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.