Complete subset least squares support vector regression

【Author】 Qiu, Yue

【Source】ECONOMICS LETTERS

【影响因子】1.469

【Abstract】In this paper, we propose a new method for combining forecasts based on complete subset least squares support vector regressions (LSSVRCS) that is applicable to both linear and nonlinear data generation processes. Our LSSVRCS is very flexible that it can incorporate other methods, like ridge regression or complete subset regression, as special cases. In a Monte Carlo simulation experiment, our LSSVRCS outperforms many other competing approaches. The out-of-sample performance of the LSSVRCS method is examined in an analysis for predicting Bitcoin realized volatility. The results favor our method relative to others. (c) 2021 Elsevier B.V. All rights reserved.

【Keywords】Complete subset regression; Machine learning; Bitcoin; Volatility forecasting

【发表时间】2021 MAR

【收录时间】2022-01-02

【文献类型】

【主题类别】

--