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dc.contributor.authorEnsor, Katherine Bennett
Koev, Ginger M.
dc.date.accessioned 2017-06-15T15:30:09Z
dc.date.available 2017-06-15T15:30:09Z
dc.date.issued 2014
dc.identifier.citation Ensor, Katherine Bennett and Koev, Ginger M.. "Computational finance: correlation, volatility, and markets." WIREs Computational Statistics, 6, no. 5 (2014) 326-340. http://dx.doi.org/10.1002/wics.1323.
dc.identifier.urihttp://hdl.handle.net/1911/94866
dc.description.abstract Financial 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.language.iso eng
dc.rights This 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.title Computational finance: correlation, volatility, and markets
dc.type Journal article
dc.citation.journalTitle WIREs Computational Statistics
dc.subject.keywordco-volatility forecasting
dynamic conditional correlation
GARCH/MGARCH
regime switching
stock volatility
dc.citation.volumeNumber 6
dc.citation.issueNumber 5
dc.contributor.publisher Wiley
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1002/wics.1323
dc.type.publication publisher version
dc.citation.firstpage 326
dc.citation.lastpage 340


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This 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.
Except where otherwise noted, this item's license is described as This 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.