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.
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Fatone, L., Mariani, F., Zirilli, F.: Calibration in the “real world” of a partially specified stochastic volatility model (2021, to be published)
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Fatone, L., Mariani, F., Zirilli, F. (2022). A Hybrid Model Based on Stochastic Volatility and Machine Learning to Forecast Log Returns of a Risky Asset. In: Corazza, M., Perna, C., Pizzi, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. MAF 2022. Springer, Cham. https://doi.org/10.1007/978-3-030-99638-3_38
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DOI: https://doi.org/10.1007/978-3-030-99638-3_38
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