scholarly journals Modeling and Forecasting Market Value-at-Risk of DS30 Index through GARCH Family Models with Heavy Tail Distribution

2018 ◽  
Vol 5 (4) ◽  
pp. 129-134
Author(s):  
Md. Monimul Huq ◽  
Md. Ayub Ali
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Market Value ◽  
Heavy Tail ◽  
Tail Distribution ◽  
Modeling And Forecasting ◽  
Family Models

scholarly journals Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

2017 ◽  
Vol 28 (75) ◽  
pp. 361-376 ◽  
Author(s):  
Leandro dos Santos Maciel ◽  
Rosangela Ballini
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Garch Models ◽  
Time Interval ◽  
Risk Modeling ◽  
Additional Information ◽  
Volatility Models ◽  
Modeling And Forecasting ◽  
The Difference ◽  
Market Indexes

ABSTRACT This article considers range-based volatility modeling for identifying and forecasting conditional volatility models based on returns. It suggests the inclusion of range measuring, defined as the difference between the maximum and minimum price of an asset within a time interval, as an exogenous variable in generalized autoregressive conditional heteroscedasticity (GARCH) models. The motivation is evaluating whether range provides additional information to the volatility process (intraday variability) and improves forecasting, when compared to GARCH-type approaches and the conditional autoregressive range (CARR) model. The empirical analysis uses data from the main stock market indexes for the U.S. and Brazilian economies, i.e. S&P 500 and IBOVESPA, respectively, within the period from January 2004 to December 2014. Performance is compared in terms of accuracy, by means of value-at-risk (VaR) modeling and forecasting. The out-of-sample results indicate that range-based volatility models provide more accurate VaR forecasts than GARCH models.

Mark to market value at risk

2019 ◽  
Vol 208 (1) ◽  
pp. 299-321 ◽  
Author(s):  
Yu Chen ◽  
Zhicheng Wang ◽  
Zhengjun Zhang
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Market Value

VAR As a Tool to Assess the Market Risk of Trading Positions of a Commercial Bank

2016 ◽  
Vol 4 (2) ◽  
pp. 58-64
Author(s):  
Попова ◽  
Anna Popova
Keyword(s):  
At Risk ◽  
Exchange Rates ◽  
Commercial Bank ◽  
Value At Risk ◽  
Market Value ◽  
Market Risk ◽  
Foreign Exchange Rates ◽  
Financial Instruments ◽  
Short Period ◽  
The Common

Forecasting of any risk is the crucial activity for any commercial bank. In current situation market risk is an important element needed to be analyzed. The probability of this type of risk may be affected by the change in the market value of financial instruments and by the volatility of foreign exchange rates. Nowadays in Russia each organization should conduct proper risk-management and be able to predict possible losses. The article presents the assessment of the market risk by the example of the price of the common share of the Bank of Moscow. Forecasting is implemented by three models: ARIMA, Value-at-Risk and VAR. Scientific novelty of this article is in comparison of the prediction procedures of above mentioned methods. The result obtained during the analysis shows, that the model Value-at-Risk is efficient for a short period of forecasting and should be combined with others models in order to get more accurate results.

scholarly journals Estimation of value at risk by using gjr-garch copula based on block maxima

2021 ◽  
Vol 5 (2) ◽  
pp. 405-414
Author(s):  
Hasna Afifah Rusyda ◽  
Fajar Indrayatna ◽  
Lienda Noviyanti
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Estimation ◽  
Joint Probability ◽  
Extreme Value Distribution ◽  
Copula Functions ◽  
Tail Distribution ◽  
Block Maxima ◽  
Dependent Model ◽  
Portfolio Return

This paper will discuss the risk estimation of a portfolio based on value at risk (VaR) using a copula-based asymmetric Glosten – Jagannathan – Runkle - Generalized Autoregressive Conditional Heteroskedasticity (GJR-GARCH). There is non-linear correlation for dependent model structure among the variables that lead to the inaccurate VaR estimation so that we use copula functions to model the joint probability of large market movements. Data is GEV distributed. Therefore, we use Block Maxima consisting of fitting an extreme value distribution as a tail distribution to count VaR. The results show VaR can estimate the risk of portfolio return reasonably because the model has captured the data properties. Data volatility can be accommodated by GJR-GARCH, Copula can capture dependence between stocks, and Block maxima can accommodate extreme tail behavior of the data.

scholarly journals PEMBENTUKAN PORTOFOLIO SAHAM DENGAN METODE MARKOWITZ DAN PENGUKURAN VALUE AT RISK BERDASARKAN GENERALIZED EXTREME VALUE (Studi Kasus: Saham Perusahaan The IDX Top Ten Blue 2017)

2018 ◽  
Vol 7 (2) ◽  
pp. 212-223
Author(s):  
Ria Epelina Situmorang ◽  
Di Asih I Maruddani ◽  
Rukun Santoso
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Extreme Values ◽  
Heavy Tail ◽  
Extreme Value ◽  
Return Rate ◽  
Optimal Portfolio ◽  
Average Risk ◽  
Generalized Extreme Value ◽  
Financial Investment

In financial investment, investors will try to minimize risk and increase returns for portfolio formation. One method of forming an optimal portfolio is the Markowitz method. This method can reduce the risk and increase returns. The performance portfolio is measured using the Sharpe index. Value at Risk (VaR) is an estimate of the maximum loss that will be experienced in a certain time period and level of trust. The characteristics of financial data are the extreme values that are alleged to have heavy tail and cause financial risk to be very large. The existence of extreme values can be modeled with Generalized Extreme Value (GEV). This study uses company stock data of The IDX Top Ten Blue 2017 which forms an optimal portfolio consisting of two stocks, namely a combination of TLKM and BMRI stocks for the best weight of 20%: 80% with the expected return rate of 0.00111 and standard deviation of 0.01057. Portfolio performance as measured by the Sharpe index is 1,06190 indicating the return obtained from investing in the portfolio above the average risk-free investment return rate of -0,01010. Risk calculation is obtained based on Generalized Extreme Value (GEV) if you invest both of these stocks with a 95% confidence level is 0,0206 or 2,06% of the current assets. Keywords: Portfolio, Risk, Heavy Tail, Value at Risk (VaR), Markowitz, Sharpe Index, Generalized Extreme Value (GEV).

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