Full 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.
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.
