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Unscented Kalman filter for noisy multivariate financial time-series data

Kalman filter is one of the novel techniques useful for statistical estimation theory and now widely used in many practical applications. In this paper, we consider the process of applying Unscented Kalman Filtering algorithm to multivariate financial time series data to determine if the algorithm c...

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Main Authors: Jadid Abdulkadir, S., Yong, S.-P.
Format: Article
Published: 2013
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84892425765&doi=10.1007%2f978-3-642-44949-9_9&partnerID=40&md5=43ff824734590a9478a4b9878861ec82
http://eprints.utp.edu.my/32949/
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spelling my.utp.eprints.329492022-03-30T01:10:51Z Unscented Kalman filter for noisy multivariate financial time-series data Jadid Abdulkadir, S. Yong, S.-P. Kalman filter is one of the novel techniques useful for statistical estimation theory and now widely used in many practical applications. In this paper, we consider the process of applying Unscented Kalman Filtering algorithm to multivariate financial time series data to determine if the algorithm could be used to smooth the direction of KLCI stock price movements using five different measurement variance values. Financial data are characterized by non-linearity, noise, chaotic in nature, volatile and the biggest impediment is due to the colossal nature of the capacity of transmitted data from the trading market. Unscented Kalman filter employs the use of unscented transformation commonly referred to as sigma points from which estimates are recovered from. The filtered output precisely internments the covariance of noisy input data producing smoothed and less noisy estimates. © 2013 Springer-Verlag. 2013 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84892425765&doi=10.1007%2f978-3-642-44949-9_9&partnerID=40&md5=43ff824734590a9478a4b9878861ec82 Jadid Abdulkadir, S. and Yong, S.-P. (2013) Unscented Kalman filter for noisy multivariate financial time-series data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8271 L . pp. 87-96. http://eprints.utp.edu.my/32949/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Kalman filter is one of the novel techniques useful for statistical estimation theory and now widely used in many practical applications. In this paper, we consider the process of applying Unscented Kalman Filtering algorithm to multivariate financial time series data to determine if the algorithm could be used to smooth the direction of KLCI stock price movements using five different measurement variance values. Financial data are characterized by non-linearity, noise, chaotic in nature, volatile and the biggest impediment is due to the colossal nature of the capacity of transmitted data from the trading market. Unscented Kalman filter employs the use of unscented transformation commonly referred to as sigma points from which estimates are recovered from. The filtered output precisely internments the covariance of noisy input data producing smoothed and less noisy estimates. © 2013 Springer-Verlag.
format Article
author Jadid Abdulkadir, S.
Yong, S.-P.
spellingShingle Jadid Abdulkadir, S.
Yong, S.-P.
Unscented Kalman filter for noisy multivariate financial time-series data
author_facet Jadid Abdulkadir, S.
Yong, S.-P.
author_sort Jadid Abdulkadir, S.
title Unscented Kalman filter for noisy multivariate financial time-series data
title_short Unscented Kalman filter for noisy multivariate financial time-series data
title_full Unscented Kalman filter for noisy multivariate financial time-series data
title_fullStr Unscented Kalman filter for noisy multivariate financial time-series data
title_full_unstemmed Unscented Kalman filter for noisy multivariate financial time-series data
title_sort unscented kalman filter for noisy multivariate financial time-series data
publishDate 2013
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84892425765&doi=10.1007%2f978-3-642-44949-9_9&partnerID=40&md5=43ff824734590a9478a4b9878861ec82
http://eprints.utp.edu.my/32949/
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score 13.211869

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