Conditional VaR using GARCH-EVT approach: Forecasting Volatility in Tunisian Financial Market (original) (raw)
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This paper attempted to calculate the market risk in the Tehran Stock Exchange by estimating the Conditional Value at Risk. Since the Conditional Value at Risk is a tail-related measure, Extreme Value Theory has been utilized to estimate the risk more accurately. Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models were used to model the volatility-clustering feature, and to estimate the parameters of the model, the Maximum Likelihood method was applied. The results of the study showed that in the estimation of model parameters, assuming T-student distribution function gave better results than the Normal distribution function. The Monte Carlo simulation method was used for backtesting the Conditional Value at Risk model, and in the end, the performance of different models, in the estimation of this measure, was compared.
Quantitative Finance, 2013
Although stock prices fluctuate, the variations are relatively small and are frequently assumed to be normal distributed on a large time scale. But sometimes these fluctuations can become determinant, especially when unforeseen large drops in asset prices are observed that could result in huge losses or even in market crashes. The evidence shows that these events happen far more often than would be expected under the generalized assumption of normal distributed financial returns. Thus it is crucial to properly model the distribution tails so as to be able to predict the frequency and magnitude of extreme stock price returns. In this paper we follow the approach suggested by and combine the GARCH-type models with the Extreme Value Theory (EVT) to estimate the tails of three financial index returns S&P 500, FTSE 100 and NIKKEI 225 representing three important financial areas in the world. Our results indicate that EVT-based conditional quantile estimates are more accurate than those from conventional GARCH models assuming normal or Student's t distribution innovations when doing not only in-sample but also out-of-sample estimation. Moreover, these results are robust to alternative GARCH model specifications. The findings of this paper should be useful to investors in general, since their goal is to be able to forecast unforeseen price movements and take advantage of them by positioning themselves in the market according to these predictions. JEL classification: C52; C53; D46 ; G15
Estimation of Value at Risk (VaR) Based On Lévy-GARCH Models: Evidence from Tehran Stock Exchange
Journal of Money and Economy, 2021
Risk (VaR) using GARCH type models with improved return distribution. Value at Risk (VaR) is an essential benchmark for measuring the risk of financial markets quantitatively. The parametric method, historical simulation, and Monte Carlo simulation have been proposed in several financial mathematics and engineering studies to calculate VaR, that each of them has some limitations. Therefore, these methods are not recommended in the case of complications in financial modeling since they require considering a series of assumptions, such as symmetric distributions in return on assets. Because the stock exchange data in the present study are skewed, asymmetric distributions along with symmetric distributions have been used for estimating VaR in this study. In this paper, the performance of fifteen VaR models with a compound of three conditional volatility characteristics including GARCH, APARCH and GJR and five distributional assumptions (normal, Student's t, skewed Student's t and two different Lévy distributions, include normal-inverse Gaussian (NIG) and generalized hyperbolic (GHyp)) for return innovations are investigated in the chemical, base metals, automobile, and cement industries. To do so, daily data from of Tehran Stock Exchange are used from 2013 to 2020. The results show that the GJR model with NIG distribution is more accurate than other models. According to the industry index loss function, the highest and lowest risks are related to the automotive and cement industries.
Forecasting Value-at-Risk using GARCH and Extreme-Value-Theory Approaches for Daily Returns
2017
This paper deals with the application of Univariate Generalised Autoregressive Conditional Heteroskedasticity (GARCH) modelling and Extreme Value Theory (EVT) to model extreme market risk for returns on DowJones market index. The study compares the performance of GARCH models and EVT (unconditional & conditional) in predicting daily Value-at-Risk (VaR) at 95% and 99% levels of confidence by using daily returns. In order to demonstrate the effect of using different innovations, GARCH(1,1) under three different distributional assumptions; Normal, Student’s t and skewed Student’s t, is applied to the daily returns. Furthermore, an EVT-based dynamic approach is also investigated, using the popular Peak Over Threshold (POT) method. Finally, an innovation approach is used whereby GARCH is combined with EVT-POT by using the two-step procedure of McNeil (1998). Statistical methods are used to evaluate the forecasting performance of all the models. In this study, it is found that the GARCH m...
A Comparison of the Extreme Value Theory and GARCH Models in terms of Risk Measures
2018
In this paper, we apply extreme value theory (EVT) and time series models to eight developed and emerging stock markets published in the Morgan Stanley Capital International (MSCI) Index. Based on the Human Development Index (HDI) rankings, which are consistent with the MSCI index, we analyse Singapore, Spain, UK and US for developed stock markets and Chile, Russia, Malaysia and Turkey for emerging stock markets. We use the daily prices (in USD) of eight countries for the period from January 2014 to December 2017 and examine the performances of the models based on in-sample testing. Calculating the value-at-risk (VaR) as a risk measure for both right and left tails of the log-returns of the selected models, we compare these countries in terms of their financial risks. The obtained risk measures enable us to discuss the grouping and the ranking of the stock markets and their relative positions.
A GARCH APPROACH TO VaR CALCULATION IN FINANCIAL MARKET
2020
Value at Risk (VaR) has already becomes a standard measurement that must be carried out by financial institution for both internal interest and regulatory. VaR is defined as the value that portfolio will loss with a certain probability value and over a certain time horizon (usually one or ten days). In this paper we examine of VaR calculation when the volatility is not constant using generalized autoregressive conditional heteroscedastic (GARCH) model. We illustrate the method to real data from Indonesian financial market that is the stock of PT. Indosat Tbk.
Empirical analysis of GARCH models in value at risk estimation
Journal of International Financial Markets, Institutions and Money, 2006
This paper studies seven GARCH models, including RiskMetrics and two long memory GARCH models, in Value at Risk (VaR) estimation. Both long and short positions of investment were considered. The seven models were applied to 12 market indices and four foreign exchange rates to assess each model in estimating VaR at various confidence levels. The results indicate that both stationary and fractionally integrated GARCH models outperform RiskMetrics in estimating 1% VaR. Although most return series show fat-tailed distribution and satisfy the long memory property, it is more important to consider a model with fat-tailed error in estimating VaR. Asymmetric behavior is also discovered in the stock market data that t-error models give better 1% VaR estimates than normal-error models in long position, but not in short position. No such asymmetry is observed in the exchange rate data.
A comparison of GARCH models for VaR estimation
Expert Systems with Applications, 2012
This study is an attempt to compare a comprehensive list of GARCH models in quantifying risks of VaR under stress times. We gather data of stock market indices from both emerging (Brazil and Turkey) and developed (Germany and the USA) markets, over the period of global financial crisis and make use of numerous GARCH specifications to return VaR values. Then we compare the assessments of VaR with the realized returns by Kupiec and Christoffersen Tests. Besides, we calculate Quadratic Losses to evaluate the GARCH specifications in calculating VaR. The results reveal that the ARCH specification is the best performer followed by GARCH(1, 1) and the Student's t distribution is slightly better than the Normal. The other outcome of the paper is that the worst performers are Non-Linear Power GARCH and Non-Linear Power GARCH with a shift. All GARCH estimations are carried out with STATA that uses the Maximum Likelihood method of estimation.
Quantile forecasts using the Realized GARCH-EVT approach
Studies in Economics and Finance, 2018
PurposeThis study aims to implement a novel approach of using the Realized generalized autoregressive conditional heteroskedasticity (GARCH) model within the conditional extreme value theory (EVT) framework to generate quantile forecasts. The Realized GARCH-EVT models are estimated with different realized volatility measures. The forecasting ability of the Realized GARCH-EVT models is compared with that of the standard GARCH-EVT models.Design/methodology/approachOne-step-ahead forecasts of Value-at-Risk (VaR) and expected shortfall (ES) for five European stock indices, using different two-stage GARCH-EVT models, are generated. The forecasting ability of the standard GARCH-EVT model and the asymmetric exponential GARCH (EGARCH)-EVT model is compared with that of the Realized GARCH-EVT model. Additionally, five realized volatility measures are used to test whether the choice of realized volatility measure affects the forecasting performance of the Realized GARCH-EVT model.FindingsIn t...