Liquidity risk management, highlighted by the 2008 financial crisis, has become a critical factor in ensuring the resilience of financial markets. To meet the operational challenges involved, technological solutions like the ALTO platform developed by Amundi Technology have become indispensable risk management tools. Generative artificial intelligence is now unlocking new opportunities to enhance the anticipation of volatility spikes, enhancing overall risk management strategies. Insights from Anne Gruz, Strategic Product Manager Risk and Sustainability, and Nabil Cherrat, Deputy Head of ALTO Product Group at Amundi Technology.
Liquidity Risk Management: A Priority During Periods of Market Volatility
The financial crises of 2008 and the 2020 pandemic have highlighted the critical importance of managing liquidity risk during periods of market volatility. The credit collapse between 2008 and 2009 led to frozen credit markets and to a severe liquidity crunch, causing some financial institutions to disappear and others to become insolvent[1]. Similarly, the expansion of COVID-19 in 2020 triggered a widespread liquidity shock in bond markets, along with massive redemption requests for certain funds[2].
As a cornerstone of financial market stability, liquidity management is a strategic priority for investors protection. “The liquidity of a portfolio is defined as the ability of the investors to convert their assets into cash without significantly affecting their market price. Low liquidity suggests that the selling may take more time or require accepting a lower price”, explains Nabil Cherrat. “The crisis revealed that many investment funds lacked adequate liquidity management practices, leading to severe consequences for investors and the broader market”, adds Anne Gruz.
The periods of volatility in 2008 and 2020 have indeed highlighted the importance of liquidity risk as well as the operational challenges associated with it, such as difficulties in valuing assets and changes in investor behavior, which have “implications for investment decision-making and overall portfolio performance”, explains Anne Gruz. “Market volatility leads to wider Bid-Ask spreads and, consequently, higher transaction costs”, observes Nabil Cherrat. “Hedging strategies, particularly those involving OTC products, face increased pressure from margin calls during periods of heightened volatility. This creates specific liquidity needs that must be anticipated and incorporated into management models”, notes Anne Gruz.
The importance of liquidity risk management is reflected in the regulatory framework established by the European Directive 2009/65/EC (UCITS), which applies to Undertakings for Collective Investment in Transferable Securities (UCITS). It underwent significant revisions following the 2008 crisis, with the European Directive 2011/61/EU on Alternative Investment Fund Managers (AIFM), which governs Alternative Investment Funds (AIFs). Additionally, the guidelines issued by the European Securities and Markets Authority (ESMA) focus on “liquidity stress testing in UCITS and AIFs”[3]. In the face of these increasing regulatory requirements, technology has become an indispensable analytical tool for portfolio managers.
The Key Role of Technology in Strengthening Financial Market Resilience Against Liquidity Crises
Technology solutions are essential for improving liquidity risk management, helping portfolio managers to collect, analyze, communicate, and use data more efficiently. “At Amundi Technology, we have developed an integrated platform, ALTO, which has the advantage of using a single data source for liquidity management and the overall management process. This integration allows managers to seamlessly access portfolio metrics, market risk indicators, and liquidity analysis, providing a consistent and comprehensive view”, explains Anne Gruz.
“Our platform is based on an innovative approach: we leverage data from our trading desk, Amundi Intermediation, to regularly calibrate all liquidity-related data”, adds Nabil Cherrat. The ALTO platform relies on internally generated data from millions of transactions processed annually, as well as public market data. The objective is to provide a comprehensive view of liquidity across multiple asset classes.
The effectiveness of the technological module is structured around three areas of analysis. “Our first focus is on liability modeling. We analyze portfolio inflows and outflows, modeling investor behavior during a crisis. The second focus is market modeling, where we evaluate Bid-Ask spreads on financial products and their evolution in a crisis context. The third focus involves cross-referencing asset and liability data to identify temporal mismatches, enabling us to anticipate and better manage these misalignments before they create tensions during a crisis”, explains Anne Gruz.
Amundi Technology provides portfolio managers with an integrated platform that centralizes comprehensive internal and external data. ALTO offers a range of features designed to manage liquidity risk, including crisis scenario simulations that enable users to anticipate and adapt effectively. This approach contributes to the resilience of financial markets. Complementing current technological solutions, artificial intelligence (AI) is paving the way for new advancements in anticipating volatility spikes and managing liquidity risk.
The Role of Artificial Intelligence in the Evolution of Liquidity Management
“The past few years have shown us the frequency of volatility spikes caused by crises, especially geopolitical ones”, notes Nabil Cherrat. This realization has led managers to turn to technology, including artificial intelligence tools, such as sentiment analysis or predictive modelling, to better anticipate liquidity crises. “To detect speculative bubbles and periods of crisis, we implement advanced indicators through predictive analytics. This approach can leverage AI tools to anticipate and issue alerts as early as possible in the process”, adds Nabil Cherrat.
In addition to anticipating volatility spikes and bubble busts, AI also helps portfolio managers execute exit transactions during the portfolio liquidation phase within shorter timeframes. It optimizes transaction sizes and minimizes transaction costs losses during rapid market management, preserving portfolio performance.
“The impact of AI on the microstructure of capital markets is a complex and evolving topic. It is therefore essential to use AI driven technologies to gain real-time insights into market developments and to analyze vast amounts of data to provide liquidity more efficiently. It can be used to adjust order execution strategies, including bid-ask spreads, in real-time based on market conditions, which can lead to improved market efficiency”, explains Anne Gruz.
However, AI also raises concerns about market stability, fairness, and the potential for systemic risks. It needs to be used carefully.