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A Hybrid Model Based on Stochastic Volatility and Machine Learning to Forecast Log Returns of a Risky Asset

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Lorella Fatone

    (Università di Camerino, Dipartimento di Matematica)

  • Francesca Mariani

    (Università Politecnica delle Marche, Dipartimento di Scienze Economiche e Sociali)

  • Francesco Zirilli

Abstract

A hybrid model that combines a stochastic volatility model [2] and the K Nearest Neighbors (KNN) model [1] is proposed to obtain precision forecasts of log returns of a risky asset traded in the financial market. The precision forecasts are the sum of the forecasts obtained with the stochastic volatility model and a correction term produced by the KNN model. Numerical experiments based on real data are performed to investigate the accuracy of the precision forecasts.

Suggested Citation

  • Lorella Fatone & Francesca Mariani & Francesco Zirilli, 2022. "A Hybrid Model Based on Stochastic Volatility and Machine Learning to Forecast Log Returns of a Risky Asset," Springer Books, in: Marco Corazza & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 235-240, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-99638-3_38
    DOI: 10.1007/978-3-030-99638-3_38
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