Journal of Industrial and Systems Engineering, 2017

The use of GARCH models to characterize crude oil price volatility is widely observed in the empirical literature. In this paper the efficiency of six univariate GARCH models and two methods of estimation the parameters for forecasting oil price volatility are examined and the best method for forecasting crude oil price volatility of Brent market is determined. All the examined models in this paperbelong to the univariate time series family. This article investigates and compares the efficiency of volatility models for crude oil markets. The four years out-of-sample volatility forecasts of the GARCH models are evaluated using the superior predictive ability test with more loss function. The results find that GARCH (1,1) model can outperform all of the other models for the crude oil price of Brent market across different loss functions. Four different measures are used to evaluate the forecasting accuracy of the models. Then, two methods of estimation the parameters of GARCH models, ...

Volatility and the risk-return trade off of crude oil or crude oil market participation is essential to National Investment, decision making, marketing, and the determination of the financial strength of Nations among other things. Therefore, this research study was targeted at modeling price volatility and the risk-return related to crude oil export in Nigerian crude oil market using the first order asymmetric and symmetric univariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) family model in three distributional assumptions namely, Normal, student's-t and generalized error distribution. To achieve this target, three objectives with three research questions and two hypotheses were raised for the study. The data for the study was extracted from the Central Bank of Nigeria online statistical database starting from January, 1987 to June, and 2017. The results from the statistical analysis reveal that the markets were optimistic of their investment and other trade related activities. Sequel to that, there were high probabilities of gains than losses. Although, the variables use in these markets were extremely volatiles and shows evidence there exists positive risk first-rated meaning that investments or investors deserved rewards for holding risky assets. In estimation, first order symmetric GARCH model (GARCH, (1,1) in student's-t error assumption gave a better fit than the first order Asymmetric GARCH model (EGARCH (1,1)) in Normal error distributional assumptions. However, the selected models were subjected to several diagnostic test such as ARCH effect test, test for serial correlation and QQ-plot in order to validate their fitness which was confirmed to be appropriate. And recommendations were made to the Government to look for new ways to diversify the economy from total dependence on oil to non-crude oil such as agriculture, manufacturing and mining sector. For investors or marketers in this markets, they were advice to be mindful in trading in a highly volatile period especially when there is evidence of high standard deviation in the descriptive statistic of the return series and in modeling volatility of price return of certain micro/ macroeconomic variable the leverage effect of such variable should be properly estimated using asymmetric GARCH model.

Journal of Financial Risk Management, 2022

This paper aims at providing an in-depth analysis of forecasting ability of different GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models and finding the best GARCH model for VaR estimation for crude oil. Analysis of VaR forecasting performance of different GARCH models is done using Kupiecs POF test, Christoffersens test and Backtesting VaR Loss Function. Crude oil is one of the most important fuel sources and has contributed to over a third of the world's energy consumption. Oil shocks have influence on macroeconomic activities through various ways. Sharp oil price changes delay business investment because they raise uncertainty thus reducing aggregate output for some time. Analysis of crude oil prices trends is instrumental in informing the economy's policy and decision making. Continued development and improvement of models used in analyzing prices improve forecasting accuracy which in turns leads to better costs and revenue prediction by businesses. The study uses Brent Crude Oil prices data over a period of ten years from the year 2011 to 2020. The study finds that the IGARCH T-distribution model is the best model out of the five models for VaR estimation based on LR.uc Statistic (0.235) and LR.cc Statistic (0.317) which are the least among the values realized. ME and RMSE for the five models used for forecasting have negligible difference. However, the IGARCH model stands out with IGARCH T-distribution being the best out of the five models in this study with ME of 0.0000963591 and RMSE of 0.05304335. We therefore conclude that the IGARCH T-distribution model is the best model out of the five models used in this study for forecasting Brent crude oil price volatility as well as for VaR estimations.

2018

Most of the times, Economic and Financial data not only become highly volatile but also show heterogeneous variances (heteroscedasticity). The common method of the Box Jenkins cannot be used for data modeling as the method has an effect of heteroscedasticity (autoregressive conditional heteroscedastic ARCH effects). One of the usable methods to overcome the effect of heteroscedasticity is GARCH model. The aim of this study is to find the best model to estimate the parameters, to predict the share price, and to forecast the volatility of data share price of Adaro energy Tbk, Indonesia, from January 2014 to December 2016. The study also discuss the Window Dressing. The best model which fits the data is identified as AR(1)-GARCH (1,1). The application of this best model for forecasting the share price of Adaro energy Tbk, Indonesia, for the next 30 days showed very promising results and the mean absolute percentage error was determined as 2.16%.

2021

This article presents the advantages of multivariate GARCH models. Multivariate GARCH models are identified as the best and flexible models in econometrics. Also, the interest of these models is to be able to examine and analyze the various relations which the various series maintain between them. In order to be able to estimate several financial series to analyze their correlations and transfers of volatility. We present an application on the relationship between the existing volatility in the oil market and the energy market, which we found that the assembly performance of the BEKK-GARCH form is better than that of other models.

Energy Economics, 2012

The use of parametric GARCH models to characterise crude oil price volatility is widely observed in the empirical literature. In this paper, we consider an alternative approach involving nonparametric method to model and forecast oil price return volatility. Focusing on two crude oil markets, Brent and West Texas Intermediate (WTI), we show that the out-of-sample volatility forecast of the nonparametric GARCH model yields superior performance relative to an extensive class of parametric GARCH models. These results are supported by the use of robust loss functions and the Hansen's (2005) superior predictive ability test. The improvement in forecasting accuracy of oil price return volatility based on the nonparametric GARCH model suggests that this method offers an attractive and viable alternative to the commonly used parametric GARCH models.

2019

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....

Bullion, 2019

For quite long time now, the Nigerian government had adopted the Moving Average (MA) method to peg crude oil price benchmark. However, MA has been considered to be inefficient, due to the observed large discrepancy between projected price and the actual crude oil price in the international oil market. Therefore, in an attempt to search for the appropriate oil benchmark, this research tries to compare ARIMA and GARCH models, with the aim of identifying the best econometrics model for forecasting Nigerian crude oil prices. A time series data for the monthly Bonny light oil price (MBLP) from April 1986 to November, 2018 was used. The unit root tests results using ADF and PP as well as correlogram indicated that MBLP is stationary at first difference transformation. Moreover, ARIMA (1, 1, 6) is found to be the best fitted model. Symmetric GARCH (1, 1)-GED is also found to be the parsimonious model and produce better forecast than GARCH (1,1)-t. The comparative forecasting performance between the best fitted ARIMA model (ARIMA (1, 1, 6)) and GARCH model (GARCH (1, 1)-GED) has shown that GARCH model perform better than the ARIMA model. Therefore, the study recommends that the Nigerian government and policy makers should replace the conventional MA approach with GARCH in benchmarking Nigerian oil price in the budgeting process.

Qalaai Zanist Scientific Journal, 2019

The paper aims to compare the performance of several univariate symmetric and asymmetric GARCH volatility models in modeling and forecasting the volatility of daily Gasoline prices in Erbil city. This paper chooses the GARCH, GARCH-M, TGARCH, E-GARCH and Power GARCH model to analyze the daily return of Gasoline under three different error distributions: normal distribution, student-t distribution and generalized error distribution and then compare the results and choose the appropriate model to forecast the volatility. The sample is divided into two subsamples. The first subsample is called insample data set (Training sample) used to estimate the ARMA-GARCH models for underlying data and the second subsample is called out-sample data set (Testing sample) used to investigate the performance of volatility forecasting. As a result of analyses, we conclude that the best model fits the volatility of Gasoline returns series is AR(2)-Power GARCH(

Journal of Petroleum Science and Engineering, 2013

The study focuses on a new class of nonlinear volatility models based on neural networks and STAR type nonlinearity. Accordingly, LSTAR-LST-GARCH family and LSTAR-LST-GARCH-NN family of models will be evaluated to analyze petrol prices with economic applications. The nonlinear behavior and leptokurtic distribution are discussed in many studies. The study aims proposing augmentation of linear GARCH, fractionally integrated FI-GARCH and Asymmetric Power APGARCH models with LSTAR type nonlinearity modeling. Further, the proposed models will be augmented with neural networks to benefit from well known learning and forecasting capabilities. The multilayer perceptron (MLP) neural network model and LSTAR model have significant similarities in terms of their architecture. The proposed LSTAR-LST-GARCH family and ANN augmented LSTAR-LST-GARCH-MLP models are evaluated for modeling petrol prices. Empirical findings of the study are: (1) Fractionally integrated and asymmetric power improvements among the GARCH family models provide better forecasting capability for petrol prices; better captured long memory and high volatility characteristics of petrol prices. (2). LSTAR-LST-GARCH model family results in even better gains in out-of-sample forecasting. (3) Donaldson and Kamstra (1997) based MLP-GARCH family provided similar results with the LSTAR-LST-GARCH family models. One exception is for MLP-FIGARCH and MLP-FIAPGARCH models; FI and AP augmented models proposed in this study.