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Statistics - Business, Economics & Finance | Statistical Inference for Financial Engineering

Statistical Inference for Financial Engineering

Taniguchi, M., Amano, T., Ogata, H., Taniai, H.

2014, X, 118 p. 15 illus., 6 illus. in color.

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  • Prepares readers for analyzing the specific feature of financial data
  • Provides powerful statistical tools (e.g. the LAN-based approach, empirical likelihood, control variates, quantile regression, etc.)
  • Reflects the latest developments (e.g., stable distributions, market microstructure etc.)

​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.

Content Level » Graduate

Keywords » 62P05, 91G70 - LAN-based optimal inference for time series - empirical likelihood - financial time series - non-linear / non-Gaussian models - rank-based semiparametric inference

Related subjects » Business, Economics & Finance - Financial Economics - Quantitative Finance

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