About a-team Marketing Services
The knowledge platform for the financial technology industry
The knowledge platform for the financial technology industry

A-Team Insight Blogs

StatPro Discovers Solution to the Liquidity Risk Paradox

Subscribe to our newsletter

StatPro Group, a leading provider of portfolio analysis and asset valuation services for the global asset management industry, announces a major breakthrough in its risk measurement research.

StatPro has designed an innovative approach to measuring market liquidity risk that does not rely only on observed bid, ask and volumes. Instead, factors such as market capitalisation, the percentage of ownership of a stock and the size of an issue for a fixed income instrument are taken into account.

Liquidity risk refers to the risk of losing money when you suddenly liquidate one or more positions in your portfolio. The loss comes from selling the positions at a lower price than the one at which those positions are marked-to-market.

“While innovations in the area of market risk have been very active in recent years – for example, introducing the concept of Value at Risk – little exists on liquidity risk. The reason is that while measuring market risk you can create models that are calibrated with market data, you cannot do the same for liquidity risk,” said Dario Cintioli, Global Head of Risk of StatPro.

“In calibrating a liquidity risk model you need access to the bid, ask and volume information. Well, the problem is that this information is only available for liquid issues. Whatever model you invent, you will always lack the basic information to calibrate it for the instruments that present most of your liquidity risk. We call this the ‘liquidity risk paradox’.

StatPro’s software facilitates the selection of the appropriate liquidity risk scenario and the computation of the expected loss for liquidity risk. The view includes a breakdown of the liquidity risk loss across various components.

The user can select one scenario and build a ‘tree’ of criteria for breaking down the liquidity risk contribution at each hierarchy level, down to single asset composition. The risk manager can drill down through every component of liquidity risk, discovering how much is coming and from where, without any previous knowledge of the portfolio. This tool enables the risk manager to ‘X-ray’ the liquidity risk of the portfolio, spotting any challenging situations.

“We have solved the paradox of measuring market liquidity risk when trading volume and market price information is not available. As with all our risk analyses, liquidity risk can be run at single asset level, portfolio level, portfolio versus benchmark and as an aggregation of several portfolios. The latter option is critical to liquidity risk, as the percentage of ownership of one stock can be negligible when measured by portfolio, but can become relevant at ‘firm’ level,” concludes Cintioli.

Subscribe to our newsletter

Related content

WEBINAR

Upcoming Webinar: The ROI of Data Trust: Quantifying the Business Value of Data Observability

Date: 8 July 2026 Time: 10:00am ET / 3:00pm London / 4:00pm CET Duration: 50 minutes Data is the fuel that keeps modern financial institutions’ motors running but if that data can’t be trusted then the decisions made based upon it, or the uses to which its put, will be compromised. That’s especially important for...

BLOG

Northern Trust Highlights Asset Owners’ Data Challenge in Private Markets

Much is spoken of the data challenges that institutional asset managers are facing as they redraw their business models to meet the demands of a new economic environment, but less is said of asset owners, who are undergoing their own operational transformations. For them, the data journey is just as challenging; as their operational models...

EVENT

AI in Capital Markets Summit London

Now in its 3rd year, the AI in Capital Markets Summit returns with a focus on the practicalities of onboarding AI enterprise wide for business value creation. Whilst AI offers huge potential to revolutionise capital markets operations many are struggling to move beyond pilot phase to generate substantial value from AI.

GUIDE

AI in Capital Markets Handbook 2026

AI adoption in capital markets has moved into a more disciplined phase. The priority is now controlled deployment: where AI can be used safely, where it can deliver measurable value, and how outputs can be governed, monitored and evidenced. The 2026 edition of the AI in Capital Markets Handbook examines how AI is being applied...