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Monte Carlo simulation and finance 1st Edition by Don L Mcleish ISBN 0471731773 9780471731771

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Author(s): Don L. McLeish
ISBN(s): 9780471731771, 0471731773
Edition: 1
File Details: PDF, 2.02 MB
Year: 2005
Language: english
SKU: EB-1437088 Category: Tag:

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Product details:

ISBN 10: 0471731773 
ISBN 13: 9780471731771
Author: Don L Mcleish

Monte Carlo methods have been used for decades in physics, engineering, statistics, and other fields. Monte Carlo Simulation and Finance explains the nuts and bolts of this essential technique used to value derivatives and other securities. Author and educator Don McLeish examines this fundamental process, and discusses important issues, including specialized problems in finance that Monte Carlo and Quasi-Monte Carlo methods can help solve and the different ways Monte Carlo methods can be improved upon. This state-of-the-art book on Monte Carlo simulation methods is ideal for finance professionals and students. Order your copy today.

Monte Carlo simulation and finance 1st Table of contents:

Chapter 1: Introduction to Monte Carlo Simulation

  • What is Monte Carlo Simulation?

  • Historical Overview and Development of Monte Carlo Methods

  • The Role of Randomness in Simulation

  • Applications of Monte Carlo Simulation in Various Fields

Chapter 2: Fundamental Concepts in Probability and Statistics

  • Probability Distributions and Random Variables

  • Expected Value, Variance, and Covariance

  • Common Probability Distributions in Finance

  • Central Limit Theorem and Law of Large Numbers

Chapter 3: Basic Monte Carlo Simulation Technique

  • Generating Random Numbers and Random Variables

  • The Monte Carlo Estimation Process

  • Convergence and Accuracy of Monte Carlo Simulation

  • Variance Reduction Techniques: Importance Sampling, Antithetic Variates

Chapter 4: Monte Carlo Simulation in Financial Modeling

  • Financial Models and Stochastic Processes

  • Simulating Stock Prices: Geometric Brownian Motion

  • Simulating Interest Rates and Bond Prices

  • Monte Carlo Simulation for Portfolio Optimization

Chapter 5: Monte Carlo Simulation for Derivative Pricing

  • Introduction to Derivatives: Options, Futures, and Swaps

  • Pricing European and American Options Using Monte Carlo

  • The Black-Scholes Model and its Limitations

  • Path-Dependent Options: Barrier and Asian Options

  • Pricing with Stochastic Volatility Models

Chapter 6: Risk Management and Monte Carlo Simulation

  • Value at Risk (VaR) and Monte Carlo Simulation

  • Simulating Portfolio Risk and Return

  • Stress Testing and Scenario Analysis

  • Quantifying and Managing Tail Risk

  • Conditional Value at Risk (CVaR) and Monte Carlo

Chapter 7: Advanced Topics in Monte Carlo Simulation for Finance

  • Multi-Factor Models and Multi-Asset Simulations

  • Stochastic Volatility Models: Heston Model, SABR Model

  • Simulation for Credit Risk and Default Modeling

  • Simulation of Copulas and Multivariate Distributions

  • Monte Carlo in High-Dimensional and Complex Models

Chapter 8: Optimizing Monte Carlo Simulations

  • Parallel and Distributed Computing Techniques

  • GPU and Cloud Computing for Monte Carlo Simulations

  • Optimizing Convergence Speed and Reducing Computational Costs

  • Batch and Control Variates for Efficiency

Chapter 9: Applications of Monte Carlo Simulation in Financial Decision-Making

  • Portfolio Management and Asset Allocation

  • Capital Budgeting and Project Evaluation

  • Financial Planning and Forecasting

  • Hedging and Trading Strategies

Chapter 10: Real-World Case Studies in Finance

  • Case Study 1: Option Pricing in Practice

  • Case Study 2: Portfolio Risk and Optimization

  • Case Study 3: Credit Risk and Default Modeling

  • Case Study 4: Stress Testing in Banking

Chapter 11: Monte Carlo Simulation in Algorithmic Trading

  • Introduction to Algorithmic Trading

  • Modeling Price Dynamics for High-Frequency Trading

  • Risk Management and Monte Carlo in Trading Algorithms

  • Backtesting and Performance Evaluation of Trading Strategies

Chapter 12: Challenges and Limitations of Monte Carlo Simulation in Finance

  • The Curse of Dimensionality

  • Model Risk and Assumptions in Financial Models

  • Computational Challenges and Approximation Methods

  • Sensitivity to Input Parameters and Model Calibration

Chapter 13: The Future of Monte Carlo Simulation in Finance

  • Machine Learning and Artificial Intelligence in Monte Carlo Methods

  • Quantum Computing and the Future of Simulation

  • Innovations in Stochastic Modeling and Simulation Techniques

  • The Role of Monte Carlo in Regulatory and Compliance Testing

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Tags: Don L Mcleish, Monte Carlo, finance