In the current environment of heightened uncertainty, managing a multi-asset portfolio has rarely looked as complex as it does today, especially for those investors looking for an appropriate governance model on which to take investment decisions.

The issue is not only to make accurate market forecasts and formulate appropriate investment views, but also to construct an efficient portfolio based on these views within a given risk budget.

Mean-variance optimization has long been recognized as a standard practice, but, in our view, it can lead to highly concentrated and unstable portfolios. A purely risk-based optimisation also implies regular and strict rebalancing which is hardly suitable for those portfolios where asset allocation is combined with other sources of potential alpha generation.

As a result, risk budgeting techniques have been developed to allow more stable allocations and more robust diversification than mean-variance optimised portfolios. These techniques allow the identification of criteria that investors, we believe, can rely on to choose how to allocate their risk budget and size their trades. Among others, they include the number of investment ideas to be integrated in the portfolio and their diversification potential.

‘Effective diversification’ is at the core of our approach to portfolio construction. We believe that diversification is effective not only with regard to allocating across asset classes, but also for investing in uncorrelated investment opportunities across themes, asset classes, sectors or regions. With this aim, an active multi-asset investment framework should allocate risk across four pillars: macro strategy, macro hedging, satellite strategies and selection strategies, with the goal of achieving the portfolio’s target return or objective.

We consider that the issue of how to size a given trade should be raised not only at the actual time of the investment, but also – and probably more importantly - at an earlier stage. We believe that trade sizing should occur regularly, at least on an annual basis, when the portfolio manager plans the budget of risk of their portfolio for the period ahead. Our analysis will differentiate between a ‘Planning-ahead’ step and an ‘At investing’ one. In practice, sizing a trade will imply considering the typical standalone risk of each trade, its correlation with other strategies, together with the investor’s level of conviction in the trade. Put together, these factors will set the actual trade size.

Finally, w e will argue about the need to implement an advanced risk budgeting technique to enable the whole process. There is no ‘one-size-fits-all’ strategy for every investment environment and any portfolio allocation has to be a dynamic exercise. Even though history is not predictive of the future, detailed information about past trades and their contribution to portfolio efficiency could be of help for portfolio managers to establish the features of the alpha expected in various strategies, serving as a compass across different market environments. This implies relying on a quantitative framework, including a rich history of past trades to help measure portfolio managers’ skills in different areas of expertise, alongside a robust risk and performance system. Asset managers with a long track record of investment decisions can learn from the analysis of past success and can be more knowledgeable when setting future alpha targets and risk budgets.


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Germano Matteo
Deputy Group Chief Investment Officer
Head of Multi Asset Portfolio Construction and Financial Engineering
Portfolio Construction and Financial Engineering - Senior Analyst
RC - Author - TAZE-BERNARD Eric
Senior Advisor, Amundi Investment Institute
RC - Author - BERTINO Claudia
Head of Investment Insights, Publishing and Client Development, Amundi Investment Institute
Multi-Asset Investment Specialist
RC - Author - PORTELLI Lorenzo
Head of Cross Asset Strategy, Head of Research at Amundi Italy, Amundi Investment Institute
Head of Multi Asset Business Development and Investment Specialists