Portfolio Optimization

Theory and Application

Inbunden, Engelska, 2025

1 319 kr

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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.

Produktinformation

Utgivningsdatum2025-06-12
Mått262 x 186 x 42 mm
Vikt1 401 g
FormatInbunden
SpråkEngelska
Antal sidor608
FörlagCambridge University Press
ISBN9781009428088

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Daniel P. Palomar is a Professor at the Hong Kong University of Science and Technology. He is recognized as EURASIP Fellow, IEEE Fellow, and Fulbright Scholar, and recipient of numerous research awards. His current research focus is on convex optimization applications in signal processing, machine learning, and finance. He is the author of many research articles and books, including 'Convex Optimization in Signal Processing and Communications'.

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.

'Daniel Palomar's book is a hands-on guide to portfolio optimization at the research frontier. By integrating financial data modeling, code, equations, and real-world data, it bridges theory and practice. A must-read for aspiring data-driven portfolio managers and researchers seeking to stay updated with the latest advancements.' Kris Boudt, Ghent University, Vrije Universiteit Brussel and Vrije Universiteit Amsterdam