Statistical Inference in Multifractal Random Walk Models for Financial Time Series

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.

Autorentext

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.

Zusammenfassung
"[...] 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)

Inhalt
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.