scholarly journals Value at Risk for Gold Spot Based on Quantile-GARCH Model

2016 ◽  
Author(s):  
Li Li ◽  
Lei Xiao
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Garch Model

Accurate value-at-risk forecasting based on the normal-GARCH model

2006 ◽  
Vol 51 (4) ◽  
pp. 2295-2312 ◽  
Author(s):  
Christoph Hartz ◽  
Stefan Mittnik ◽  
Marc Paolella
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Garch Model ◽  
Risk Forecasting

scholarly journals Research on the Value at Risk of Basis for Stock Index Futures Hedging in China Based on Two-State Markov Process and Semiparametric RS-GARCH Model

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Liang Wang ◽  
Tingjia Xu ◽  
Longhao Qin ◽  
Chenge Liu
Keyword(s):  
At Risk ◽  
Markov Process ◽  
Value At Risk ◽  
Likelihood Function ◽  
Parametric Method ◽  
Garch Model ◽  
Stock Index ◽  
Stock Index Futures ◽  
Index Futures ◽  
Futures Hedging

This article aims to investigate the Value at Risk of basis for stock index futures hedging in China. Since the RS-GARCH model can effectively describe the state transition of variance in VaR and the two-state Markov process can significantly reduce the dimension, this paper constructs the parameter and semiparametric RS-GARCH models based on two-state Markov process. Furthermore, the logarithm likelihood function method and the kernel estimation with invariable bandwidth method are used for VaR estimation and empirical analysis. It is found that the three fitting errors (MSE, MAD, and QLIKE) of conditional variance calculated by semiparametric model are significantly smaller than that of the parametric model. The results of Kupiec backtesting on VaR obtained by the two models show that the failure days of the former are less than or equal to that of the latter, so it can be inferred that the semiparametric RS-GARCH model constructed in this paper is more effective in estimating the Value at Risk of the basis for Chinese stock index futures. In addition, the mean value and standard deviation of VaR obtained by the semiparametric RS-GARCH model are smaller than that of the parametric method, which can prove that the former model is more conservative in risk estimation.

Estimation of Extreme Value-at-Risk: An EVT Approach for Quantile GARCH Model

2014 ◽  
Author(s):  
Yanping Yi ◽  
Xingdong Feng ◽  
Zhuo Huang
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Garch Model ◽  
Extreme Value

scholarly journals A Quantitative Analysis of Value-at-Risk based on Two-factor Pricing Model and GARCH Model

2021 ◽  
Vol 13 ◽  
pp. 338-340
Author(s):  
Qingqing Yu
Keyword(s):  
At Risk ◽  
Quantitative Analysis ◽  
Value At Risk ◽  
Garch Model ◽  
Theory And Practice ◽  
Financial Risks ◽  
Pricing Model ◽  
Gradual Improvement ◽  
New Instrument ◽  
Main Instrument

Value-at-risk has become the main instrument for the measurement and management of financial risks. With innovative construction of multi-level capital market system and gradual improvement of functions of financial system in China, financial risks demonstrate some new uncertainties. With regard to quantitative analysis and the management of risks in Chinese financial market, adoption of some methods for quantification of value-at-risk is of greatly importance in the fields of both theory and practice. The constant improvement in research of theory and practice concerning financial development gives birth to all kinds of new instruments for measurement and management of financial risks, among which value-at-risk is a common new instrument applied in measurement and management of modern finance. In this paper, an empirical analysis is conducted on quantification of financial value-at-risk based on Two-factor pricing model and GARCH model.

GJR-GARCH model in value-at-risk of financial holdings

2011 ◽  
Vol 21 (24) ◽  
pp. 1819-1829 ◽  
Author(s):  
Y. C. Su ◽  
H. C. Huang ◽  
Y. J. Lin
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Garch Model

Value-at-Risk Estimation Based on Empirical Likelihood Method

2010 ◽  
Vol 143-144 ◽  
pp. 1-5
Author(s):  
Zhao Wei Meng ◽  
Pei Chao Yu
Keyword(s):  
At Risk ◽  
Empirical Likelihood ◽  
Value At Risk ◽  
Simulation Analysis ◽  
Risk Estimation ◽  
Garch Model ◽  
Likelihood Method ◽  
Financial Risks ◽  
Financial Assets ◽  
Empirical Likelihood Method

Value at Risk ( ) is a method using statistical knowledge to measure financial risks, and its calculating core is to estimate or predicate fluctuation of the financial assets price. In recent years, the main method of estimating and predicating fluctuation of the financial assets price is the GARCH model. So to determine a reasonable GARCH model becomes the crux of calculating. In this paper, we proposed using empirical likelihood method to estimate , and we also proved that the empirical likelihood method is more effective and more concise than other current methods by simulation analysis.

scholarly journals ARIMA-GARCH Model and ARIMA-GARCH Ensemble for Value-at-Risk Prediction on Stocks Portfolio

2020 ◽  
Author(s):  
Tarno Tarno ◽  
Di Asih I Maruddani ◽  
Rita Rahmawati ◽  
Abdul Hoyyi ◽  
Trimono Trimono ◽  
...  
Keyword(s):  
At Risk ◽  
Risk Prediction ◽  
Confidence Level ◽  
Value At Risk ◽  
Garch Model ◽  
Holding Period ◽  
Return Variance ◽  
Stock Portfolio ◽  
Portfolio Return ◽  
Model Approach

Stocks portfolio is a form of investment that can be used to minimize the risk of loss. In a stock portfolio, the value at risk (VaR) can be predicted through the portfolio return. If portfolio return variance is heteroscedastic risk prediction can be done by using VaR with ARIMA-GARCH or Ensemble ARIMA-GARCH model approach. Furthermore, the accuracy of VaR is tested through backtesting test. In this study, the portfolio formed from Astra Agro Lestari Ltd (AALI) and Indofood Ltd (INDF) stocks from 10/02/2012 to 10/01/2019. The results showed that the best model is ARIMA(0,0,[3])-GARCH(1,2) with AIC of -5.604 and MSE 1.874e-07.At confidence level of 95% and 1 day holding period, the VaR of the ARIMA(0,0,[3])-GARCH(1,2) was -0.3464. Based on the backtesting test, it is proven to be very accurate to predict the value of loss risk because the value of the violation ratio (VR) is equal to 0.

Sign in / Sign up

Export Citation Format

Share Document