Statistical Inference in Multifractal Random Walk Models for Financial Time Series
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Beschreibung
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.
Autor*in
Cristina Sattarhoff
Themen in »Statistical Inference in Multifractal Random Walk Models for Financial Time Series«
Cristina Financial financial marketes efficiency financial volatility GMM estimation HAC estimation Inference Models Multifractal Peter Random Sattarhoff Series Stahlecker Statistical
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«[...] I find that the essential concept of multifractality is explained rather well given the length of the book. The book is written in an understandable way and is easy to read. Therefore, I find the book a very well realized introduction to MMF, espacially MRW. As a result, it is highly recommendable as introductory literature for any reader with interest in this research field.» (Vahidin Jeleskovic, Jahrbücher für Nationalökonomie und Statistik 232, 2012/6)
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