The financial markets have undergone a period of distress that has strained the trusted relationship between investors and financial advisors; new regulation has been forged to push for higher levels of transparency and risk-based communication as part of investment decision-making. This has ignited the quest for better portfolio optimisation techniques that can combine the added-value asymmetry of real products (as they strongly contributed to pre-crisis budgets) with the life-cycle requirements of investors, supported by intuitive graphical representation of seemingly complex mathematical relationships between real portfolios and products as required by regulation.
Upon reading Modern Portfolio Management, readers will understand the importance of simulating real securities (especially fixed income and structured products) during the making of optimal portfolios, as well as the importance of simulating financial investments over time to match in a transparent way actual goals and constraints instead of relying solely upon past performance or personal judgement.
Traditional portfolio management approaches have proven to be ineffective. Probabilistic scenario optimisation is emerging as an appealing alternative framework to facilitate the realignment of investors’ risk/return preferences with the risk/return characteristics of actual investments.
- A Modern Risk Management Perspective and The Probability Measure
- Dealing with Real Securities and Reinvestment Strategies: Fixed Income, Structured Products and Inflation
- Elicitation and Modeling of Risk/Return Time Profiles
- Review of Markowitz and Black-Litterman approaches
- Probabilistic Scenario Optimisation and Goal-based Investing
- Optimisation Case Studies
This book is a must-read for portfolio managers as well as financial advisors – in particular, all investment managers engaging in (or thinking of engaging in) long-term and goal-based asset allocations.
1. Beyond Modern Portfolio Theory
Part 1 - Risk Management Framework
2. A Modern Risk Management Perspective
3. The Probability Measure
4. Real Securities and Re-Investment Strategies: Fixed Income, Structured Products and Inflation-linked
5. Elicitation and Modeling of Risk/Return Time Profiles
Part 2 - Portfolio Optimisation Methods
6. à la Markowitz’s: a Tale of Simple Worlds
7. Black-Litterman’s Approach: a Tale of Subjective Views
8. Probabilistic Scenario Optimisation (PSO)
Part 3 - Portfolio Optimisation Case Studies
9. Case studies: Mean-Variance and Black-Litterman
10. Case studies: Probabilistic Scenario Optimisation
Paolo Sironi is practice leader of wealth management solutions and risk content services at IBM Risk Analytics, where he is responsible for quantitative methods and asset allocation advisory for financial institutions (retail banking, private banking, ultra-high-net-worth and institutional advisory clients). Combining risk analytics and technology, Paolo’s expertise spans wealth management, asset management, investment banking, market and credit risk management, regulatory reporting, cognitive computing, on-cloud and banking digitalisation. Before joining IBM, Paolo worked as managing director of Capitects, the company (a provider of risk management solutions) which he founded in 2008 as a joint venture between Sal. Oppenheim Private Bank and Algorithmics and which became a part of IBM following the Algorithmics acquisition. Prior to Capitects, Paolo worked as head of market and counterparty risk modelling at Banca Commerciale Italiana and Banca Intesa Sanpaolo.