Estimating Conditional Value at Risk in the Tehran Stock Exchange Based on the Extreme Value Theory Using GARCH Models

Tabasi, Hamed; Yousefi, Vahidreza; Tamošaitienė, Jolanta; Ghasemi, Foroogh

doi:10.3390/admsci9020040

Cited by 16 publications

(8 citation statements)

References 43 publications

(46 reference statements)

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“…where u N represents the number of exceedances over the threshold u and n is the sample. By substituting Equations (18) and (20) into Equation 19…”

Section: Modelling Tails Using Extreme Value Theorymentioning

confidence: 99%

“…In recent studies in finance VaR is be estimated more accurately using the conditional-EVT approach especially in modelling the distribution of extreme events and estimating extreme tail risks than the conventional models. Moreover, in some other studies, the conditional Value-at-Risk has been used as the risk measure, and researchers have shown that theoretically and empirically, using [18] showed that the GARCH-EVT model outperforms the simple GARCH model with Student's t and normal distributions for residuals.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting Value-at-Risk of Financial Markets under the Global Pandemic of COVID-19 Using Conditional Extreme Value Theory

Omari¹,

Mundia²,

Ngina³

2020

JMF

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The recent global pandemic of coronavirus (COVID-19) has had an enormous impact on the financial markets across the world. It has created an unprecedented level of risk uncertainty, prompting investors to impetuously dispose of their assets leading to significant losses over a very short period. In this paper, the conditional heteroscedastic models and extreme value theory are combined to examine the extreme tail behaviour of stock indices from major economies over the period before and during the COVID-19 pandemic outbreak. Daily returns data of stock market indices from twelve different countries are used in this study. The paper implements a dynamic method for forecasting a one-day ahead Value at Risk. As a first step, a comprehensive in-sample volatility modelling is implemented with skewed Student's-t distribution assumption and their goodness of fit is determined using information selection criteria. In the second step, the VaR quantiles are estimated with the help of conditional Extreme Value Theory framework and then used to estimate the out-of-sample VaR forecasts. Backtesting results suggest that the conditional EVT based models consistently produce a better 1-day VaR performance compared with conditional models with asymmetric probability distributions for return innovations and maybe a better option in the estimation of VaR. This emphasizes the importance of modelling extreme events in stock markets using conditional extreme value theory and shows that the ability of the model to capture volatility clustering accurately is not sufficient for a correct assessment of risk in these markets.

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“…where u N represents the number of exceedances over the threshold u and n is the sample. By substituting Equations (18) and (20) into Equation 19…”

Section: Modelling Tails Using Extreme Value Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Value-at-Risk of Financial Markets under the Global Pandemic of COVID-19 Using Conditional Extreme Value Theory

Omari¹,

Mundia²,

Ngina³

2020

JMF

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show abstract

“…The EVD is fruitful in forecasting when there is a heavier right tail on the underlying prices in market and to describe extremely unlikely events [27]. The EVD with real parameters µ as the location parameter and σ as the scale parameter presents a statistical continuous distribution given over the set of real numbers.…”

Section: Var Based On the Evdmentioning

confidence: 99%

On the Statistical GARCH Model for Managing the Risk by Employing a Fat-Tailed Distribution in Finance

et al. 2020

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The Conditional Value-at-Risk (CVaR) is a coherent measure that evaluates the risk for different investing scenarios. On the other hand, since the extreme value distribution has been revealed to furnish better financial and economical data adjustment in contrast to the well-known normal distribution, we here employ this distribution in investigating explicit formulas for the two common risk measures, i.e., VaR and CVaR, to have better tools in risk management. The formulas are then employed under the generalized autoregressive conditional heteroskedasticity (GARCH) model for risk management as our main contribution. To confirm the theoretical discussions of this work, the daily returns of several stocks are considered and worked out. The simulation results uphold the superiority of our findings.

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“…Shrestha et al [14] and Valipour et al [15] analyzed risk allocation. Hamed et al [16] and Dixit and Tiwari [17] estimated conditional value at risk. Hatefi and Tamosaitiene [18] presented the model for evaluating construction projects by considering interrelationships among risk factors.…”

Section: Introductionmentioning

confidence: 99%

Project Portfolio Construction Using Extreme Value Theory

2021

Self Cite

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Choosing proper projects has a great impact on organizational success. Firms have various factors for choosing projects based on their different objectives and strategies. The problem of optimization of projects’ risks and returns is among the most prevalent issues in project portfolio selection. In order to optimize and select proper projects, the amount of projects’ expected risks and returns must be evaluated correctly. Determining the relevant distribution is very important in achieving these expectations. In this research, various types of practical distributions were examined, and considering expected and realized risks, the effects of choosing the different distribution on estimation of risks on construction projects were studied.

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Estimating Conditional Value at Risk in the Tehran Stock Exchange Based on the Extreme Value Theory Using GARCH Models

Cited by 16 publications

References 43 publications

Forecasting Value-at-Risk of Financial Markets under the Global Pandemic of COVID-19 Using Conditional Extreme Value Theory

Forecasting Value-at-Risk of Financial Markets under the Global Pandemic of COVID-19 Using Conditional Extreme Value Theory

On the Statistical GARCH Model for Managing the Risk by Employing a Fat-Tailed Distribution in Finance

Project Portfolio Construction Using Extreme Value Theory

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