Liquidity risk is a key challenge faced by European asset managers, according to a survey of over 120 leading asset managers who gathered in London, Madrid and Milan during May to attend a series of conferences that discussed the future of risk measurement, VaR and Ucits in the asset management industry.

Stricter regulations in Europe will continue to increase the need for improved risk management systems, asset pricing, and credit and liquidity risks. But the main challenge highlighted at the events lies in establishing reliable liquidity risk measurements. Market liquidity risk (or the risk of losing a certain amount of money when one liquidates the positions held in a portfolio or fund) is very difficult to gauge.

A survey conducted at the three conferences revealed that two thirds of attendees believe that liquidity risk is the industry’s weakest area at the moment when looking at the Ucits IV VaR approach. Almost a quarter of the attendees don’t measure liquidity risk at all at the moment, while over half use the number of days to liquidate a position as the liquidity measure and one in five use a quantitative approach to model liquidity risk.

Almost half of the attendees think that the biggest weakness in using the Ucits III VaR approach is computing a reliable VaR, while a third thinks it is stress tests. Only a small proportion (4%) of the panel sees no weakness at all in the Ucits III VaR approach.

“It is paramount for asset managers to build appropriate, reliable and scalable risk management processes, and also to understand them. In this way, they can focus on their job – managing and growing portfolios, whilst meeting the increasing demands of financial regulatory bodies,” said Dario Cintioli, Global Head of Risk of StatPro, the portfolio analysis vendor that organised the conferences with the support of KPMG.

“We have created a methodology to measure market liquidity risk when trading volume and market price information is not available. The method replicates the mechanism by which a market maker builds the price of a financial product for hedging purposes. The model is based on alternative scenarios – normal, stressed and highly stressed – providing answers to different market conditions,” concludes Cintioli.