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Extracting Garch Effects from Asset Returns Using Robust NMF
Complex and Adaptive Systems Laboratory, University College Dublin, Ireland.ORCID iD: 0000-0002-3912-1470
Complex and Adaptive Systems Laboratory, University College Dublin, Ireland.
Complex and Adaptive Systems Laboratory, University College Dublin, Ireland.
2009 (English)In: Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009. DSP/SPE 2009. IEEE 13th, Marco Island, FL: IEEE Signal Processing Society, 2009, 200-5 p.Conference paper, Published paper (Refereed)
Abstract [en]

Identification of assets on the stock market that exhibit co-movement is a critical task for generating an efficiently diversified portfolio. We present a new application of non-negative matrix factorization to factor analysis of financial time series. We consider a conditionally heteroscedastic latent factor model, where each series is parameterized by a univariate ARCH model. Volatility clustering characteristics, e.g. GARCH effects, of the constituent assets of the Dow Jones Industrial Average are lever-aged to cluster assets based on the commonality of their volatility clusters. We present a new non-negative matrix factorization algorithm which is robust in the presence of noise, Robust NMF. We use a mixed low-rank over-complete dictionary learning approach to separate out the background Gaussian noise, emphasize the GARCH effects and achieve clearer asset groupings.

Place, publisher, year, edition, pages
Marco Island, FL: IEEE Signal Processing Society, 2009. 200-5 p.
Keyword [en]
Gaussian noise, learning (artificial intelligence), matrix decomposition, pattern clustering, stock markets, time series, Dow Jones Industrial Average, GARCH effects, asset groupings, asset identification, asset returns, background Gaussian noise, cluster assets, dictionary learning approach, diversified portfolio, factor analysis, financial time series, heteroscedastic latent factor model, non-negative matrix factorization, robust NMF, stock market, univariate ARCH model, volatility clustering characteristics, Adaptive systems, Autocorrelation, Educational institutions, Laboratories, Matrix decomposition, Noise robustness, Portfolios, Probability distribution, Stock markets, Time series analysis, Autoregressive Conditional Heteroscedasticity, Clustering, Low rank decomposition, Non-negative Matrix Factorization, Sparseness
National Category
Signal Processing
Research subject
Applied and Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-174162DOI: 10.1109/DSP.2009.4785921ISI: 000267715800037Scopus ID: 2-s2.0-63649088746ISBN: 978-1-4244-3677-4 (print)OAI: oai:DiVA.org:kth-174162DiVA: diva2:858340
Conference
DSP/SPE 2009
Note

QC 20151005

Available from: 2015-10-01 Created: 2015-10-01 Last updated: 2015-10-05Bibliographically approved

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de Fréin, Ruairí

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CiteExportLink to record
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  • apa
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