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In our latest whitepaper, ‘Hands-on reverse stress testing’, we provide a practical guide to do just this by leveraging our enterprise management platform Axioma Risk, used to extract portfolio historical simulation data and factor returns.

How badly could a portfolio get hurt? What scenario would be responsible for such a loss? What are the main risk drivers in this scenario and how plausible would such a scenario be? These questions are all part of what is commonly called reverse stress testing. This paper aims to illustrate how one could follow a very pragmatic end-to-end workflow to answer these questions.

In risk management a lot of focus and attention is (rightly) put on models and methodologies used to compute ex-ante risk measures. And in the context of a multi-asset class universe which is vast by nature, perfect data (market data, terms and conditions provided by the user) and bug-free algorithms are not always possible. Therefore, one of the key challenges for risk managers is to ensure that any risk analytic produced is sound and reliable.

Understanding changes in risk estimates can be key, especially in times of crisis when volatilities spike and correlations point in the same direction, eliminating the diversification that was supposed to protect a portfolio.

We can all agree that understanding risk – specifically, the reason why a risk number has changed – is key for sound portfolio management.