{ "item_title" : "Statistical Inference in Multifractal Random Walk Models for Financial Time Series", "item_author" : [" Peter Stahlecker", "Cristina Sattarhoff "], "item_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.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/3/63/160/673/3631606737_b.jpg", "price_data" : { "retail_price" : "42.10", "online_price" : "42.10", "our_price" : "42.10", "club_price" : "42.10", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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