TY - UNPB
T1 - Volatility prediction with mixture density networks
AU - Schittenkopf, Christian
AU - Dorffner, Georg
AU - Dockner, Engelbert J.
PY - 1998
Y1 - 1998
N2 - 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)
AB - 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)
U2 - 10.57938/03053498-7a01-4f07-a998-1c1837d98604
DO - 10.57938/03053498-7a01-4f07-a998-1c1837d98604
M3 - WU Working Paper and Case
T3 - Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
BT - Volatility prediction with mixture density networks
PB - SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business
CY - Vienna
ER -