Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables

Lu Zhang,Junbiao Zhang,Chiao Su,Tao Xiong

doi:10.1155/2017/5730295

Abstract

This paper examines the interval forecasting of carbon futures prices in one of the most important carbon futures market. Specifically, the purpose of this study is to present a novel hybrid approach, which is composed of multioutput support vector regression (MSVR) and particle swarm optimization (PSO), in the task of forecasting the highest and lowest prices of carbon futures on the next trading day. Furthermore, we set out to investigate if considering some potential predictors, which have strong influence on carbon futures prices, in modeling process is useful for achieving better prediction performance. Aiming at testing its effectiveness, we benchmark the forecasting performance of our approach against four competitors. The daily interval prices of carbon futures contracts traded in the Intercontinental Futures Exchange from August 12, 2010, to November 13, 2014, are used as the experiment dataset. The statistical significance of the interval forecasts is examined. The proposed hybrid approach is found to demonstrate the higher forecasting performance relative to all other competitors. Our application offers practitioners a promising set of results with interval forecasting in carbon futures market.

Highlights

Forecasting carbon futures prices is part of the basis of financial investment decisions
We develop a multioutput support vector regression (MSVR)-particle swarm optimization (PSO) method, in which PSO is used to solve the parameter for MSVR, for interval forecasting of carbon futures prices
We proposed a hybrid method, by incorporating multioutput support vector regression and particle swarm optimization, for interval forecasting of the carbon futures prices

Summary

Introduction

Forecasting carbon futures prices is part of the basis of financial investment decisions. Methods for forecasting carbon futures prices have attracted the attention of financial researchers and practitioners. Fan et al [6] proposed a short-term prediction model, based on neural networks, for carbon futures prices forecasting. Zhu and Wei [11] developed a novel hybrid prediction model that exploits the unique strength of the ARIMA and LSSVM techniques for carbon futures prices forecasting. Atsalakis [15] proposed three computational intelligence techniques, that is, a hybrid neurofuzzy controller that forms a closed-loop feedback mechanism, an artificial neural network based system, and an adaptive neurofuzzy inference system for accurately forecasting the changes in the carbon price. It should be noted that the studies aforementioned concentrated on point forecasting instead of an interval one

Objectives

Methods

Results

Conclusion