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Complete subset least squares support vector regression

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  • Qiu, Yue

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.

Suggested Citation

  • Qiu, Yue, 2021. "Complete subset least squares support vector regression," Economics Letters, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:ecolet:v:200:y:2021:i:c:s0165176521000148
    DOI: 10.1016/j.econlet.2021.109737
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    References listed on IDEAS

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    4. Junjie Hu & Wolfgang Karl Hardle & Weiyu Kuo, 2019. "Risk of Bitcoin Market: Volatility, Jumps, and Forecasts," Papers 1912.05228, arXiv.org, revised Dec 2021.
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    7. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
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    Cited by:

    1. Gunnarsson, Elias Søvik & Isern, Håkon Ramon & Kaloudis, Aristidis & Risstad, Morten & Vigdel, Benjamin & Westgaard, Sjur, 2024. "Prediction of realized volatility and implied volatility indices using AI and machine learning: A review," International Review of Financial Analysis, Elsevier, vol. 93(C).
    2. Francesco Audrino & Jonathan Chassot, 2024. "HARd to Beat: The Overlooked Impact of Rolling Windows in the Era of Machine Learning," Papers 2406.08041, arXiv.org.

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    More about this item

    Keywords

    Complete subset regression; Machine learning; Bitcoin; Volatility forecasting;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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