Forecasting carbon price using a multi‐objective least squares support vector machine with mixture kernels

Bangzhu Zhu, Shunxin Ye, Ping Wang, Julien Chevallier and Yi‐ming Wei
Additional contact information
Julien Chevallier: LED - Laboratoire d'Economie Dionysien - UP8 - Université Paris 8 Vincennes-Saint-Denis

Post-Print from HAL

Abstract: Abstract For improving forecasting accuracy and trading performance, this paper proposes a new multi‐objective least squares support vector machine with mixture kernels to forecast asset prices. First, a mixture kernel function is introduced into taking full use of global and local kernel functions, which is adaptively determined following a data‐driven procedure. Second, a multi‐objective fitness function is proposed by incorporating level forecasting and trading performance, and particle swarm optimization is used to synchronously search the optimal model selections of least squares support vector machine with mixture kernels. Taking CO 2 assets as examples, the results obtained show that compared with the popular models, the proposed model can achieve higher forecasting accuracy and higher trading performance. The advantages of the mixture kernel function and the multi‐objective fitness function can improve the forecasting ability of the asset price. The findings also show that the models with a high‐level forecasting accuracy cannot always have a high trading performance of asset price forecasting. In contrast, high directional forecasting usually means a high trading performance.

Date: 2022-01
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Published in Journal of Forecasting, 2022, 41 (1), pp.100-117. ⟨10.1002/for.2784⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-04250287

DOI: 10.1002/for.2784

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().