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This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers asset-...
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This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers the as...
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers the as...
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... This research project has been built around three dimensions: liability liquidity risk, asset liquidity risk and asset-liability risk management. It resulted in three publications, each one considering a specific dimension: (1) modeling the liability liquidity risk (Roncalli et al., 2021a), (2) modeling the asset liquidity risk (Roncalli et al., 2021b) and (3) managing the asset-liability liquidity risk (Roncalli, 2021c). The discussions we had with the asset management industry show that these working papers may be viewed as too elaborate. ...
his article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers the asset-liability management of the liquidity gap risk (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019a, 2020a) and are useful for fund managers. The review of the academic literature and professional research studies shows that there is a lack of standardized and analytical models. The aim of this research project is then to fill the gap with the goal of developing mathematical and statistical approaches, and providing appropriate answers. The three dimensions have been developed in the published working papers: (1) modeling the liability liquidity risk (Roncalli et al., 2021a), (2) modeling the asset liquidity risk (Roncalli et al., 2021b) and (3) managing the asset-liability liquidity risk (Roncalli, 2021c). This fourth working paper provides three examples and the comprehensive details to compute the redemption coverage ratio, implement reverse stress testing and estimate the liquidation cost of the redemption portfolio. The portfolios have been chosen in order to cover the main asset classes: large-cap stocks, small-cap stocks, sovereign bonds and corporate bonds. Since we provide the data in the appendix, these basic examples are easily reproducible and may help quantitative analysts to understand the different steps to implement liquidity stress testing in asset management.
... This research project has been built around three dimensions: liability liquidity risk, asset liquidity risk and asset-liability risk management. It resulted in three publications, each one considering a specific dimension: (1) modeling the liability liquidity risk (Roncalli et al., 2021a), (2) modeling the asset liquidity risk (Roncalli et al., 2021b) and (3) managing the asset-liability liquidity risk (Roncalli, 2021c). The discussions we had with the asset management industry show that these working papers may be viewed as too elaborate. ...
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers the asset-liability management of the liquidity gap risk (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019a, 2020a) and are useful for fund managers. The review of the academic literature and professional research studies shows that there is a lack of standardized and analytical models. The aim of this research project is then to fill the gap with the goal of developing mathematical and statistical approaches, and providing appropriate answers. The three dimensions have been developed in the published working papers: (1) modeling the liability liquidity risk (Roncalli et al., 2021a), (2) modeling the asset liquidity risk (Roncalli et al., 2021b) and (3) managing the asset-liability liquidity risk (Roncalli, 2021c). This fourth working paper provides three examples and the comprehensive details to compute the redemption coverage ratio, implement reverse stress testing and estimate the liquidation cost of the redemption portfolio. The portfolios have been chosen in order to cover the main asset classes: large-cap stocks, small-cap stocks, sovereign bonds and corporate bonds. Since we provide the data in the appendix, these basic examples are easily reproducible and may help quantitative analysts to understand the different steps to implement liquidity stress testing in asset management.
... Indeed, it encompasses two sources of uncertainty: liability risk and asset risk. As shown by Roncalli et al. (2021a), there are two main approaches for measuring the liability risk. We can use an historical approach or a frequency-severity framework. ...
... Following Roncalli et al. (2021a), the total net assets (TNA) equal the total value of assets A (t) less the current or accrued liabilities D (t): ...
... The goal of this research project was then to fill the gap to develop mathematical and statistical approaches and provide appropriate answers. The first part of this project was dedicated to the liability liquidity risk (Roncalli et al., 2021a) and focused on the statistical modeling of redemption shocks. The second part concerned the asset liquidity risk (Roncalli et al., 2021b) and dealt with the modeling of the transaction cost function. ...
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers the modeling of the liability liquidity risk (or funding liquidity), the second dimension is dedicated to the modeling of the asset liquidity risk (or market liquidity), whereas the third dimension considers the management of the asset-liability liquidity risk (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019, 2020) and are useful for fund managers. In this third and last research paper focused on managing the asset-liability liquidity risk, we explore the ALM tools that can be put in place to control the liquidity gap. These ALM tools can be split into three categories: measurement tools, management tools and monitoring tools. In terms of measurement tools, we focus on the computation of the redemption coverage ratio (RCR), which is the central instrument of liquidity stress testing programs. We also study the redemption liquidation policy and the different implementation methodologies, and we show how reverse stress testing can be developed. In terms of liquidity management tools, we study the calibration of liquidity buffers, the pros and cons of special arrangements (redemption suspensions, gates, side pockets and in-kind redemptions) and the effectiveness of swing pricing. In terms of liquidity monitoring tools, we compare the macro- and micro-approaches of liquidity monitoring in order to identify the transmission channels of liquidity risk.
... Indeed, it encompasses two sources of uncertainty: liability risk and asset risk. As shown by Roncalli et al. (2021a), there are two main approaches for measuring the liability risk. We can use an historical approach or a frequency-severity framework. ...
... Following Roncalli et al. (2021a), the total net assets (TNA) equal the total value of assets A (t) less the current or accrued liabilities D (t): ...
... The goal of this research project was then to fill the gap to develop mathematical and statistical approaches and provide appropriate answers. The first part of this project was dedicated to the liability liquidity risk (Roncalli et al., 2021a) and focused on the statistical modeling of redemption shocks. The second part concerned the asset liquidity risk (Roncalli et al., 2021b) and dealt with the modeling of the transaction cost function. ...
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers the modeling of the liability liquidity risk (or funding liquidity), the second dimension is dedicated to the modeling of the asset liquidity risk (or market liquidity), whereas the third dimension considers the management of the asset-liability liquidity risk (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019, 2020) and are useful for fund managers. In this third and last research paper focused on managing the asset-liability liquidity risk, we explore the ALM tools that can be put in place to control the liquidity gap. These ALM tools can be split into three categories: measurement tools, management tools and monitoring tools. In terms of measurement tools, we focus on the computation of the redemption coverage ratio (RCR), which is the central instrument of liquidity stress testing programs. We also study the redemption liquidation policy and the different implementation methodologies, and we show how reverse stress testing can be developed. In terms of liquidity management tools, we study the calibration of liquidity buffers, the pros and cons of special arrangements (redemption suspensions, gates, side pockets and in-kind redemptions) and the effectiveness of swing pricing. In terms of liquidity monitoring tools, we compare the macro- and micro-approaches of liquidity monitoring in order to identify the transmission channels of liquidity risk.
... In Roncalli et al. (2020), we have developed several methods and tools in order to define a redemption shock R for a given investment fund. This redemption shock is expressed as a percentage of the fund's total net asset TNA. ...
... The liability liquidity risk is studied inRoncalli et al. (2020), whereas the asset-liability management tools are presented inRoncalli et al. (2021) ...
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers the asset-liability management of the liquidity gap risk (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019, 2020) and are useful for fund managers. The review of the academic literature and professional research studies shows that there is a lack of standardized and analytical models. The aim of this research project is then to fill the gap with the goal of developing mathematical and statistical approaches, and providing appropriate answers.
In this second article focused on asset liquidity risk modeling, we propose a market impact model to estimate transaction costs. After presenting a toy model that helps to understand the main concepts of asset liquidity, we consider a two-regime model, which is based on the power-law property of price impact. Then, we define several asset liquidity measures such as liquidity cost, liquidation ratio and shortfall or time to liquidation in order to assess the different dimensions of asset liquidity. Finally, we apply this asset liquidity framework to stocks and bonds and discuss the issues of calibrating the transaction cost model.
... In Roncalli et al. (2020), we have developed several methods and tools in order to define a redemption shock R for a given investment fund. This redemption shock is expressed as a percentage of the fund's total net asset TNA. ...
... The liability liquidity risk is studied inRoncalli et al. (2020), whereas the asset-liability management tools are presented inRoncalli et al. (2021) ...
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers the asset-liability management of the liquidity gap risk (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019, 2020) and are useful for fund managers. The review of the academic literature and professional research studies shows that there is a lack of standardized and analytical models. The aim of this research project is then to fill the gap with the goal of developing mathematical and statistical approaches, and providing appropriate answers. In this second article focused on asset liquidity risk modeling, we propose a market impact model to estimate transaction costs. After presenting a toy model that helps to understand the main concepts of asset liquidity, we consider a two-regime model, which is based on the power-law property of price impact. Then, we define several asset liquidity measures such as liquidity cost, liquidation ratio and shortfall or time to liquidation in order to assess the different dimensions of asset liquidity. Finally, we apply this asset liquidity framework to stocks and bonds and discuss the issues of calibrating the transaction cost model.
