Have banks been exposed to open fund sales during the Covid crisis?

If open-end mutual funds experience redemption pressures, they may be forced to sell assets, thereby contributing to asset price dislocations that could in turn be felt by other entities holding similar assets. This fire sale externality is a key justification for the regulatory actions proposed and implemented. In this article, I quantify the spillover risks of unwanted sales and present some preliminary results on the potential exposure of US banking institutions to untimely sales of open-ended fund assets.

Outflows of funds opened during the Covid crisis

We generally view banks as the natural providers of liquidity, primarily through their deposit accounts, but nonbank financial institutions have also become major providers of liquidity in the economy over the years. These include open-ended mutual funds (OEF) which offer investors the option of redeeming their shares on demand. Since redemptions by an investor may affect the share value of the remaining investors, OEF investors have a first come advantage buy out before everyone else in stressful circumstances, generating the conditions for a run-type dynamic (Chen, Goldstein and Jiang [2010]; Goldstein, Jiang and Ng [2017]).

When OEFs experience significant cash outflows, as in March 2020, they can accommodate redemptions through the partial liquidation of their assets. This sale of assets, if carried out on a large scale between funds, affects prices to a greater extent than under normal market conditions when sales by some entities are offset by purchases by others. Such diffuse (fire) asset sales could have macroeconomic consequences, a possibility that was explicitly mentioned in the holistic review of pandemic-related market turbulence that regulators undertook in the fall of 2020.

Quantifying the risk of unexpected sales of open-ended funds

Previously Liberty Street Economy (see here and here), my co-authors and I concluded that the spillover potential of OEFs has increased, in part because OEFs as a whole are attracting more investors, but also because of a growth in community of their assets. In this analysis, we hypothesized a redemption shock affecting certain funds, then assessed the potential impact on other funds. The latter may not have been affected by the initial shock, but the value of their portfolios could be negatively affected because they held assets in common with funds that were affected by the initial shock. However, such hypothetical shocks could spill over beyond the OEFs to other entities with similar assets. In particular, banks may be indirectly exposed to a crisis due to their exposure to assets held by OEFs.

In order to quantify such exposures, I hypothesize shock scenarios that could affect the US registered bond OEF population at any given time, and estimate the magnitude of the spillover impact on the all of the US Bank Holding Companies (BHC). The chart below shows the time series of aggregate overflow vulnerabilities of the top 100 BHCs calculated for each quarter between 1996: Q1 and 2019: Q4. The line is normalized to one in 1996: Q1, so changes can be easily interpreted as changes from this baseline. Estimates suggest that the potential systemic spillover effects of OEFs to BHCs from forced asset sales have increased dramatically over time, about six times since mid-1996. Interestingly, after a period when the Overall vulnerability appeared to have diminished, the fallout reached its historic high point early in the COVID-19 crisis.

BHC losses as a ratio of total equity

BHC losses as a ratio of total equity
Source: author’s calculations.

Notes: BHC is a banking holding company. The losses are normalized to 1996: Q1.

By studying the cross-section of vulnerabilities, one can assess whether a given BHC, based on its own characteristics, appears more or less exposed to a possible OEF clearance sale. The histogram in the graph below shows the distribution of overflow losses for each BHC in the cross section, calculated from 2019: T4. The numbers are normalized to the calculated value for the BHC with the lowest overflow number, so the histogram conveniently describes the “distance” in multiples of overflows through the BHCs. Estimates indicate substantial heterogeneity in exposure of BHCs to potential OEF-induced fire sale.

Normalized losses for cross section BHC, 2019: Q4

Normalized losses for cross section BHC, 2019: Q4
Source: author’s calculations.

Notes: The numbers are normalized against the calculated value for the Bank Holding Company (BHC) with the lowest number of fallouts, so the histogram conveniently describes the “distance” in multiples of fallout across. the BHC. So, for example, the first bar represents BHCs with overflows up to 3.2 times larger than the smallest observed in the cross section.

The fact that BHCs are differently exposed to vulnerabilities related to unwanted sales suggests a possible role for prudential supervision. As a result, I correlate cross-sectional vulnerability with characteristics specific to BHCs that are likely to contribute to increased vulnerability: the total assets appearing on their balance sheets, the equity / asset ratio, and the proportion of relatively illiquid assets held in their balance sheets. their balance sheets. Total BHC assets correlate positively with vulnerability to spillovers (since larger dollar exposures likely translate into larger declines in asset values), but the correlation is modest. The (negative) correlation with the equity / assets ratio of a BHC is more relevant. And finally, as one would expect, the correlation is strong and positive with the relative holdings of more illiquid assets.

BHC fire sale vulnerability in March 2020

Using the cross section of BHC vulnerabilities from 2019: T4, I am building an index of latent vulnerability to shock which then materialized in the following quarter. Did the BHCs that came from a ex ante outlook more exposed to the disposal of OEF assets stronger impact a posteriori? Since the measure of exposure in 2019: Q4 is purely determined by the degree of community of assets before the crisis, it can reasonably be considered independent of the factors that caused the pressure on buybacks and sells. assets that followed during the first half of March.

To detect an immediate impact on a BHC’s latent vulnerability balance sheet, I focus on its holdings of available-for-sale securities as a percentage of total assets. If asset sales had an impact on prices, BHCs with greater exposure to OEF sales should have suffered greater losses in the market value of their securities holdings. I find that indeed, BHCs with higher than median latent vulnerability showed a larger reduction (estimated at around 56 basis points) in the value of their shares of securities held. This change is statistically significant. It is also economically significant because the magnitude is approximately 10% of the standard deviation of the distribution of the change in value of shares in the BHC securities portfolio.

To trace the potential impact of the immediate change in balance sheet values ​​on the possible effects on bank operations and reported performance, I compare the return on equity (ROE) of BHCs in 2019: Q4 with their performance in 2020: T4. Preliminary results indicate that the ROE of latent high vulnerability BHCs deteriorates disproportionately compared to low vulnerability banks, by about half a percentage point higher. The difference in impact between the two groups is around 10% if we consider the ROE over four quarters. It is economically significant since the average ROE between the two groups is around 10%.


BHCs are vulnerable to potential forced sales of OEF assets. These vulnerabilities have grown over time, simply because of the steadily increasing dollar amount of assets held in common. Are there specific aspects of a BHC’s business model that make it more vulnerable to this type of risk? Are there specific asset classes that could be particularly important when shocks are transmitted from OEF to BHC? Exploring these and other related questions is essential to developing supervisory guidelines and observable metrics that could help examiners supervise banks.

Nicola Cetorelli

Nicola Cetorelli is vice president of the research and statistics group at the Federal Reserve Bank of New York.

The views expressed in this article are those of the author and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

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