Isbn: 9783631606735 - statistical inference in multifractal random walk models for financial time series: 18 (volkswirtschaftliche analysen) (3 resultados)

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  • Idioma: Alemán

    Editorial: Frankfurt, Berlin, Bern, Bruxelles, New York, Oxford, Wien: Lang, 2011

    3631606737 / 9783631606735

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    Librería: Borkert, Schwarz und Zerfaß GbR, Berlin, AlemaniaBorkert, Schwarz und Zerfaß GbR

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    Miembro de asociación: BOEVGIAQ

    Condición: Usado - Como Nuevo

    EUR 7,00

    Envío por EUR 40,00 
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    Cantidad disponible: 1 disponibles

    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.

  • Idioma: Inglés

    Editorial: Peter Lang, 2011

    3631606737 / 9783631606735

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    Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

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    Condición: Nuevo

    EUR 33,75

    Envío por EUR 60,75 
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    Cantidad disponible: 2 disponibles

    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.

  • Idioma: Inglés

    Editorial: Peter Lang Ltd. International Academic Publishers Apr 2011, 2011

    3631606737 / 9783631606735

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    • Impresión bajo demanda

    Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.

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    Condición: Nuevo

    EUR 33,75

    Envío por EUR 23,00 
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    Cantidad disponible: 2 disponibles

    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.