Paolo Brandimarte - Wiley Handbooks in Financial Engineering and Econometrics: Handbook in Monte Carlo Simulation : Applications in Financial Engineering, Risk Management, and Economics ebook FB2, PDF, EPUB
9780470531112 0470531118 An accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economics Providing readers with an in-depth and comprehensive guide, the "Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics "presents a timely account of the applicationsof Monte Carlo methods in financial engineering and economics. Written by an international leading expert in thefield, the handbook illustrates the challenges confronting present-day financial practitioners and provides various applicationsof Monte Carlo techniques to answer these issues. The book is organized into five parts: introduction andmotivation; input analysis, modeling, and estimation; random variate and sample path generation; output analysisand variance reduction; and applications ranging from option pricing and risk management to optimization. The "Handbook in Monte Carlo Simulation "features: An introductory section for basic material on stochastic modeling and estimation aimed at readers who may need a summary or review of the essentials Carefully crafted examples in order to spot potential pitfalls and drawbacks of each approach An accessible treatment of advanced topics such as low-discrepancy sequences, stochastic optimization, dynamic programming, risk measures, and Markov chain Monte Carlo methods Numerous pieces of R code used to illustrate fundamental ideas in concrete terms and encourage experimentation The "Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics "is a complete reference for practitioners in the fields of finance, business, applied statistics, econometrics, and engineering, as well as a supplement for MBA and graduate-level courses on Monte Carlo methods and simulation., Concentrating primarily on easily displayed theories and methodologies of Monte Carlo simulation, this authoritative book goes wider and deeper than any other and includes timely applications to the fields of financial engineering, risk management, and economics. Written by a well-known, international expert in the field, the book includes topics such as random number and variate generation, input modeling with real data analysis for adequate fit, Bayesian MCMC, and more. It is a handy reference for practitioners in the fields of finance, business, applied statistics, econometrics, and engineering, The Handbook in Monte Carlo Simulation Applications in Financial Engineering, Risk Management, and Economics features: An introductory section for basic material on stochastic modeling and estimation aimed at readers who may need a summary or review of the essentials Carefully crafted examples in order to spot potential pitfalls and drawbacks of each approach An accessible treatment of advanced topics such as low discrepancy sequences, stochastic optimization, dynamic programming, risk measures, and Markov chain Monte Carlo methods Numerous pieces of R code used to illustrate fundamental ideas in concrete terms and encourage experimentation.
9780470531112 0470531118 An accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economics Providing readers with an in-depth and comprehensive guide, the "Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics "presents a timely account of the applicationsof Monte Carlo methods in financial engineering and economics. Written by an international leading expert in thefield, the handbook illustrates the challenges confronting present-day financial practitioners and provides various applicationsof Monte Carlo techniques to answer these issues. The book is organized into five parts: introduction andmotivation; input analysis, modeling, and estimation; random variate and sample path generation; output analysisand variance reduction; and applications ranging from option pricing and risk management to optimization. The "Handbook in Monte Carlo Simulation "features: An introductory section for basic material on stochastic modeling and estimation aimed at readers who may need a summary or review of the essentials Carefully crafted examples in order to spot potential pitfalls and drawbacks of each approach An accessible treatment of advanced topics such as low-discrepancy sequences, stochastic optimization, dynamic programming, risk measures, and Markov chain Monte Carlo methods Numerous pieces of R code used to illustrate fundamental ideas in concrete terms and encourage experimentation The "Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics "is a complete reference for practitioners in the fields of finance, business, applied statistics, econometrics, and engineering, as well as a supplement for MBA and graduate-level courses on Monte Carlo methods and simulation., Concentrating primarily on easily displayed theories and methodologies of Monte Carlo simulation, this authoritative book goes wider and deeper than any other and includes timely applications to the fields of financial engineering, risk management, and economics. Written by a well-known, international expert in the field, the book includes topics such as random number and variate generation, input modeling with real data analysis for adequate fit, Bayesian MCMC, and more. It is a handy reference for practitioners in the fields of finance, business, applied statistics, econometrics, and engineering, The Handbook in Monte Carlo Simulation Applications in Financial Engineering, Risk Management, and Economics features: An introductory section for basic material on stochastic modeling and estimation aimed at readers who may need a summary or review of the essentials Carefully crafted examples in order to spot potential pitfalls and drawbacks of each approach An accessible treatment of advanced topics such as low discrepancy sequences, stochastic optimization, dynamic programming, risk measures, and Markov chain Monte Carlo methods Numerous pieces of R code used to illustrate fundamental ideas in concrete terms and encourage experimentation.