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.
A Hybrid Model Based on Stochastic Volatility and Machine Learning to Forecast Log Returns of a Risky Asset / Fatone, Lorella; Mariani, Francesca; Zirilli, Francesco. - (2022), pp. 235-240. (Intervento presentato al convegno Mathematical and Statistical Methods for Actuarial Sciences and Finance tenutosi a University of Salerno, 20-22 April 2022) [10.1007/978-3-030-99638-3_38].
A Hybrid Model Based on Stochastic Volatility and Machine Learning to Forecast Log Returns of a Risky Asset
Mariani, Francesca
;
2022-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
