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Appendix I. Estimating HQLA Measures Using Aggregated Data
The author would like to thank Peter Breuer, Cristina Cuervo, Daniel Hardy, Bradley Jones, Ivo Krznar, Sheheryar Malik, Rebecca McCaughrin, and Steven Phillips for their comments on a previous version of this paper.
In this paper, ‘investment funds’ refer to collective investment vehicles. Investment funds cover mainly collective investment vehicles regulated under the Investment Company Act of 1940 in the U.S. (‘mutual funds’) and under the Undertakings for the Collective Investments in Transferable Securities Directive (‘UCITS Directive’) in the European Union.
There are a few exceptions. The association of the Luxembourg fund industry published guidelines on liquidity risk management for UCITS in 2013 (ALFI (2013)) and in France, the securities market regulator, the Autorité des Marchés Financiers (AMF) published a consultation paper on the use of stress tests in August 2016 (AMF (2016)). In the U.S., under SEC rule 2a7, MMFs are required to perform periodic stress tests based upon specified hypothetical events such as increases in short-term interest rates, ratings downgrades, increase in spreads combined with investors redemptions etc.
On the effectiveness of LMTs, an exception is Malik and Lindner (2017), which analyzes the effectiveness of swing pricing as a systemic risk mitigation technique.
In a few cases, the 1st percentile of net flows might be positive, implying that the fund would face inflows under the historical approach.
The integration of the macroeconomic scenario into funds’ liquidity stress tests is one-step further towards system-wide stress testing as severe shocks to banks and funds can be assessed at the same time. Ideally, the model would also feature feedback loops between financial markets, banks and funds, which are outside the scope of this paper. Preliminary work by the FSB attempts at filling this gap.
Panel estimation was also explored, but there is wide heterogeneity at the fund level regarding net flow pattern, which resulted in non-significant results.
Banking sector stress tests typically use quarterly data while investment fund stress tests use higher-frequency data (monthly). Therefore, the values of the macrofinancial variables must be converted to monthly frequency.
The spread variables were not included in the adverse scenario used for the banking sector stress tests. Hence additional assumptions were required. For the Luxembourg FSAP, the highest monthly change in spreads observed over 2007–2016 was applied.
For example, the Bloomberg LQA function provides estimates for each bond, based on market data from similar bonds; other providers such as MSCI LiquidMetrics offer similar services.
For example, for the Luxembourg FSAP, a sample of 191 funds was used, which resulted in a portfolio of around 22,000 individual securities.
Estimates from the empirical literature can also be used to assess the price impact of trades. For example, Greenwood et al. (2015) and ECB (2015) make the assumption that €10 billion of trading imbalances lead to a price change of 10 basis points. However, the authors apply the same estimates by asset classes, without any distinction.
If the analysis is done on a large sample of funds, collecting security-by-security data might be cumbersome and difficult. Therefore, it is also possible to group securities by buckets depending on the issuer type (sovereign, corporates) and ratings. Appendix 1 provides additional details on options to derive the HQLA measures from aggregated data and compares the results with security-level data.
A recent paper by Morris et al. (2017) shows that fund managers tend to hoard cash in advance of anticipated investors’ redemptions and funds investing in less liquid asset classes tend to hoard more than other funds. From that perspective, since managers hoard cash in advance of redemptions, they are likely to amplify fire sales.
For the Luxembourg FSAP, the prorata and waterfall approaches were applied only to the liquidity buffers of the fund and not to the overall portfolio.
In the euro area, funds report derivatives on a gross basis (rather than net basis) and depending on the valuation date, derivatives can appear on the asset or liability side of the funds. For non-UCITS, the Alternative Investment Fund Manager Directive (AIFMD) require Alternative Investment Funds (AIF) to report data to their supervisors, although because the reporting is in its early days, data quality remains an issue.
In the EU, under the UCITS Directive, funds are required to have a depositary bank domiciled in the country of the fund. The duties of the depositary bank are i) safekeeping of assets (custody duties and asset monitoring duties for other assets such as derivatives contracts), ii) oversight of the fund (NAV calculation, investment restrictions etc.), and iii) cash flow monitoring (i.e. ensuring that all cash is properly booked in segregated accounts in the name of the management company of the fund).