Computational finance: correlation, volatility, and markets

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dc.citation.firstpage326
dc.citation.issueNumber5
dc.citation.journalTitleWIREs Computational Statistics
dc.citation.lastpage340
dc.citation.volumeNumber6
dc.contributor.authorEnsor, Katherine Bennett
dc.contributor.authorKoev, Ginger M.
dc.date.accessioned2017-06-15T15:30:09Z
dc.date.available2017-06-15T15:30:09Z
dc.date.issued2014
dc.description.abstractFinancial data by nature are inter-related and should be analyzed using multivariate methods. Many models exist for the joint analysis of multiple financial instruments. Early models often assumed some type of constant behavior between the instruments over the time period of analysis. But today, time-varying covariance models are a key component of financial time series analysis leading to a deeper understanding of changing market conditions. Models for covolatility of financial data quickly grow in their complexity and parameters, and 20 years of research offers a variety of solutions to this complexity. After a short introduction of univariate volatility models, this article begins with the basic multivariate formulation for time series covariance modeling and moves to leading time series tools that address this complexity. Coupling these models with regime switching via a Markov process extends the features that can be understood from market behavior. We ground this review in an example of modeling the covariance of securities within sectors and sectors within markets, with dynamics that allow for two different market regimes. Specifically, we simultaneously model individual daily stock data that belong to one of three market sectors and examine the behavior of the market as a whole as well as the behavior of the market sectors over time. A motivation for this characterization concerns portfolio diversification and stock anomalies, and we capture the changing comovement of stocks within and between sectors as market conditions change. For example, some of these market conditions include market crashes or collapses and common external influences.
dc.identifier.citationEnsor, Katherine Bennett and Koev, Ginger M.. "Computational finance: correlation, volatility, and markets." <i>WIREs Computational Statistics,</i> 6, no. 5 (2014) Wiley: 326-340. http://dx.doi.org/10.1002/wics.1323.
dc.identifier.doihttp://dx.doi.org/10.1002/wics.1323
dc.identifier.urihttps://hdl.handle.net/1911/94866
dc.language.isoeng
dc.publisherWiley
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/us/
dc.subject.keywordco-volatility forecasting
dc.subject.keyworddynamic conditional correlation
dc.subject.keywordGARCH/MGARCH
dc.subject.keywordregime switching
dc.subject.keywordstock volatility
dc.titleComputational finance: correlation, volatility, and markets
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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