Nikolaev, Nikolay; Tino, Peter and Smirnov, Evgueni. 2011. Time-Dependent Series Variance Estimation via Recurrent Neural Networks. Artificial Neural Networks and Machine Learning – ICANN 2011, 6791(n/a), pp. 176-184. ISSN 0302-9743 [Article]

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Abstract or Description

This paper presents a nonlinear model for computing the time-dependent evolution of the variance in time series of returns on assets. First, we design a recurrent network representation of the variance, which extends the typically linear models. Second, we derive temporal training equations with which the network weights are inferred so as to maximize the likelihood of the data. Experimental results show that this dynamic recurrent network model yields results with improved statistical characteristics and economic performance.

Item Type:	Article
Identification Number (DOI):	https://doi.org/10.1007/978-3-642-21735-7_22
Departments, Centres and Research Units:	Computing
Dates:	Date Event 2011 Published
Item ID:	6797
Date Deposited:	12 Apr 2012 11:23
Last Modified:	20 Jun 2017 11:17
Peer Reviewed:	Yes, this version has been peer-reviewed.
URI:	https://research.gold.ac.uk/id/eprint/6797

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