Computational finance: correlation, volatility, and markets

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dc.citation.firstpage326en_US
dc.citation.issueNumber5en_US
dc.citation.journalTitleWIREs Computational Statisticsen_US
dc.citation.lastpage340en_US
dc.citation.volumeNumber6en_US
dc.contributor.authorEnsor, Katherine Bennetten_US
dc.contributor.authorKoev, Ginger M.en_US
dc.date.accessioned2017-06-15T15:30:09Zen_US
dc.date.available2017-06-15T15:30:09Zen_US
dc.date.issued2014en_US
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.en_US
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.en_US
dc.identifier.doihttp://dx.doi.org/10.1002/wics.1323en_US
dc.identifier.urihttps://hdl.handle.net/1911/94866en_US
dc.language.isoengen_US
dc.publisherWileyen_US
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.en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/us/en_US
dc.subject.keywordco-volatility forecastingen_US
dc.subject.keyworddynamic conditional correlationen_US
dc.subject.keywordGARCH/MGARCHen_US
dc.subject.keywordregime switchingen_US
dc.subject.keywordstock volatilityen_US
dc.titleComputational finance: correlation, volatility, and marketsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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