Estimation of the Value-at-Risk (VaR) Using the TARCH Model by Considering the Effects of Long Memory in Stock Investments (original) (raw)
Related papers
ARMA-GARCH model for value-at-risk (VaR) prediction on stocks of PT. Astra Agro Lestari.Tbk
2021
PT. Astra Agro Lestari Tbk (AALI) is one of the plantation companies with the largest market capitalization in Indonesia. AALI stocks traded on the stock exchange have fairly fluctuating value and volatility of stock returns are not constant (heteroskedastic). One of the risk measurements that can be used to predict the risk of stock investing is Value-at-Risk (VaR). In conditions that are heteroskedastic stock returns, risk prediction can be done with the VaR ARCH/GARCH and VaR ARCH/GARCH combination model. Empirical studies were carried out on AALI stocks for the period of August 2, 2012 until October 1, 2019. The results obtained showed that the best model was ARIMA (0,0,1)-GARCH (1,2) with AIC value of -4.9793 and MSE of 0.00005. At the 95% trust level, the VaR ARCH/ARCH value was -0.3464.
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.
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.
Jurnal Varian, 2021
Before buying a stock, an investor must estimate the risk which will be received. VaR is one of the methods that can be used to measure the level of risk. Most stock returns have a high fluctuation, so the variant is heteroscedastic, which is thought to be caused by exogenous variables. The time series model used to model data that is not only influenced by the previous period but is also influenced by exogenous variables is ARIMAX. In contrast, the GARCHX model is used to obtain a more optimal stock return data model with heteroscedasticity cases and is influenced by exogenous variables. This study uses the ARIMAX-GARCHX model to calculate the VaR of the stock returns of PT Bank Central Asia Tbk. The exogenous variables used are the exchange rate return of IDR/USD and the return of the JCI in the period January 3, 2017, to March 31, 2021. The best model chosen is the ARIMAX(2,0,1,1)-GARCHX(1,1,1). VaR calculation is carried out with the concept of moving windows with time intervals of 250, 375, and 500 transaction days. The results obtained at the 95% confidence level, the maximum loss obtained by an investor is 1,4%.
Variance – Covariance (Delta Normal) Approach of Var Models:An Example Frombombay Stock Exchange
Zenodo (CERN European Organization for Nuclear Research), 2019
Numerous investors are inclined to understand, the quantum of wealth or capital they can lose in a specific time period, which could be one day or 5 days or 10 days.In this research paper, out of numerous approaches, variance-covariance approach of VaR is discussed.This method helps in prediction of maximum loss that can occur for a specific time period and given probability. Here in order to calculate VaR, portfolios are created, which is followed by identification of returns distribution. Finally VaR of portfolios is calculated. Daily loss is calculated using data for the period of 01 st January 2018 to 31 st December 2018as historical data consisting of 246 days. Companies were selected from Bombay Stock Exchange (BSE). VaR has been computed for both 95% and 99% confidence intervals for holding period of 1 day and 10 days.
Finance a Uver
In this paper, we explore the relevance of asymmetry, long memory and fat tails in modeling and forecasting the conditional volatility and market risk for the Gulf Cooperation Council (GCC) stock markets. Various linear and non-linear long-memory GARCH-class models under three density functions are used to investigate this relevancy. Our results reveal that non-linear GARCH-class models accommodating long memory and asymmetry can better capture the volatility of returns. In particular, we find that some stock returns’ behaviors are well described by dual long memory in the mean and the conditional variances. Interestingly, the FIAPARCH volatility model with skewed Student distribution is found to be the best suited for estimating the value at risk and expected shortfall for short and long trading positions. This model outperforms the other competing long-memory GARCH-class models and simple GARCH and EGARCH models. Overall, long-memory, asymmetry, persistence and fat tails are impor...
Value at Risk: A Standard Tool in Measuring Risk : A Quantitative Study on Stock Portfolio
2011
The role of risk management has gained momentum in recent years most notably after the recent financial crisis. This thesis uses a quantitative approach to evaluate the theory of value at risk which is considered a benchmark to measure financial risk. The thesis makes use of both parametric and non parametric approaches to evaluate the effectiveness of VAR as a standard tool in measuring risk of stock portfolio. This study uses the normal distribution, student t-distribution, historical simulation and the exponential weighted moving average at 95% and 99% confidence levels on the stock returns of Sonny Ericsson, Three Months Swedish Treasury bill (STB3M) and Nordea Bank. The evaluations of the VAR models are based on the Kupiec (1995) Test. From a general perspective, the results of the study indicate that VAR as a proxy of risk measurement has some imprecision in its estimates. However, this imprecision is not all the same for all the approaches. The results indicate that models which assume normality of return distribution display poor performance at both confidence levels than models which assume fatter tails or have leptokurtic characteristics. Another finding from the study which may be interesting is the fact that during the period of high volatility such as the financial crisis of 2008, the imprecision of VAR estimates increases. For the parametric approaches, the t-distribution VAR estimates were accurate at 95% confidence level, while normal distribution approach produced inaccurate estimates at 95% confidence level. However both approaches were unable to provide accurate estimates at 99% confidence level. For the non parametric approaches the exponentially weighted moving average outperformed the historical simulation approach at 95% confidence level, while at the 99% confidence level both approaches tend to perform equally. The results of this study thus question the reliability on VAR as a standard tool in measuring risk on stock portfolio. It also suggest that more research should be done to improve on the accuracy of VAR approaches, given that the role of risk management in today's business environment is increasing ever than before. The study suggest VAR should be complemented with other risk measures such as Extreme value theory and stress testing, and that more than one back testing techniques should be used to test the accuracy of VAR.
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.
Journal of Physics: Conference Series, 2017
Value-at-Risk has already become a standard measurement that must be carried out by the financial institution for both internal interest and regulatory. In this paper, the estimation of Value-at-Risk of some stocks with econometric models approach is analyzed. In this research, we assume that the stock return follows the time series model. To do the estimation of mean value we are using ARMA models, while to estimate the variance value we are using FIGARCH models. Furthermore, the mean value estimator and the variance are used to estimate the Value-at-Risk. The result of the analysis shows that from five stock PRUF, BBRI, MPPA, BMRI, and INDF, the Value-at-Risk obtained are 0.01791, 0.06037, 0.02550, 0.06030, and 0.02585 respectively. Since Value-at-Risk represents the maximum risk size of each stock at a 95% level of significance, then it can be taken into consideration in determining the investment policy on stocks.
A Comparative Performance of Conventional Methods for Estimating Market Risk Using Value at Risk
This paper presents a comparative evaluation of the predictive performance of conventional univariate VaR models including unconditional normal distribution model, exponentially weighted moving average (EWMA/RiskMetrics), Historical Simulation, Filtered Historical Simulation, GARCH-normal and GARCH Students t models in terms of their forecasting accuracy. The paper empirically determines the extent to which the aforementioned methods are reliable in estimating one-day ahead Value at Risk (VaR). The analysis is based on daily closing prices of the USD/KES exchange rates over the period starting January 03, 2003 to December 31, 2016. In order to assess the performance of the models, the rolling window of approximately four years (n=1000 days) is used for backtesting purposes. The backtesting analysis covers the sub-period from November 2008 to December 2016, consequently including the most volatile periods of the Kenyan shilling and the historical all-time high in September 2015. The empirical results demonstrate that GJR-GARCH-t approach and Filtered Historical Simulation method with GARCH volatility specification perform competitively accurate in estimating VaR forecasts for both standard and more extreme quantiles thereby generally out-performing all the other models under consideration.