Real-time analytics offers particularly strong benefits for hedge fund risk management, allowing them to operate with more intelligence and speed across all risk models. David Cassonnet,Global Head of Business Development at ActiveViam, explains the four essential ingredients of successful real-time risk management.
Hard experience in a highly competitive market has shown hedge funds the importance of being able to make decisions based on real-time analytics. While hedge funds are not subject to regulatory rules, multi-dimensional stress tests, real-time PnL, close liquidity risk management and Expected Shortfall have all proved their value, as has value-at-risk (VaR).
Taking VaR and PnL as an example, if a hedge fund wants to monitor its VaR or PnL continuously for every position and every portfolio, updating continuously as new data streams in, real-time analytics software makes that a feasible task. For example, as option pricing changes intra-day the risk analyst can drill down to that one piece of data to confirm where the change came from, then aggregate the sensitivities and interpret the change straight away.
There are also considerably wider benefits in terms of the higher returns that come from managing limits more closely with confidence, validating decisions faster to beat the competition in a sector where milliseconds matter, and spotting new opportunities and threats,
Here are my four tips for getting a successful real-time risk function up and running:
First, think carefully about your data inputs. Like any risk management model, VaR models are only as good as the inputs. Then it is important to be able to drill down to the PnL vectors, investigate stress scenarios/shocks to the portfolio and evaluate the impact on the desk, book and the wider hedge fund PnL.
Second, make sure your real-time analytics solution can handle massive datasets. Again using VaR as an example, if you need to slice and dice rolling VaR and run “What-If” simulations as part of a pre-deal check, the best way to keep the calculations accurate is to standardize on software built to handle incredibly large datasets. Those datasets will only continue to grow, driven by hedge funds’ work on longer case histories and the consideration of multiple PnL scenarios. Software that can make sense of terabytes of data is now a must!
Third, ensure that all types of “what-ifs” are accounted for in real-time. One lesson from the past two years is that you have to be able to replace and scale the sensitivities’ data so you have maximum control over any scenario. If you can scale up or down immediately on an open position, you are in a much better position to make the right decision – fast.
Finally, think about the bigger risk picture. You can apply the benefits of real-time risk analytics to other risk-based regulations of relevance to hedge funds as they emerge.
This approach underpins a wider risk strategy that includes credit risk, liquidity risk, market risk and counterparty risk. In fact, real-time risk analytics based on these four core principles will support profitability and compliance across all risk models and put a hedge fund in a better position to handle whatever future market shocks may be in store.
