An accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economics
Read alsoThe Hidden Agenda
Each of us pitches ideas every day. Regardless of what idea we’re selling—or who we’re selling it to—it all boils down to the act of stirring someone to join you, to agree to follow you. Yet we consistently underestimate how critical it is to recognize the role of the decision maker. Decisions are, after all, made by people; and people have needs…
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.