Statistical Inference For Financial Engineering
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Statistical Inference for Financial Engineering
Author: Masanobu Taniguchi
language: en
Publisher: Springer Science & Business Media
Release Date: 2014-03-26
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.
Optimal Statistical Inference in Financial Engineering
Until now, few systematic studies of optimal statistical inference for stochastic processes had existed in the financial engineering literature, even though this idea is fundamental to the field. Balancing statistical theory with data analysis, Optimal Statistical Inference in Financial Engineering examines how stochastic models can effectively des
Financial Engineering
"Financial Engineering: Statistics and Data Analysis" is a comprehensive guide tailored for professionals and students navigating the dynamic landscape of finance. We encapsulate the pivotal role of statistics and data analysis in the modern financial industry, where data-driven insights are essential for informed decision-making and risk management. Through a meticulous blend of theoretical foundations and practical applications, this book equips readers with the analytical tools necessary to tackle complex financial challenges with confidence. From understanding key statistical concepts to leveraging advanced data analysis techniques, each chapter deepens the reader's proficiency in analyzing financial data and extracting actionable insights. Whether exploring risk management strategies, portfolio optimization techniques, or financial modeling methodologies, this book serves as a trusted companion for mastering financial analysis intricacies. With real-world examples, case studies, and hands-on exercises, readers are empowered to apply theoretical concepts to real-world scenarios, enhancing their ability to navigate today's financial markets. "Financial Engineering: Statistics and Data Analysis" is not just a textbook; it's a roadmap for success in financial engineering, offering invaluable insights for professionals and students alike.