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

Lorella Fatone (), Francesca Mariani () and Francesco Zirilli ()
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Lorella Fatone: Università di Camerino, Dipartimento di Matematica
Francesca Mariani: Università Politecnica delle Marche, Dipartimento di Scienze Economiche e Sociali

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2022, pp 235-240 from Springer

Abstract: 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.

Keywords: Precision forecast; Stochastic volatility model; Machine learning (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-99638-3_38

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DOI: 10.1007/978-3-030-99638-3_38

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