Numerical methods in finance and economics: a MATLAB-based introduction (2nd edition)

The book deals with numerical methods relevant for financial applications, such as portfolio optimization and derivative pricing. After introductory chapters on financial markets and instruments, also covering models based on stochastic differential equations and risk measurement issues, and on numerical methods, we move on to describe the most important tools, such as: 1) numerical integration by Monte Carlo sampling and low-discrepancy sequences (with due emphasis on variance reduction strategies); 2) finite difference methods for partial differential equations; 3) optimization methods. These methods are illustrated, along with MATLAB code, in later chapters describing several applications to option pricing and portfolio optimization. We deal in particular with binomial/trinomial lattices, exotic option pricing by Monte Carlo simulation, finite difference methods for option pricing, portfolio optimization by mixed-integer and stochastic linear programming, and numerical dynamic programming, which is also the foundation of recent methods to price high-dimensional, American-style options.