Financial Engineering and Computation: Principles, Mathematics, and Algorithms

Annotation Nowadays students and professionals intending to work in any area of finance must master not only advanced concepts and mathematical models but also learn how to implement these models computationally. This comprehensive text combines the theory and mathematics behind financial engineering with an emphasis on computation, in keeping with the way financial engineering is practiced in today's capital markets. Unlike most books on investments, financial engineering, or derivative securities, the book starts from very basic ideas in finance and gradually builds up the theory. It offers a thorough grounding in the subject for MBAs in finance, students of engineering and sciences who are pursuing a career in finance, researchers in computational finance, system analysts, and financial engineers. Along with the theory, the author presents numerous algorithms for pricing, risk management, and portfolio management. The emphasis is on pricing financial and derivative securities: bonds, options, futures, forwards, interest rate derivatives, mortgage-backed securities, bonds with embedded options, and more. Each instrument is treated in a short, self-contained chapter for ready reference use. Many of these algorithms are coded in Java as programs for the Web, available from the book's home page (www.csie.ntu.edu/~lyuu/Capitals/capitals.htm) Students And Professionals Intending To Work In Any Area Of Finance Must Master Not Only Advanced Concepts And Mathematical Models But Also Learn How To Implement These Models Computationally. This Comprehensive Text, First Published In 2002, Combines The Theory And Mathematics Behind Financial Engineering With An Emphasis On Computation, In Keeping With The Way Financial Engineering Is Practised In Capital Markets. Unlike Most Books On Investments, Financial Engineering, Or Derivative Securities, The Book Starts From Very Basic Ideas In Finance And Gradually Builds Up The Theory. It Offers A Thorough Grounding In The Subject For Mbas In Finance, Students Of Engineering And Sciences Who Are Pursuing A Career In Finance, Researchers In Computational Finance, System Analysts, And Financial Engineers. Along With The Theory, The Author Presents Numerous Algorithms For Pricing, Risk Management, And Portfolio Management. The Emphasis Is On Pricing Financial And Derivative Securities: Bonds, Options, Futures, Forwards, Interest Rate Derivatives, Mortgage-backed Securities, Bonds With Embedded Options, And More. 1. Introduction -- 2. Analysis Of Algorithms -- 3. Basic Financial Mathematics -- 4. Bond Price Volatility -- 5. Term Structure Of Interest Rates -- 6. Fundamental Statistical Concepts -- 7. Option Basics -- 8. Arbitrage In Option Pricing -- 9. Option Pricing Models -- 10. Sensitivity Analysis Of Options -- 11. Extensions Of Options Theory -- 12. Forwards, Futures, Futures Options, Swaps -- 13. Stochastic Processes And Brownian Motion -- 14. Continuous-time Financial Mathematics -- 15. Continuous-time Derivatives Pricing -- 16. Hedging -- 17. Trees -- 18. Numerical Methods -- 19. Matrix Computation -- 20. Time Series Analysis -- 21. Interest Rate Derivative Securities -- 22. Term Structure Fitting. Yuh-dauh Lyuu. Includes Bibliographical References (p. 553-583) And Index. Machine Learning involves several scientific domains including mathematics, computer science, statistics and biology, and is an approach that enables computers to automatically learn from data. Focusing on complex media and how to convert raw data into useful information, this book offers both introductory and advanced material in the combined fields of machine learning and image/video processing. The machine learning techniques presented enable readers to address many real world problems involving complex data. Examples covering areas such as automatic speech and handwriting transcription, automatic face recognition, and semantic video segmentation are included, along with detailed introductions to algorithms and examples of their applications. The book is organized in four parts: The first focuses on technical aspects, basic mathematical notions and elementary machine learning techniques. The second provides an extensive survey of most relevant machine learning techniques for media processing, while the third part focuses on applications and shows how techniques are applied in actual problems. The fourth part contains detailed appendices that provide notions about the main mathematical instruments used throughout the text. Students and researchers needing a solid foundation or reference, and practitioners interested in discovering more about the state-of-the-art will find this book invaluable. Examples and problems are based on data and software packages publicly available on the web Nowadays students and professionals intending to work in any area of finance must master not only advanced concepts and mathematical models but also learn how to implement these models computationally. This comprehensive text combines the theory and mathematics behind financial engineering with an emphasis on computation, in keeping with the way financial engineering is practiced in today's capital markets. Unlike most books on investments, financial engineering, or derivative securities, the book starts from very basic ideas in finance and gradually builds up the theory. It offers a thorough grounding in the subject for MBAs in finance, students of engineering and sciences who are pursuing a career in finance, researchers in computational finance, system analysts, and financial engineers. Along with the theory, the author presents numerous algorithms for pricing, risk management, and portfolio management. The emphasis is on pricing financial and derivative bonds, options, futures, forwards, interest rate derivatives, mortgage-backed securities, bonds with embedded options, and more. Each instrument is treated in a short, self-contained chapter for ready reference use. Many of these algorithms are coded in Java as programs for the Web, available from the book's home page ((http://www.csie.ntu.edu/~lyuu/Capitals/capitals.htm) www.csie.ntu.edu/~lyuu/Capitals/capit... ) Nowadays students and professionals intending to work in any area of finance must master not only advanced concepts and mathematical models but also learn how to implement these models computationally. This comprehensive text combines the theory and mathematics behind financial engineering with an emphasis on computation, in keeping with the way financial engineering is practised in today's capital markets. Unlike most books on investments, financial engineering, or derivative securities, the book starts from very basic ideas in finance and gradually builds up the theory. It offers a thorough grounding in the subject for MBAs in finance, students of engineering and sciences who are pursuing a career in finance, researchers in computational finance, system analysts, and financial engineers. Along with the theory, the author presents numerous algorithms for pricing, risk management, and portfolio management. The emphasis is on pricing financial and derivative securities: bonds, options, futures, forwards, interest rate derivatives, mortgage-backed securities, bonds with embedded options, and more Focusing on complex media and how to convert raw data into useful information, this book offers both introductory and advanced material in the combined fields of machine learning and image/video processing. It is organized into three parts. The first focuses on technical aspects, basic mathematical notions and elementary machine learning techniques. The second provides an extensive survey of most relevant machine learning techniques for media processing. The third focuses on applications and shows how techniques are applied in actual problems. Examples and problems are based on data and software packages publicly available on the web. This book illustrates how to deal with complex media and convert raw data into useful information. Students and researchers needing a solid foundation or reference, and practitioners interested in discovering more about the state-of-the-art will find this book invaluable. Illustrates how to deal with complex media and convert raw data into useful information. Once the original data have been converted into a digital representation, the processing of different media can be performed under the unifying framework of machine learning