Monte Carlo Simulation with Applications to Finance

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A01=Hui Wang
advanced Monte Carlo techniques for finance
Antithetic Sampling
Arbitrage Free Pricing
Arbitrary Free Option Pricing
Author_Hui Wang
Binomial Tree
Binomial Tree Model
Brownian Motion
Call Option
Category=KCH
Category=KF
Category=PBT
Category=PBW
Classical Black Scholes Model
Conditional Expectation
Control Variate Estimate
Control Variate Method
Control Variates
Cross-Entropy
Cumulative Distribution Function
diffusion processes
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
financial engineering
Geometric Brownian Motion
Iid Copies
Lookback Call Option
MATLAB exercises
Monte Carlo Methods In Financial Engineering
Monte Carlo Simulation
Practitioners In The Financial Industry
quantitative finance methods
Risk Free Interest Rate
Risk Neutral Probability Measure
Score Function Method
Simulation Of Diffusion Processes
Spread Call Option
Standard Brownian Motion
Standard Normal Random Variable
Standard Normal Random Vector
Stochastic Calculus
Stochastic Differential Equation
Stochastic Integral
stochastic simulation
Strike Price
Students In Financial Engineering
Underlying Stock Price
variance reduction
Variance Reduction Techniques

Product details

  • ISBN 9780367381356
  • Weight: 408g
  • Dimensions: 156 x 234mm
  • Publication Date: 05 Sep 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Developed from the author’s course on Monte Carlo simulation at Brown University, Monte Carlo Simulation with Applications to Finance provides a self-contained introduction to Monte Carlo methods in financial engineering. It is suitable for advanced undergraduate and graduate students taking a one-semester course or for practitioners in the financial industry.

The author first presents the necessary mathematical tools for simulation, arbitrary free option pricing, and the basic implementation of Monte Carlo schemes. He then describes variance reduction techniques, including control variates, stratification, conditioning, importance sampling, and cross-entropy. The text concludes with stochastic calculus and the simulation of diffusion processes.

Only requiring some familiarity with probability and statistics, the book keeps much of the mathematics at an informal level and avoids technical measure-theoretic jargon to provide a practical understanding of the basics. It includes a large number of examples as well as MATLAB® coding exercises that are designed in a progressive manner so that no prior experience with MATLAB is needed.

Hui Wang is an associate professor in the Division of Applied Mathematics at Brown University. He earned a Ph.D. in statistics from Columbia University. His research and teaching cover Monte Carlo simulation, mathematical finance, probability and statistics, and stochastic optimization.

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