Improving multi-asset portfolios by smarter portfolio construction

Multi-Asset funds invest in multiple asset classes (e.g. stocks and bonds) as opposed to being limited to one asset class. This brings additional challenges for the risk management of such funds. Some examples:

Risk parity

Risk parity is a popular topic and is about dividing the risk budget equally across asset classes, between assets within asset classes, and/or across active investment decisions. This is different from equal money weights. For example, in a 50%-50% equity-bond portfolio the equities account for more than 95% of the portfolio volatility. You need a 25%-75% equity-bond portfolio to get an equal volatility contribution from equities and bonds. Risk parity, however, is also a difficult topic. E.g. in 2015 until July bond volatility had been on the rise whereas equity volatility was near all-time lows. Well-known risk-parity funds such as the one from Bridgewater as a result had increased their equity holdings at the expense of the bond holdings. Yet in August 2015 equities took a big hit and so did risk-parity funds.

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Hidden risks

A multi asset portfolio can also have hidden risks. E.g. on January 15, 2015, the Swiss central bank gave up the 3-year old currency peg of the Swiss Franc with the Euro. The result: A 20% appreciation of the Swiss Franc. At the same time the Swiss equity market took a large hit to the extent that for a foreign investor in Swiss equities it appeared nothing happened. Yet a multi asset portfolio shorting the Swiss Franc and overweighting Swiss equities took a double hit. Hence cross-asset correlations are an important source of risk.

Downside risk control

As a reflection of investment risk, downside risk and drawdowns are much more important than volatility. Distributional downside risk measures are linked to distributional asymmetry, as reflected in skewness and lower partial moments, and specific tail risk measures. Drawdowns, in contrast, are composed of consecutive (or chained) returns and this cumulative return focus makes them difficult to analyze and manage.

Multi-Asset funds

Multi-Asset funds are growing fast. Robeco also puts in a large effort to grow in this area. Robeco already has several multi-asset funds. Through this internship we want to improve the risk management and portfolio construction of these funds beyond what we already have in place.

Research questions

First let us stress that we expect an active contribution from you on what are the important questions and which are not. Hence you can help with extending the list of examples below or even replace the examples by topics that are deemed more relevant. Second, we use performance drivers including quantitative models in the multi-asset funds. Hence the impact of risk management on these performance drivers and with it fund returns is also of crucial importance.


  • What risk measures are appropriate to define risk budgets? Currently we use volatility and volatility contributions, but we’d like to extend the scope to downside risk and tail risk.
  • How should we weight different asset classes relative to each other? Do we strive for equal risk or equal risk contributions, or can we incorporate information about the risk-return trade-off? How often should we rethink these weights?
  • How should we weight assets within the same asset class relative to each other?
  • Identify hidden risks. Should we impose cross-asset weight restrictions to avoid concentrated risks?
  • Can stop-loss rules help to reduce the downside risk – and notably drawdowns?
  • For all decisions above we need to monitor the impact on the performance of the quantitative strategies employed by our multi asset funds. E.g. how do these strategies perform in different risk regimes?
  • Risk balance performance drivers: We employ multiple performance drivers and we have a desired risk contribution of each of these performance drivers. How can we best achieve this goal?

Selected literature

Accar, E. & R. Toffel, 2000, “Stop-loss and Investment Returns”, working paper

Hallerbach, W.G., 2015, “Advances in Portfolio Risk Control.” Chapter 1 in E. Jurczenko (editor), “Risk-Based and Factor Investing” Elsevier/ISTE Press Ltd., London.

Kaminski, K. & A. Lo, 2014, “When Do Stop-Loss Rules Stop Losses”, Journal of Financial markets 18, pp.234-254

Lee, C.K, 2016, “Expected Drawdown Management: An Ex-Ante, Long-Term Approach to Portfolio Construction”, The Journal of Wealth Management 18/4 (Spring), pp.65-74

Magdon-Ismail, M. & A. Atiya, 2004, “Maximum Drawdown”, RISK Magazine 17/10 (October), pp.99-102

Shelton, A., 2016, “The Value of Stop-loss, Stop-gain Strategies in Dynamic Asset Allocation, Journal of Asset Management, Aug, pp.1-20