Loading…
Forecasting carbon price using a multi‐objective least squares support vector machine with mixture kernels
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...
Saved in:
Published in: | Journal of forecasting 2022-01, Vol.41 (1), p.100-117 |
---|---|
Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | 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 CO2 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. |
---|---|
ISSN: | 0277-6693 1099-131X |
DOI: | 10.1002/for.2784 |