%0 Book
%A Cristina Sattarhoff
%D 2012
%C Berlin, Germany
%I Peter Lang Verlag
%@ 9783653007954
%T Statistical Inference in Multifractal Random Walk Models for Financial Time Series
%R 10.3726/978-3-653-00795-4
%U https://www.peterlang.com/document/1044459
%X 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.
%K financial volatility, HAC estimation, GMM estimation, financial marketes efficiency
%G English