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Statistical Inference in Multifractal Random Walk Models for Financial Time Series: 18 (Volkswirtschaftliche Analysen) - Tapa blanda
Sinopsis
The dynamics of financial returns varies with the return period, from high-frequency data to daily, quarterly or annual data. Multifractal Random Walk models can capture the statistical relation between returns and return periods, thus facilitating a more accurate representation of real price changes. This book provides a generalized method of moments estimation technique for the model parameters with enhanced performance in finite samples, and a novel testing procedure for multifractality. The resource-efficient computer-based manipulation of large datasets is a typical challenge in finance. In this connection, this book also proposes a new algorithm for the computation of heteroscedasticity and autocorrelation consistent (HAC) covariance matrix estimators that can cope with large datasets.
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Acerca del autor
Cristina Sattarhoff holds a Diploma in Business Administration from the University of Hamburg. From 2005 to 2010 she worked as a research assistant at the Institute of Statistics and Econometrics of the University of Hamburg and received her PhD in Economics.
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Statistical Inference in Multifractal Random Walk Models for Financial Time Series. Reihe: Volkswirtschaftliche Analysen - Band 18.
Publicado por
Frankfurt, Berlin, Bern, Bruxelles, New York, Oxford, Wien: Lang, 2011
ISBN 10: 3631606737
ISBN 13: 9783631606735
Antiguo o usado
Originalbroschur.
Librería: Borkert, Schwarz und Zerfaß GbR, Berlin, Alemania
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Originalbroschur. Condición: Wie neu. 101 S. Tadelloses Exemplar. - Contents: Financial econometrics - Multifractal volatility - Multifractal Random Walk - GMM estimation - Monte Carlo simulation study - Multifractality test - Empirical analysis of international stock index data - Financial markets efficiency - HAC estimation - Stylized facts of financial time series - Fat-tailed distribution - Scale invariance - MATLAB. - The dynamics of financial returns varies with the return period, from high-frequency data to daily, quarterly or annual data. Multifractal Random Walk models can capture the statistical relation between returns and return periods, thus facilitating a more accurate representation of real price changes. This book provides a generalized method of moments estimation technique for the model parameters with enhanced performance in finite samples, and a novel testing procedure for multifractality. The resource-efficient computer-based manipulation of large datasets is a typical challenge in finance. In this connection, this book also proposes a new algorithm for the computation of heteroscedasticity and autocorrelation consistent (HAC) covariance matrix estimators that can cope with large datasets. (Verlagstext). ISBN 9783631606735 Sprache: Deutsch Gewicht in Gramm: 550. Nº de ref. del artículo: 1023401
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Statistical Inference in Multifractal Random Walk Models for Financial Time Series
Publicado por
Peter Lang Ltd. International Academic Publishers Apr 2011, 2011
ISBN 10: 3631606737
ISBN 13: 9783631606735
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The dynamics of financial returns varies with the return period, from high-frequency data to daily, quarterly or annual data. Multifractal Random Walk models can capture the statistical relation between returns and return periods, thus facilitating a more accurate representation of real price changes. This book provides a generalized method of moments estimation technique for the model parameters with enhanced performance in finite samples, and a novel testing procedure for multifractality. The resource-efficient computer-based manipulation of large datasets is a typical challenge in finance. In this connection, this book also proposes a new algorithm for the computation of heteroscedasticity and autocorrelation consistent (HAC) covariance matrix estimators that can cope with large datasets. 102 pp. Englisch. Nº de ref. del artículo: 9783631606735
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Statistical Inference in Multifractal Random Walk Models for Financial Time Series
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The dynamics of financial returns varies with the return period, from high-frequency data to daily, quarterly or annual data. Multifractal Random Walk models can capture the statistical relation between returns and return periods, thus facilitating a more accurate representation of real price changes. This book provides a generalized method of moments estimation technique for the model parameters with enhanced performance in finite samples, and a novel testing procedure for multifractality. The resource-efficient computer-based manipulation of large datasets is a typical challenge in finance. In this connection, this book also proposes a new algorithm for the computation of heteroscedasticity and autocorrelation consistent (HAC) covariance matrix estimators that can cope with large datasets.; Dissertationsschrift. Nº de ref. del artículo: 9783631606735
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Statistical Inference in Multifractal Random Walk Models for Financial Time Series
Librería: preigu, Osnabrück, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Statistical Inference in Multifractal Random Walk Models for Financial Time Series | Cristina Sattarhoff | Taschenbuch | Englisch | Peter Lang | EAN 9783631606735 | Verantwortliche Person für die EU: Kolibri 360 GmbH, Ettore-Bugatti-Str. 6-14, 51149 Köln, produktsicherheit[at]kolibri360[dot]de | Anbieter: preigu. Nº de ref. del artículo: 103831627
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