Modelling and Forecasting Multivariate Realized Volatility


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CHIRIAC, Roxana, Valeri VOEV, 2008. Modelling and Forecasting Multivariate Realized Volatility

@techreport{Chiriac2008Model-12161, series={CoFE-Diskussionspapiere / Zentrum für Finanzen und Ökonometrie}, title={Modelling and Forecasting Multivariate Realized Volatility}, year={2008}, number={2008/06}, author={Chiriac, Roxana and Voev, Valeri} }

<rdf:RDF xmlns:rdf="" xmlns:bibo="" xmlns:dc="" xmlns:dcterms="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dc:creator>Chiriac, Roxana</dc:creator> <dcterms:title>Modelling and Forecasting Multivariate Realized Volatility</dcterms:title> <dcterms:available rdf:datatype="">2011-03-25T09:43:08Z</dcterms:available> <bibo:uri rdf:resource=""/> <dc:format>application/pdf</dc:format> <dc:language>eng</dc:language> <dc:creator>Voev, Valeri</dc:creator> <dcterms:rights rdf:resource=""/> <dc:rights>deposit-license</dc:rights> <dc:contributor>Voev, Valeri</dc:contributor> <dcterms:abstract xml:lang="eng">This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions. We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model s forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies that any risk-averse investor, regardless of the type of utility function, would be better-off using our model.</dcterms:abstract> <dcterms:issued>2008</dcterms:issued> <dc:contributor>Chiriac, Roxana</dc:contributor> <dc:date rdf:datatype="">2011-03-25T09:43:08Z</dc:date> </rdf:Description> </rdf:RDF>

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