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.
