Volume: 8, Issue: 4(1997)
pp. 399-415 DOI: 10.1142/S0129065797000409
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Abstract |
Full Text (PDF, 553KB)
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| Title: |
A Constrained Neural Network Kalman Filter for Price Estimation
in High Frequency Financial Data |
| Author(s): |
Peter J. Bolland
London Business School,
Department of Decision Science, Sussex Place, Regents Park,
London NW1 4SA, UKJerome T. Connor
London Business School,
Department of Decision Science, Sussex Place, Regents Park,
London NW1 4SA, UK
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| Abstract: |
In this paper we present a neural network extended Kalman filter for modeling
noisy financial time series. The neural network is employed to estimate the
nonlinear dynamics of the extended Kalman filter. Conditions for the neural
network weight matrix are provided to guarantee the stability of the filter. The
extended Kalman filter presented is designed to filter three types of noise
commonly observed in financial data: process noise, measurement noise, and
arrival noise. The erratic arrival of data (arrival noise) results in the
neural network predictions being iterated into the future. Constraining the
neural network to have a fixed point at the origin produces better iterated
predictions and more stable results. The performance of constrained and
unconstrained neural networks within the extended Kalman filter is demonstrated
on "Quote" tick data from the $/DM exchange rate
(1993–1995). |
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