Portfolio Optimization

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.

• Provides a comprehensive coverage of portfolio optimization formulations, allowing readers to understand different portfolio paradigms and the application of a variety of techniques based on their specific needs and contexts
• Puts an emphasis on practical algorithms and real-world applications, and includes numerous numerical experiments based on market data
• Departs from the conventional Gaussian assumption and adopts more realistic heavy-tailed distributions, exploring a range of methods, from basic time series models to cutting-edge financial graph estimation approaches