Complete subset least squares support vector regression. (English) Zbl 1462.62642
Summary: In this paper, we propose a new method for combining forecasts based on complete subset least squares support vector regressions (LSSVR\(^{\text{CS}})\) that is applicable to both linear and nonlinear data generation processes. Our LSSVR\(^{\text{CS}}\) 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 LSSVR\(^{\text{CS}}\) outperforms many other competing approaches. The out-of-sample performance of the LSSVR\(^{\text{CS}}\) method is examined in an analysis for predicting Bitcoin realized volatility. The results favor our method relative to others.
MSC:
| 62P05 | Applications of statistics to actuarial sciences and financial mathematics |
| 62J07 | Ridge regression; shrinkage estimators (Lasso) |
| 62M20 | Inference from stochastic processes and prediction |
| 68T05 | Learning and adaptive systems in artificial intelligence |
Keywords:
complete subset regression; ridge regression; machine learning; Bitcoin; volatility forecastingReferences:
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