation, volatility clustering, and heteroscedasticity can be mentioned. Any estimation of risk without considering these characteristics or using unrealistic assumptions about financial assets returns increases the probability of failure in the risk management process. For this purpose, at first, the marginal distributions of returns are obtained using extreme value theory (EVT). Concerning characteristics of financial assets returns and also the primary filter to apply EVT, we use heteroscedasticity models for the marginal distributions of assets. Then the structure of the dependence between different stocks is estimated by using C-Vine, D-Vine, and R-Vine pair copula models. Afterward, the VaR of portfolio is estimated using the Monte Carlo simulation method. The final results show that the model with GARCH marginal distribution and R-Vine pair copula has been able to achieve the best performance among rival models at 95% confidence level.

In this paper, the effect of banks network topology on banks systemic risk in Tehran Stock Exchange have been investigated by using banks daily stock return from 2013 to 2021. First, systemic risk index have been measured and decomposed by using EVT approach and then this index divided into two dimensions, bank tail risk and systemic linkage. Then the network between banks listed in the banking industry of Tehran Stock Exchange have been created based on dynamic conditional correlations (DCC) by using the spanning minimum tree (MST) approach and the banks network topology have been measured. Finally, the relationship between banks systemic risk and its dimensions with banks network topology have been investigated by using regression of panel data. According to the results, Post-Bank, Tejarat and Saderat banks have the highest and Karafarin and Eghtesad-Novin banks have the lowest systemic risk, respectively. Also, the regression results showed that there is a positive and significant relationship between the variables of node strength, betweenness centrality and size with the banks systemic risk and a negative and significant relationship between the variables of node degree and liquidity with banks systemic risk.

Investigating the probablility of rare events occurring (events that occur with very low probability) is an important issue in portfolio risk management. extreme value theory of value provides the mathematical basis for modeling these events and calculating the risk criteria associated with them, such as the value at risk. The purpose of this paper is to model the dependency structure andextreme value theory of 10 foreign exchange companies of Tehran Stock Exchange (Persian Gulf Holding, Bandar Abbas Refinery, Mobarakeh Steel, Topico, Ghadir, Parsian Oil and Gas, Melli Mes, Gol Gohar, Mobile Communications, Chadormelo).The results indicated the fact that among the stock returns of the top 10 companies evaluated, it is possible that using extreme value theory of value using vine Copula functions, the results of the forecast were greatly increased. Results of the copula function in six modes: simple Copula (t), time-varying Copula (tDCC), and Gaussian distribution-based time-varying Copula (GDCC). tvSJC) was investigated. In all six cases, the use of the Copula -wine method increased the accuracy in predicting optimal stock returns.

Portfolio risk measurement has always been one crucial aspect of finance. Several approaches have been modeled through time and some traditional approaches are criticized by researchers. Traditional methods use the Gaussian approach that it been used normal distribution for assets return. Empirical studies indicate that the return of assets has a stylized fact. The stylized facts include the fat tail, negative skewness and volatility clustering. Furthermore, some studies show that there is asymmetric tail dependence. This means in market downsides, dependence is more than upside market. So this concept should be considered in portfolio risk measurement, otherwise, the estimates will end up having a bias.

To consider the stylized fact, different methods had been proposed. For modeling fat tail and negative skewness, this article uses extreme value theory and to consider volatility clustering, GARCH, EGARCH and GJR are applied. For modeling nonlinear and asymmetric dependence different methods are proposed. In the present article T-skewed copula is used. Mean-variance model is used to compute the weight of the portfolio. To analyze the result, backtesting is examined and for comparing methods, QPS function is applied.

In this article TSE data have been used. At first, stylized facts have been checked. For testing fat tail and negative skewness, JB test is used. to analyze the volatility clustering, Liung Box test is applied. Also to check asymmetric tail dependence, exceedance correlation and paired T-test are used. Findings indicate the existence of the stylized facts in the 30 stock return of TSE. After running the models, the only approach based GJR and EGARCH is accepted in backtesting. Also based on QPS function, findings represent the strength of the applied approach to common ones.

Results show that the use of the GJR model is better than other models for Portfolio risk measurementthat it shows the importance of leverage effect in TSE for stock return modeling.

Generally, the greatest risk in the capital market or in the portfolio of investors occurs when large sudden changes occur in the unfavorable portfolio. These losses are in the distribution sequence, and for this purpose they are called "limitative values". In this research, the logarithmic efficiency of the Tehran Stock Exchange Index based on the information received during the time interval between the day (due to the use of high frequency data) during the years 1392 to 1395, and the use of the maximum block approach in measuring the VaR value index is used. It turned out VaR index was calculated using historical simulation methods and variance-covariance method as the traditional risk assessment criteria and the results were compared. The results of data analysis in R software showed that the use of monthly information in calculating the risk-weighted value index has a higher predictive accuracy and the error rate (test error) in this case is lower than traditional risk assessment methods.

Study of the Extreme behavior of the Stock Market and the correct pattern of the distribution of returns has an important role in the management of market risk. The current study, based on BMM approach, examines the distribution pattern of return in Tehran Stock Exchange in different time intervals. In order to choose the appropriate model in different time series, L-Moment ratio diagram was used. Then, by using the calculation of parameters of the selected models based on the approach of Maximum likelihood, the conformity and selection of appropriate model in each time interval was performed based on AD test. To model the distribution of patterns of total market returns, the total stock index data of 2004-2016 was used and the basis of the performed calculation has the log of daily return of Tehran stock exchange. The results indicated that among the examined model, GL model in annual time interval and in minimum series, and GEV model in other time intervals of minimum and maximum series had a better performance.

Given the importance of exchange-traded funds and their ever-increasing advancement in financial markets, it is important to review and explain their ranking based on criteria beyond the examination of returns. Also, considering that broad distribution sequences in financial data are of particular importance in measuring financial risk, in this study, based on criteria beyond the efficiency and considering the value of risk, based on the Extreme -Value Theory (EVT) and the modified Sharpe Ratio, the rating of exchange-traded funds has been dealt with. Then evaluation their models with different back testing such as Kupiec test, Christoffersen test. For the purpose of this study, the first period of September 2014 until the end of September 2017 was considered for the funds that were active in the capital market during this period. The results indicate the proper capabilities of VaR models which is based on GHARCH-EVT approach.

Value at Risk is one of the most important criteria in financial markets for risk assessment. Various methods have been proposed for measuring this index. Extreme Value Theory is one of the new methods for calculating the value at risk that focuses on Distribution sequence of series, and instead of taking all data into account without considering the limiting assumptions such as the assumption of normalization. In this research, the logarithmic return of Tehran Stock Exchange index based on the data received during the time intervals of the day (due to the use of high frequency data) during the years 1392 to 1395 was summed up and the Block Maxima Approach was used in VaR measurement. Given the correlation between the variance and the time series of the data, the problem was first solved using the E-GARCH model. Then VaR index was calculated in three blocking conditions based on hourly, daily and monthly data. The results showed that the use of monthly data in calculating this index has a higher predictive accuracy.

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.

During last decades, financial markets have witnessed large losses due to their exposure to unexpected market crash. Resulting in these financial disasters, financial institutions, regulators and academics have developed intensive research to provide better measurement techniques and hedging tools. Value-at-Risk (VaR) is the most popular risk measure in the financial industry. In this paper Application Extreme Value Theory and long-memory to Stock Market in Iran (In Framework Model-GARCH) was Checked. We use same long-range memory GARCH-type models (FIAGARCH, HYGARCH and FIAPARCH) and EVT to forecast the financial market risk. Findings Indicated that The FIAPARCH-EVT approach performs better in predicting the one day ahead VaRs for different series studied.

Stress testing is a simulation technique to evaluate portfolio reactions to several critical situations. In this paper, we review different stress testing methodologies to examine impacts of different stress scenarios on an Iranian equity portfolio. We identify the extreme tails of all risk factors in our portfolio by extreme value theory and model their dynamic and nonlinear dependence structures with copula functions. We performed three stress tests such as historical, hybrid and hypothetical stress scenarios to simulate the joint evolution of risk factors over time in a realistic way. According to the empirical findings, we find that historical scenario method is not a suitable tool for stress testing due to several drawbacks and show the importance of forward-looking analysis such as hybrid and hypothetical scenarios. We also indicate that the hypothetical stress approach is superior to the other two scenarios from the perspective of stress testing.

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.

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.

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.

In recent years, Contention lead to intensify in money and finance market. So firm performance involved high fluctuation. Determining and convening appropriate portfolio require to consider basis risk and return. It's defined investment desirability. Simultaneous considering risk and return are the main element of investor decision making. In the following research, we are trying to evaluate downside risk and stock return in Tehran stock exchange, with due attention to reduce asset value in order to lead to decrease risk, via extreme value theory. Accordingly we apply extreme value theory in order to estimate extreme value parameters. This is done using Garch(1,1) type model, auto regressive and maximum likelihood estimation method. We use four factor Carhart model in order to extract abnormal return. For this purpose, according to the information available in TSE index real data between 1382-1392 years. This research employs panel least square to show virtually relation between stock return and extreme value quantity. Eventually, the outcomes of examining the research hypotheses demonstrate a relation between abnormal return and downside risk in the studied period. Moreover, the results revealed that panel data examining demonstrate direct relation between expected return and extreme value magnitude.

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 (χ), 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 χ 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 χ outperform the market index, and portfolios with high values of χ. Our results indicate that χ is conceptually important for risk-averse investors, differs substantially from other risk measures, and provides useful information for portfolio selection.