Monte Carlo Simulation with Applications to Mathematical Finance

A comprehensive guide to portfolio design, from Markowitz mean-variance to risk parity, robust and drawdown portfolios, index tracking and pairs trading. It connects every formulation with practical numerical algorithms.

Daniel P. Palomar

Learn Python for economic and financial modeling with NumPy, SciPy, pandas and Matplotlib. Written by Thomas Sargent and John Stachurski, with runnable code in every lecture.

Thomas J. Sargent, John Stachurski

A hands-on introduction to computational finance: Black-Scholes, Monte Carlo methods, the binomial model, discrete hedging and jump diffusion, explained with numerical experiments. Written for readers who want working methods, not only theory.

Peter A. Forsyth

A collection of C++ algorithms for finance: present value, bonds and term structure, mean-variance portfolios, binomial trees, Black-Scholes and Monte Carlo pricing. Each method comes with the formula and working code.

Bernt Arne Ødegaard

Explains the mathematics behind portfolio analysis, from matrices and orthogonal projection to Markowitz efficient portfolios and the CAPM. Proofs and worked examples make each model easy to follow.

Farida Kachapova