Complete subset least squares support vector regression

Yue Qiu

Economics Letters, 2021, vol. 200, issue C

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.

Keywords: Complete subset regression; Machine learning; Bitcoin; Volatility forecasting (search for similar items in EconPapers)
JEL-codes: C52 C53 G17 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:200:y:2021:i:c:s0165176521000148

DOI: 10.1016/j.econlet.2021.109737

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