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 p...

Fluctuations in crude oil price significantly impact the global economic market. A rise or a fall leads to redistribution of wealth in both oil-exporting and importing countries. Under such background, efficient and accurate predictions for crude oil price are critical for a stable economic development. However, crude oil price forecasting has been proved to be an extremely tough task, due to i...

This paper proposes a new method for crude oil price forecasting based on support vector machine (SVM). The procedure of developing a support vector machine model for time series forecasting involves data sampling, sample preprocessing, training & learning and out-of-sample forecasting. To evaluate the forecasting ability of SVM, we compare its performance with those of ARIMA and BPNN. The expe...

We use high-frequency intra-day realized volatility to evaluate the relative forecasting performance of several models for the volatility of crude oil daily spot returns. Our objective is to evaluate the predictive ability of time-invariant and Markov switching GARCH models over different horizons. Using Carasco, Hu and Ploberger (2014) test for regime switching in the mean and variance of the ...

This paper presents a hybrid wavelet support vector machines (WSVM) model that combines both wavelet technique and the SVM model for crude oil price forecasting. Based on the purpose, the main time series was decomposed to some multi-frequently time series by wavelet theory and these time series were imposed as input data to the SVM for forecasting of crude oil price series. To assess the effec...

This paper examines the forward pricing efficiency of the local crude palm oil (CPO) futures market. In an efficient market, the relevant signal to be used by -the producers, traders and processors is simply the futures price. The forward pricing efficiency is measured in terms of the forecasting ability of Malaysian crude palm oil futures price on physical price. The relative predictive power ...

Crude oil is a highly strategic commodity. This paper investigates the necessity of using interval data and interval econometric models for crude oil price forecasting. Compared to the traditional point-valued data, interval-valued data in a time period contain much more valuable information which is useful for market participant to make decisions. We develop three autoregressive conditional in...

This study proposes a multiple kernel learning (MKL)-based regression model for crude oil spot price forecasting and trading. We used a well-known trend-following technical analysis indicator, the moving average convergence and divergence (MACD) indicator, for extracting features from original spot prices. Additionally, we factored in the possibility that movements of target crude oil prices ma...

In this study, an empirical mode decomposition (EMD) based neural network ensemble learning model is proposed for world crude oil spot price modeling and forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite and often small number of intrinsic mode functions (IMFs). Then the three-layer feed-forward neural network (FNN) model was used to mod...