Super Quant internship

Improving multi asset portfolios by smarter risk management

Multi-Asset funds invest in multiple asset classes (e.g. stocks, bonds, currencies, commodities) as opposed to being limited to one asset class. This brings additional challenges for the risk management of such funds, viz. controlling the overall risk level over time and balancing the risks inside the portfolio. Robeco has several multi-asset funds and continues to grow in this area. Through this internship we want to improve and extend our understanding of risk management and portfolio construction.

Balancing risks & rewards

Controlling the level of overall portfolio risk over time will yield a smoother return profile, which is beneficial to investors. However, the relation between the risk level and the portfolio’s risk-adjusted performance should be clearly understood in order to avoid unintended effects. E.g., scaling down return drivers that perform well in high risk regimes will lower performance. Similarly, a portfolio that is well-diversified over various returns drivers will have less concentrated risks. A clear example: A 50%-50% equity-bond portfolio does not equate to 50%-50% risk contribution; Equities account for more than 95% of the total portfolio volatility. Various methods to risk budgeting exists: from the quite popular risk parity (i.e. equating risk contributions) to the more complex approaches that also account for risk-return trade-offs.

“Risk” is one word but not one number

This perspective offers quite a number of interesting questions that have to be tackled during the internship:
(1) Which risk concepts to choose?
(2) And what about the accurate estimation of the risk metric?

As an example: volatility (and the ubiquitous normality assumption) is widely used risk metric, but as a reflection of investment risk, downside risk and drawdowns1 are much more important to an investor. Since return distributions might be quite asymmetric, as reflected in their skewness and/or lower partial moments, downside risk measures that use this distributional asymmetry may give a different view on portfolio risk. However, do these measures add sufficiently to outweigh their increased complexity?

Not all risk answers lie in historical data

There are also hidden risks. 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. Historical data might not always be a suitable guide to the future.

Research questions of the internship are :

  • What is the risk-performance relation of the quantitative strategies employed by our multi asset funds ? How do these strategies perform in different risk regimes?

  • How can we efficiently estimate tail risk measures and tail correlations and do they offer an advantage the full-domain volatilities and correlations ?

  • Do implied volatility indices such as the VIX and VSTOXX offer advantages over using dynamic volatility models (such as GARCH, EWMA) ?

  • Can stop-loss rules help to reduce the downside risk – and notably drawdowns?

However, the list of questions is just a place to start…

The list of selected literature for an introduction to this topic:

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

Lopez de Prado, M., 2013, “How Long Does It Take to Recover from a Drawdown?” Available at SSRN:

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

Nawrocki, D., 1999, “ A Brief History of Downside Risk Measures”, The Journal of Investing 8, pp.9-25

Perold, A.F. & W.F. Sharpe, 1988, “Dynamic Strategies for Asset Allocation”, Financial Analysts Journal Jan/Feb 44/1, pp.16-27

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

1 Drawdowns are composed of consecutive (or chained) returns and this cumulative return focus makes them more difficult to analyze and manage.