Statistical Inference in Multifractal Random Walk Models for Financial Time Series

Cristina Sattarhoff

ISBN 10: 3631606737 / ISBN 13: 9783631606735

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

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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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Titolo: Statistical Inference in Multifractal Random...

Casa editrice: Peter Lang Ltd. International Academic Publishers Apr 2011

Data di pubblicazione: 2011

Legatura: Taschenbuch

Condizione: Neu

Descrizione articolo

Riassunto:

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.

L'autore:

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.