There have been many backtesting methods proposed for value at risk. Yet they have rarely been applied in practice. Here, we provide a comprehensive review of all of the recent backtesting methods ...

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A review of backtesting for value at risk

The Value at Risk (VaR) of selling the option on crude oil WTI has not widely known, whereas this trade is the most significant transactions in the world. This study aimed to analyze the Value at Risk (VaR) of the far out of the money (FOTM) and the in the money (ITM) strike position of selling option on crude oil WTI investment. The monthly option premium return data ranging from April 1984 to May 2017 was analyzed by the ARCH-GARCH and VaR method to get the risk of FOTM and ITM strike position. Empirical results indicate that the risk of the FOTM strike was much lower than the ITM strike positions. It meant that selecting the FOTM strike position of the selling option on crude oil WTI investment could be considered by stakeholders because its risk was much lower than the ITM strike position. JEL Classification: C32, G19, G32

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The Value at Risk of Selling Option on Crude Oil West Texas Intermediate

This study attempts to estimate the value-at-risk (VaR) to forecast volatility for both conventional and Islamic stock markets in Malaysia. In particular, the purpose of the article is to investigate whether GARCH models are accurate in the evaluation of VaR in emerging stock markets such as Malaysia.The daily return of the conventional (KLCI) and Islamic (FBMS) stock market are analysed for the period 2000 – 2015. The volatility model of GARCH (1,1), TGARCH (1,1) and CGARCH (1,1) with a normal and and student-t distribution are used to model the conditional variance of the stock market returns. The VaR violations of unconditional coverage and the backtesting procedure of Kupiec test are used to check the reliability and accuracy of the volatility model used for both normal and student-t distribution. Based on the Akaike Information Criterion (AIC), the best model for modelling the conventional and Islamic stock market returns is TGARCH (1,1). The backtesting results showed that for all GARCH models used, the normal distribution gives better forecast VaR compared to the student’s t distribution.

https://hrmars.com/papers_submitted/5568/An_Estimation_of_Value_at_Risk_using_GARCH_Models_for_the_Conventional_and_Islamic_Stock_Market_in_Malaysia.pdf

An Estimation of Value at Risk using GARCH Models for the Conventional and Islamic Stock Market in Malaysia

Purpose: The study focused on modelling the volatility of energy markets spot prices using GARCH models and estimating Value-at-Risk.
Methodology: The conditional heteroscedasticity models are used to model the volatility of gasoline and crude oil energy commodities. In estimating Value at Risk; GARCH-EVT model is utilized in comparison with other conventional approaches. The accuracy of the VaR forecasts is assessed by using standard statistical back testing procedures.
Results: The empirical results suggests that the gasoline and crude oil prices exhibit highly stylized features such as extreme price spikes, price dependency between markets, correlation asymmetry and non-linear dependency. We also conclude that the EGARCH-EVT model is more robust, provides the best t and outperforms the other conventional models in terms of forecasting accuracy and VaR prediction. Generally, the GARCH-EVT model can be used to plays an integral role as a risk management tool in the energy industry.
Unique contribution to theory, practice and policy: In light of the research findings, the study recommends that organizations should leverage modern technology as a basis of realizing efficiency, effectiveness, and sustainability of projects. The study likewise recommends that organizations should build capacities to enhance labour productivity. In addition, the study recommends that organizations should adopt transformational leadership approaches as a basis of enhancing performance. The study recommends the need to revise the legal framework with a view to ensure that it reflects the changing needs of the project requirements

Modelling energy market volatility using garch models and estimating value-at-risk

The oil price has a very important effect on the world economy. In this paper, using data sets of Europe Brent and West Texas Intermediate (WTI) Cushing crude oil daily prices from Jan. 4, 2000 to Jan. 4, 2016, the VaR forecasting performance of GARCH-type models are analyzed and compared in a short horizon. Based on the Kupiecs POF-test and Christo ffersens interval forecast test, as well as a Back testing VaR Loss Function, the empirical results indicate that, for Europe Brent crude oil, EGARCH (1,1) has the best performance; while for WTI, APARCH (1,1) and GJR-GARCH (1,1) outperform other GARCH models. In fact, these results also give significant guidance on how to choose a better risk management model for the certain commodity of different companies even in the same time period.

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Jin-Guo Lian

Papers

Forecast and Backtesting of VAR Models in Crude Oil Market