Portfolio Optimization
Theory and Application

A comprehensive guide to a wide range of portfolio designs, bridging the gap between mathematical formulations and practical algorithms.

Daniel P. Palomar (Author)

9781009428088, Cambridge University Press

Hardback, published 12 June 2025

608 pages
26 x 18.5 x 3.7 cm, 1.3 kg

'I highly recommend Palomar's book Portfolio Optimization to anyone interested in constructing good portfolios using computational optimization. It collects methods from an enormous literature into one volume, using common notation accessible to any STEM researcher, with clear explanations, discussion, and comparisons of different methods. It is required reading for all of my finance-curious students.' Stephen Boyd, Stanford University

This comprehensive guide to the world of financial data modeling and portfolio design is a must-read for anyone looking to understand and apply portfolio optimization in a practical context. It bridges the gap between mathematical formulations and the design of practical numerical algorithms. It explores a range of methods, from basic time series models to cutting-edge financial graph estimation approaches. The portfolio formulations span from Markowitz's original 1952 mean–variance portfolio to more advanced formulations, including downside risk portfolios, drawdown portfolios, risk parity portfolios, robust portfolios, bootstrapped portfolios, index tracking, pairs trading, and deep-learning portfolios. Enriched with a remarkable collection of numerical experiments and more than 200 figures, this is a valuable resource for researchers and finance industry practitioners. With slides, R and Python code examples, and exercise solutions available online, it serves as a textbook for portfolio optimization and financial data modeling courses, at advanced undergraduate and graduate level.

Preface
1. Introduction
I. Financial Data: 2. Financial data: stylized facts
3. Financial data: IID modeling
4. Financial data: time series modeling
5. Financial data: graphs
II. Portfolio Optimization: 6. Portfolio basics
7. Modern portfolio theory
8. Portfolio backtesting
9. High-order portfolios
10. Portfolios with alternative risk measures
11. Risk parity portfolios
12. Graph-based portfolios
13. Index tracking portfolios
14. Robust portfolios
15. Pairs trading portfolios
16. Deep learning portfolios
Appendices: Appendix A. Convex optimization theory
Appendix B. Optimization algorithms.

Subject Areas: Applied mathematics [PBW]