COMPUTATIONAL FINANCE matlab (r) oriented modeling.

"Computational Finance is increasingly important in the financial industry, as a necessary instrument for applying theoretical models to real-world challenges. Indeed, many models used in practice involve complex mathematical problems, for which an exact or a closed-form solution is not availab...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: CESARONE, FRANCESCO
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [Place of publication not identified] ROUTLEDGE, 2020.
Colección:Routledge-Giappichelli Studies in Business and Management Ser.
Temas:
Finance > Mathematical models.
Finance > Statistics.
Financial engineering.
Finances > Modèles mathématiques.
Ingénierie financière.
Finance
Finance > Mathematical models
Financial engineering
Statistics
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Half Title
  • Title Page
  • Copyright Page
  • Dedication
  • Table of Contents
  • Preface
  • Part I: Programming techniques for financial calculus
  • Chapter 1: An introduction to MATLAB®with applications
  • 1.1: MATLAB®basics
  • 1.1.1: Preliminary elements
  • 1.1.2: Vectors and matrices
  • 1.1.3: Basic linear algebra operations
  • 1.1.4: Element-by-element multiplication and division
  • 1.1.5: Colon (:) operator
  • 1.1.6: Predefined and user-defined functions
  • 1.2: M-file: Scripts and Functions
  • 1.3: Programming fundamentals
  • 1.3.1: if, else, and elseif construct
  • 1.3.2: for loops
  • 1.3.3: while loops
  • 1.4: MATLAB®graphics
  • 1.5: Preliminary exercises on programming
  • 1.6: Exercises on the basics of financial evaluation
  • 1.6.1: Interest Rate Swap
  • Part II: Portfolio selection
  • Chapter 2: Preliminary elements in Probability Theory and Statistics
  • 2.1: Basic concepts in probability
  • 2.2: Randomvariables
  • 2.3: Probability distributions
  • 2.4: Continuous randomvariables
  • 2.5: Higher-order moments and synthetic indices of a distribution
  • 2.6: Some probability distributions
  • 2.6.1: Uniformdistribution
  • 2.6.2: Normal distribution
  • 2.6.3: Log-normal distribution
  • 2.6.4: Chi-square distribution
  • 2.6.5: Student-t distribution
  • Chapter 3: Linear and Non-linear Programming
  • 3.1: General Framework
  • 3.2: Optimization with MATLAB®
  • 3.2.1: Linear Programming
  • 3.2.2: Quadratic Programming
  • 3.2.3: Non-Linear Programming
  • 3.3: Multi-objective optimization
  • 3.3.1: Efficient solutions and the efficient frontier
  • Chapter 4: Portfolio Optimization
  • 4.1: Portfolio of equities: prices and returns
  • 4.2: Risk-return analysis
  • 4.2.1: Elements of Expected Utility Theory
  • 4.2.2: General Framework
  • 4.2.3: Mean-Variance model
  • 4.2.4: Effects of diversification for an EW portfolio
  • 4.2.5: Mean-Mean Absolute Deviation model
  • 4.2.6: Mean-Maximum Loss model
  • 4.2.7: Value-at-Risk
  • 4.2.8: Mean-Conditional Value-at-Risk model
  • 4.2.9: Mean-Gini model
  • 4.3: Elements of bond portfolio immunization
  • Part III: Derivatives pricing
  • Chapter 5: Further elements on Probability Theory and Statistics
  • 5.1: Introduction toMonte Carlo simulation
  • 5.2: Stochastic processes
  • 5.2.1: Brownian motion
  • 5.2.2: Ito's Lemma
  • 5.2.3: Geometric Brownian motion
  • Chapter 6: Pricing of derivatives with an underlying security
  • 6.1: Binomial model
  • 6.1.1: A replicating portfolio of stocks and bonds
  • 6.1.2: Calibration of the binomialmodel
  • 6.1.3: Multi-period case
  • 6.2: Black-Scholes model
  • 6.2.1: Assumptions of the model
  • 6.2.2: Pricing of a European call
  • 6.2.3: Pricing equation for a call
  • 6.2.4: Implied volatility
  • 6.2.5: Black-Scholes formulas via integrals
  • 6.3: Option Pricing via theMonte Carlomethod
  • 6.3.1: Path Dependent Derivatives
  • References
  • Suggested lesson plan

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