Statistical Inference for Financial Engineering

​This monograph provides the fundamentals of statistical inference for financial engineering and covers some selected methods suitable for analyzing financial time series data. In order to describe the actual financial data, various stochastic processes, e.g. non-Gaussian linear processes, non-linear processes, long-memory processes, locally stationary processes etc. are introduced and their optimal estimation is considered as well. This book also includes several statistical approaches, e.g., discriminant analysis, the empirical likelihood method, control variate method, quantile regression, realized volatility etc., which have been recently developed and are considered to be powerful tools for analyzing the financial data, establishing a new bridge between time series and financial engineering.

This book is well suited as a professional reference book on finance, statistics and statistical financial engineering. Readers are expected to have an undergraduate-level knowledge of statistics.

Dr. Masanobu Taniguchi is a professor at Waseda University. His work focuses on time series, general asymptotic theory and econometrics and he is a fellow of the Institute of Mathematical Statistics (USA).

Dr. Tomoyuki Amano received his PhD from Waseda University, Japan and is now an associate professor at the Faculty of Economics, Wakayama University, Japan. His research interests are in financial time series and function estimators for time series.

Dr. Hiroaki Ogata is an assistant professor at the School of International Liberal Studies, Waseda University. He is currently researching empirical likelihood estimation methods in time series analysis, as well as in stable distributions.

Dr. Hiroyuki Taniai completed his PhD at Université Libre de Bruxelles and is now a research associate at the School of International Liberal Studies, Waseda University. His research interests include semiparametric inference, quantile regression and their applications in finance.