Monte Carlo Methods in Financial Engineering
Glasserman, Paul.
Monte Carlo Methods in Financial Engineering [electronic resource] / by Paul Glasserman. - 1st ed. 2003. - New York, NY : Springer New York : Imprint: Springer, 2003. - XIII, 596 p. 4 illus. online resource.
1 Foundations -- 2 Generating Random Numbers and Random Variables -- 3 Generating Sample Paths -- 4 Variance Reduction Techniques -- 5 Quasi-Monte Carlo -- 6 Discretization Methods -- 7 Estimating Sensitivities -- 8 Pricing American Options -- 9 Applications in Risk Management -- A Appendix: Convergence and Confidence Intervals -- A.1 Convergence Concepts -- A.2 Central Limit Theorem and Confidence Intervals -- B Appendix: Results from Stochastic Calculus -- B.1 ItĂ´'s Formula -- B.2 Stochastic Differential Equations -- B.3 Martingales -- B.4 Change of Measure -- C Appendix: The Term Structure of Interest Rates -- C.1 Term Structure Terminology -- C.2 Interest Rate Derivatives -- References.
Monte Carlo simulation has become an essential tool in the pricing of derivative securities and in risk management. These applications have, in turn, stimulated research into new Monte Carlo methods and renewed interest in some older techniques. This book develops the use of Monte Carlo methods in finance and it also uses simulation as a vehicle for presenting models and ideas from financial engineering. It divides roughly into three parts. The first part develops the fundamentals of Monte Carlo methods, the foundations of derivatives pricing, and the implementation of several of the most important models used in financial engineering. The next part describes techniques for improving simulation accuracy and efficiency. The final third of the book addresses special topics: estimating price sensitivities, valuing American options, and measuring market risk and credit risk in financial portfolios. The most important prerequisite is familiarity with the mathematical tools used to specify and analyze continuous-time models in finance, in particular the key ideas of stochastic calculus. Prior exposure to the basic principles of option pricing is useful but not essential. The book is aimed at graduate students in financial engineering, researchers in Monte Carlo simulation, and practitioners implementing models in industry.
9780387216171
Mathematics.
Finance, Public.
Probabilities.
Social sciences-Mathematics.
Statistics .
Econometrics.
Applications of Mathematics.
Public Economics.
Probability Theory.
Mathematics in Business, Economics and Finance.
Statistical Theory and Methods.
Quantitative Economics.
519
Monte Carlo Methods in Financial Engineering [electronic resource] / by Paul Glasserman. - 1st ed. 2003. - New York, NY : Springer New York : Imprint: Springer, 2003. - XIII, 596 p. 4 illus. online resource.
1 Foundations -- 2 Generating Random Numbers and Random Variables -- 3 Generating Sample Paths -- 4 Variance Reduction Techniques -- 5 Quasi-Monte Carlo -- 6 Discretization Methods -- 7 Estimating Sensitivities -- 8 Pricing American Options -- 9 Applications in Risk Management -- A Appendix: Convergence and Confidence Intervals -- A.1 Convergence Concepts -- A.2 Central Limit Theorem and Confidence Intervals -- B Appendix: Results from Stochastic Calculus -- B.1 ItĂ´'s Formula -- B.2 Stochastic Differential Equations -- B.3 Martingales -- B.4 Change of Measure -- C Appendix: The Term Structure of Interest Rates -- C.1 Term Structure Terminology -- C.2 Interest Rate Derivatives -- References.
Monte Carlo simulation has become an essential tool in the pricing of derivative securities and in risk management. These applications have, in turn, stimulated research into new Monte Carlo methods and renewed interest in some older techniques. This book develops the use of Monte Carlo methods in finance and it also uses simulation as a vehicle for presenting models and ideas from financial engineering. It divides roughly into three parts. The first part develops the fundamentals of Monte Carlo methods, the foundations of derivatives pricing, and the implementation of several of the most important models used in financial engineering. The next part describes techniques for improving simulation accuracy and efficiency. The final third of the book addresses special topics: estimating price sensitivities, valuing American options, and measuring market risk and credit risk in financial portfolios. The most important prerequisite is familiarity with the mathematical tools used to specify and analyze continuous-time models in finance, in particular the key ideas of stochastic calculus. Prior exposure to the basic principles of option pricing is useful but not essential. The book is aimed at graduate students in financial engineering, researchers in Monte Carlo simulation, and practitioners implementing models in industry.
9780387216171
Mathematics.
Finance, Public.
Probabilities.
Social sciences-Mathematics.
Statistics .
Econometrics.
Applications of Mathematics.
Public Economics.
Probability Theory.
Mathematics in Business, Economics and Finance.
Statistical Theory and Methods.
Quantitative Economics.
519