Computational finance deals with the mathematics of computer programs that realize financial models or systems. This book outlines the epistemic risks associated with the current valuations of different financial instruments and discusses the corresponding risk management strategies. It covers most of the research and practical areas in computational finance. Starting from traditional fundamental analysis and using algebraic and geometric tools, it is guided by the logic of science to explore information from financial data without prejudice. In fact, this book has the unique feature that it is structured around the simple requirement of objective science: the geometric structure of the data = the information contained in the data.
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The book analyzes the epistemic risks associated with the current valuations of financial instruments and their derivatives and discusses the corresponding adjusted risk management strategies. It covers most of the current research and practical areas in computational finance and corrects many of the common errors propagated in the financial literature. Starting from traditional fundamental financial analysis and using various algebraic and geometric tools, like 3- and 4-dimensional visualizations, this well-illustrated book is guided by the logic of science to explore information from uncertain financial data without prejudice. It is structured around the fundamental requirement of objective science that the (geometric) structure of the data equals the information (model) contained in the data. Numerous real world empirical examples, collected by the author during his twenty year professional career, as a Senior and Chief Economist on Wall Street (Fed, Nomura, ING, etc.), elaborate on the points made. Detailed footnotes introduce many historical characters, who have presented similar arguments in physics and mathematics. The intended readership consists of undergraduate (3rd year and Honours) and graduate (MBA, MA and Ph.D) students in finance, who have some knowledge of elementary calculus and linear algebra, as well as sophisticated practitioners in the financial services industries.
How to do financial modeling without prejudice......This book grew out of an invited, and very well attended public lecture on ?A Scientific View of Economic and Financial Data Analysis,? which the author before the New York Academy of Sciences in New York City on March 11, 1992. That invitation came from Professors Lawrence Klein (Nobel Memorial prize winner), Edmund Phelps (Member of Academy of Sciences USA) and Dominick Salvatore.......
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