A New Two-Stage Approach with Boosting and Model Averaging for Interval-Valued Crude Oil Prices Forecasting in Uncertainty Environments

Bai Huang,Yuying Sun,Shouyang Wang

doi:10.3389/fenrg.2021.707937

Abstract

In view of the intrinsic complexity of the oil market, crude oil prices are influenced by numerous factors that make forecasting very difficult. Recognizing this challenge, numerous approaches have been introduced, but little work has been done concerning the interval-valued prices. To capture the underlying characteristics of crude oil price movements, this paper proposes a two-stage forecasting procedure to forecast interval-valued time series, which generalizes point-valued forecasts to incorporate uncertainty and variability. The empirical results show that our proposed approach significantly outperforms all the benchmark models in terms of both forecasting accuracy and robustness analysis. These results can provide references for decision-makers to understand the trends of crude oil prices and improve the efficiency of economic activities.

Highlights

As one of the most important commodities, crude oil plays a vital role in various fields
This section compares the forecasting performance of the proposed 2SVBMA approach with various competing methods presented in previous studies by using interval-valued crude oil prices
The whole sample from 2005 January to 2017 December are divided into two parts: one is used for parameter estimation, and the other is used for out-of-sample forecasting

Summary

Introduction

As one of the most important commodities, crude oil plays a vital role in various fields. Crude oil prices have been extremely volatile (see Figure 1). The oil-related industries are highly sensitive to oil price changes (Ebrahim et al, 2014; Taghizadeh-Hesary et al, 2016). Accurate prediction of crude oil prices and the market volatility is valuable for market participants to make risk management plans and investment decisions (Zaabouti et al, 2016; Zhang et al, 2020). The crude oil prices are volatile, and are dependent on many factors such as market trends, sentiments and stock markets. The aforementioned factors make the crude oil prices unstable and makes its prediction complicated and challenging. We aim to develop a reliable model for crude oil price forecasting

Objectives

Methods

Results

Conclusion