News & Research

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. This understanding becomes even more important in times of volatility when correlations that once moved in one direction can swing the other way, reducing – or even removing altogether – the diversification that was intended to protect a portfolio. But this change in risk could be down to a number of reasons, making this task quite a challenging one.