Handbook in Monte Carlo simulation applications in financial engineering, risk management, and economics
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 tim...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
Wiley
2014.
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| Edición: | 1st edition |
| Colección: | Wiley handbooks in financial engineering and econometrics.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629266806719 |
Tabla de Contenidos:
- Cover; Title Page; Copyright Page; Contents; Preface; Part I Overview and Motivation; 1 Introduction to Monte Carlo Methods; 1.1 Historical origin of Monte Carlo simulation; 1.2 Monte Carlo simulation vs. Monte Carlo sampling; 1.3 System dynamics and the mechanics of Monte Carlo simulation; 1.3.1 Discrete-time models; 1.3.2 Continuous-time models; 1.3.3 Discrete-event models; 1.4 Simulation and optimization; 1.4.1 Nonconvex optimization; 1.4.2 Stochastic optimization; 1.4.3 Stochastic dynamic programming; 1.5 Pitfalls in Monte Carlo simulation; 1.5.1 Technical issues
- 1.5.2 Philosophical issues1.6 Software tools for Monte Carlo simulation; 1.7 Prerequisites; 1.7.1 Mathematical background; 1.7.2 Financial background; 1.7.3 Technical background; For further reading; References; 2 Numerical Integration Methods; 2.1 Classical quadrature formulas; 2.1.1 The rectangle rule; 2.1.2 Interpolatory quadrature formulas; 2.1.3 An alternative derivation; 2.2 Gaussian quadrature; 2.2.1 Theory of Gaussian quadrature: The role of orthogonal polynomials; 2.2.2 Gaussian quadrature in R; 2.3 Extension to higher dimensions: Product rules
- 2.4 Alternative approaches for high-dimensional integration2.4.1 Monte Carlo integration; 2.4.2 Low-discrepancy sequences; 2.4.3 Lattice methods; 2.5 Relationship with moment matching; 2.5.1 Binomial lattices; 2.5.2 Scenario generation in stochastic programming; 2.6 Numerical integration in R; For further reading; References; Part II Input Analysis: Modeling and Estimation; 3 Stochastic Modeling in Finance and Economics; 3.1 Introductory examples; 3.1.1 Single-period portfolio optimization and modeling returns; 3.1.2 Consumption-saving with uncertain labor income
- 3.1.3 Continuous-time models for asset prices and interest rates3.2 Some common probability distributions; 3.2.1 Bernoulli, binomial, and geometric variables; 3.2.2 Exponential and Poisson distributions; 3.2.3 Normal and related distributions; 3.2.4 Beta distribution; 3.2.5 Gamma distribution; 3.2.6 Empirical distributions; 3.3 Multivariate distributions: Covariance and correlation; 3.3.1 Multivariate distributions; 3.3.2 Covariance and Pearson''s correlation; 3.3.3 R functions for covariance and correlation; 3.3.4 Some typical multivariate distributions; 3.4 Modeling dependence with copulas
- 3.4.1 Kendall''s tau and Spearman''s rho3.4.2 Tail dependence; 3.5 Linear regression models: A probabilistic view; 3.6 Time series models; 3.6.1 Moving-average processes; 3.6.2 Autoregressive processes; 3.6.3 ARMA and ARIMA processes; 3.6.4 Vector autoregressive models; 3.6.5 Modeling stochastic volatility; 3.7 Stochastic differential equations; 3.7.1 From discrete to continuous time; 3.7.2 Standard Wiener process; 3.7.3 Stochastic integration and Itô''s lemma; 3.7.4 Geometric Brownian motion; 3.7.5 Generalizations; 3.8 Dimensionality reduction; 3.8.1 Principal component analysis (PCA)
- 3.8.2 Factor models
