Statistical inference in multifractal random walk models for financial time series
Author(s)
Bibliographic Information
Statistical inference in multifractal random walk models for financial time series
（Volkswirtschaftliche Analysen, Bd. 18）
P. Lang, c2011
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Note
Originally presented as the author's thesis (doctoral)--Hamburg Universität, 2010
Includes bibliographical references (p. [97]-101)
Description and Table of Contents
Description
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.
Table of Contents
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.
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