Volatility prediction with mixture density networks

2024/01/18 by Schittenkopf, Christian, Dorffner, Georg, Dockner, Engelbert J.

paper · doi:10.57938/03053498-7a01-4f07-a998-1c1837d98604

Abstract

Despite the lack of a precise definition of volatility in finance, the estimation of volatility and its prediction is an important problem. In this paper we compare the performance of standard volatility models and the performance of a class of neural models, i.e. mixture density networks (MDNs). First experimental results indicate the importance of long-term memory of the models as well as the benefit of using non-gaussian probability densities for practical applications. (author's abstract)

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