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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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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.
ISBN 10: 3631606737
ISBN 13: 9783631606735
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Da: Borkert, Schwarz und Zerfaß GbR, Berlin, Germania
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Originalbroschur. Condizione: 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. Codice articolo 1023401
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Statistical Inference in Multifractal Random Walk Models for Financial Time Series
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Peter Lang Ltd. International Academic Publishers Apr 2011, 2011
ISBN 10: 3631606737
ISBN 13: 9783631606735
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: 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. Codice articolo 9783631606735
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Statistical Inference in Multifractal Random Walk Models for Financial Time Series
Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: 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. Codice articolo 9783631606735
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Statistical Inference in Multifractal Random Walk Models for Financial Time Series
Da: preigu, Osnabrück, Germania
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Taschenbuch. Condizione: 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: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 103831627
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