Chinese Business Review (ISSN 1537-1506) Vol.14, No.6, 2015 (original) (raw)
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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.
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.
A robust VaR model under different time periods and weighting schemes
Review of Quantitative Finance and Accounting, 2007
This paper analyses several volatility models by examining their ability to forecast the Value-at-Risk (VaR) for two different time periods and two capitalization weighting schemes. Specifically, VaR is calculated for large and small capitalization stocks, based on Dow Jones (DJ) Euro Stoxx indices and is modeled for long and short trading positions by using non parametric, semi parametric and parametric methods. In order to choose one model among the various forecasting methods, a two-stage backtesting procedure is implemented. In the first stage the unconditional coverage test is used to examine the statistical accuracy of the models. In the second stage a loss function is applied to investigate whether the differences between the models, that calculated accurately the VaR, are statistically significant. Under this framework, the combination of a parametric model with the historical simulation produced robust results across the sample periods, market capitalization schemes, trading positions and confidence levels and therefore there is a risk measure that is reliable. JEL Nos: C22; C52; C53; G15
VaR Analysis for the Shanghai Stock Market
ipcsit.com
In this paper we investigated the relevance of the skewed Student's t distribution innovation in capturing long-memory and asymmetry features in the volatility of Shanghai stock markets. We also examined the performance of in-sample and out-of-sample value-at-risk (VaR) analyses using the FIAPARCH model with the normal, Student's t, and skewed Student's t distribution innovations. We found that risk managers and portfolio investors can estimate VaR and optimal margin levels most accurately by using the skewed Student's t FIAPARCH VaR models of long and short trading positions in the Shanghai stock market.
A Vector Autoregressive (VAR) Model for the Turkish Financial Markets
In this paper, we develop a vector autoregressive (VAR) model of the Turkish financial markets for the period of June 15 2006-June 15 2010 and forecasts ISE100 index, TRY/USD exchange rate, and short-term interest rates. The out-of-sample forecast performance of the VAR model is compared with the results from the univariate models. Moreover, the dynamics of the financial markets are analyzed through Granger causality and impulse response analysis.
A Vector Auto-Regressıve (VAR) Model for the Turkish Financial Markets
2011
In this paper, we develop a vector autoregressive (VAR) model of the Turkish financial markets for the period of June 15 2006-June 15 2010 and forecasts ISE100 index, TRY/USD exchange rate, and short-term interest rates. The out-ofsample forecast performance of the VAR model is compared with the results from the univariate models. Moreover, the dynamics of the financial markets are analyzed through Granger causality and impulse response analysis.
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.
RISE OF VAR MODELLING APPROACH*
Journal of Economic Surveys, 2011
This paper surveys the rise of the Vector AutoRegressive (VAR) approach from a historical perspective. It shows that the VAR approach arises from a fusion of the Cowles Commission tradition and time series statistical methods, catalysed by the rational expectations (RE) movement, that the approach offers a systematic solution to the issue of 'model choice' bypassed by Cowles researchers, hence essentially inheriting and enhancing the Cowles legacy rather than abandoning or opposing it. By tackling model choice, however, the VAR approach helps reform econometrics by shifting the research focus from measurement of given theories to identification/verification of data-coherent theories.
2020
Value at Risk (VaR) is one of the standard methods that can be used in measuring risk in stock investments. VaR is defined as the maximum possible loss for a particular position or portfolio in the known confidence level of a specific time horizon. The main topic discussed in this thesis is to estimate VaR using the TARCH (Threshold Autoregressive Conditional Heteroscedasticity) model in a time series by considering the effect of long memory. The TARCH model is applied to the daily log return data of a company's stock in Indonesia to estimate the amount of quantile that will be used in calculating VaR. Based on the analysis, it was found that with a significance level of 95% and assuming an investment of 200,000,000 IDR, the VaR using the TARCH model approach was 5,110,200 IDR per day.
Vector Autoregressive (VAR) Modeling and Projection of DSE
Chinese Business Review, 2015
In this paper, vector autoregressive (VAR) models have been recognized for the selected indicators of Dhaka stock exchange (DSE). Bangladesh uses the micro economic variables, such as stock trade, invested stock capital, stock volume, current market value, and DSE general indexes which have the direct impact on DSE prices. The data were collected for the period from June 2004 to July 2013 as the basis on daily scale. But to get the maximum explorative information and reduction of volatility, the data have been transformed to the monthly scale. The outliers and extreme values of the study variables are detected through box and whisker plot. To detect the unit root property of the study variables, various unit root tests have been applied. The forecast performance of the different VAR models is compared to have the minimum residual. Moreover, the dynamics of this financial market is analyzed through Granger causality and impulse response analysis.