- A. Berger and Bouwman (2009) liquidity creation measure To replicate the Berger-Bouwman (2009) measure on liquidity creation using FR Y-9C data, we apply the data mapping available in Berger et al. (2020).24 Individual on- and off-balance sheet items are aggregated and weighted in line with the classification provided by Berger & Bouwman (2009). Finally, the weighted positions are combined to the aggregate liquidity creation measure for each bank holding company. Note that we only replicate Berger & Bouwman’s so-called “catfat” measure, which is constructed by classifying balance sheet items by category (see Berger & Bouwman, 2009) and includes on- as well as off-balance sheet positions.
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- B. Bai et al. (2018) liquidity risk measure (LMI) To construct the Bai et al. (2018) liquidity mismatch index (LMI), we use information provided in the paper’s Online Appendix together with the FR Y-9C call report template for 2019Q4 to map all balance sheet items, except deposits, to the variables in our dataset. The deposit data is constructed in line with the approach outlined in Bai et al. (2018), using FFIEC 031 call report data for commercial banks aggregated for the respective parent bank holding company.25 24 Berger, A.N., C.H.S. Bouwman, B. Imbierowicz and C. Rauch (2020), How are banks special? – Let me count the ways. 25 We thank Jennie Bai for detailed guidance how to construct their measure. 76 Commercial banks and bank holding companies are matched with the help of the FSSD’s relationship table. We consider a bank holding company to be a commercial bank’s parent, if their relationship exists at least until 31 December 2019.
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Call Reports Why did bank stocks crash during COVID-19? Viral V. Acharya† Robert Engle‡ Sascha Steffen* March 8, 2021 Online Appendix (Not for publication) †
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- In addition to the tests in the main paper, we perform an event study using a 2-day window around 9 March 2020 and plot banks’ 2-day beta adjusted return (= "! − $!""&$)23 on banks’ exposure to the oil & gas sector scaled by Tier 1 capital (Figure A.2.). We find a significant negative correlation suggesting that oil price risk is priced in bank stock returns. 23 The beta is measured pre-crisis, i.e., at the end of Q4 2019. 65 Figure A1. Industry performance during COVID-19 This figure shows the performance of some sectors during COVID-19 using different measures. In Panel A, we plot the total loan return since Jan 1, 2020 of traded in the secondary market in the following sectors: mining, oil & gas retail, leisure, hotel & gaming. In Panel B, we plot oil price volatility (CVOX) since July 1, 2007.
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- In the next step, we calculate the asset and liability weights per category as indicated in Bai et al. (2018) using the parameters and estimates provided by the authors. Accordingly, haircut values as well as the magnitude of the Frist Principal Component used in constructing our measure are averages taken from Bai et al. (2018). As described in the main text of the paper, we use two different proxies for the liquidity premium μt, which is defined as the OIS -3m Treasury Bill spread. We create two LMIs, one using liquidity conditions as of Q4 2019 (LMI – 2019) and one using the worst liquidity condition in March 2020 (LMI – 2020). We weigh the aggregate positions with the respective asset/liability weight to calculate the liquidity risk measure per bank holding company.
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Ivashina, V., and D. Scharfstein, 2010, Bank Lending During the Financial Crisis of 2008, Journal of Financial Economics, 97(3), 319–338.
- Journal of Financial Intermediation 13:90–95. 41 Figure 1. Cumulative drawdowns and bank stock prices Panel A shows the cumulative credit line drawdowns of U.S. firms over the March 1, 2020 to July 1, 2020 period in billion USD. Panel B shows the stock prices of U.S. firms by sector, specifically firms from the energy, banking and other sectors, since Jan 1st , 2020. All variables are defined in Appendix II.
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- Kapan, T., and C. Minoiu, 2020, Liquidity Insurance vs. Credit Provision: Evidence from the COVID-19 Crisis, Working Paper.
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- Loan spreads are constructed using a weighted average (with facility amounts as weights). Bond spreads are constructed based on Gilchrist and Zakrajšek (2012) and obtained from the Federal Reserve website.
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- NYU Stern School of Business, 44 West Fourth Street, Suite 9-10, New York, NY 10012-1126, Email: vacharya@stern.nyu.edu, Tel: +1 212 998 0354. ‡
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- NYU Stern School of Business, 44 West Fourth Street, Suite 9-62, New York, NY 10012-1126, Email: rengle@stern.nyu.edu, Tel: +1 212 998 0710. *
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- Panel A. Bank stock returns Panel B. Bank stock return and liquidity risk 45 Figure 5. Stock prices and liquidity risk of U.S. banks (2007-2009) This figure shows stock prices of U.S. banks with Low or High Liquidity Risk for the Jan 2007 to Jan 2010 period.
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- Panel A. Cumulative drawdowns (in USD bn) Panel B. Stock prices of banks vs. non-financial firms 42 Figure 2. Loan vs. bond spreads This figure shows the time-series difference of loan and bond spreads (Panel A) and splitting loans by rating classes (Panel B). The loan spread is calculated based on Saunders et al. (2021). The sample is based on all loans traded in 2020 that were traded in the U.S. Leveraged Loan Index (LLI) obtained from Leveraged Commentary and Data (LCD) and matched to secondary loan market trading data from Refinitiv. The sample thus comprises about 1,000 U.S. non-financial firms. 3% of the observations are unrated (based on S&P ratings), 25% are CCC-C rated, 54% are B rated, 15% BB rated and 3% BBB rated. Loans with a “D” rating are dropped from the sample (35 firms).
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- Panel A. Liquidity risk Panel B. Components of liquidity risk 44 Figure 4. Stock prices and liquidity risk of U.S. banks This figure shows stock prices of U.S. banks with Low or High Liquidity Risk. We measure Liquidity Risk as undrawn commitments plus wholesale finance minus cash or cash equivalents (all relative to assets) and use a median split to distinguish between banks with Low vs. High Liquidity Risk. Panel A shows the stock prices of both group of banks indexed at Jan 1, 2020, Panel B shows the difference between the stock prices (in percentage point). Panel B plots bank stock returns during the March 1 – March 23, 2020 period on Liquidity Risk. All variables are defined in Appendix II.
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- Panel A. Loan-bond-spread difference Panel B. Loan-bond-spread difference (by rating) 43 Figure 3. Bank balance-sheet liquidity risk This figure shows the time-series of balance-sheet Liquidity Risk over the Q1 2010 to Q3 2020 period. We measure Liquidity Risk as undrawn commitments plus wholesale finance minus cash or cash equivalents (all relative to assets). All variables are defined in Appendix II.
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Panel A. Total loan return by industry Panel B. Oil price volatility 66 Figure A2. Event study around the oil price shock (9 March 2020) This figure plots the 2-day beta adjusted bank stock return around the oil price shock on March 9, 2002 on banks’ loan exposure to the oil & gas industry scaled by Tier 1 capital. 67 Appendix B. Reversal of Credit Line Drawdowns To investigate the effect of credit risk on corporate cash holdings during the COVID-19 pandemic, we construct a sample of all publicly listed U.S. firms, for which financial variables are available at the end of 2019 in Capital IQ. We drop financial firms and utilities and firms with total assets below US$100 million at the end of 2019. Our final sample comprises 1,971 U.S. nonfinancial firms. We construct the sample following Acharya and Steffen (2020).
- Panel B of Figure A.1. show the time-series of oil-price volatility using the CVOX oil price volatility index. While oil price volatility increases episodically during economic downturns (e.g., during the global financial crisis (GFC), i.e., the 2007 to 2009 period), the European sovereign debt crisis (2011-2012), and the oil & gas crisis in 2015-2016), volatility has increased by more than 6 times (to over 100% on an annualized basis) around March 9th , 2020 and energy stocks crashed.
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- Preference for cash has increased / remained high during the 3 quarters in 2020, particularly of lower rated and unrated firms. 72 Appendix C – SRISK-C using only unused C&I loans In Online Appendix C, we calculate SRISK-C but use only unused C&I loans (and the estimated coffients) . Everything else is as in Table 9 of the main paper. 73 Table B.1 Incremental SRISKLRMES-C Panel A reports the calculation of Incremental SRISKMES-C due to the sensitivity of bank stock returns to Unused C&I Credit Lines using the minimum (gmin) and maximum (gmax) sensitivity from different model specifications shown in prior tables. MES-Cmin (%) is calculated as Liquidity Risk x gmin. MES-Cmin ($) is calculated as Liquidity Risk x gmin x MV. Other variables are calculated accordingly. In Panel B, we show the Conditional SRISK (SRISK-C) which is the sum of Incremental SRISKCL and Incremental SRISKMES-C . All variables are defined in Appendix I. Panel A.
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Ramelli, S., and A. F. Wagner, 2020, Feverish stock price reactions to COVID-19. Review of Corporate Finance Studies 9 (3), 622-655.
Repullo, R. 2004. Capital Requirements, Market Power, and Risk-Taking in Banking. Journal of Financial Intermediation 13:156–82.
- Saunders, A., A. Spina, S. Steffen, and D. Streitz, 2021, Corporate loan spreads and economic activity, Working Paper.
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Von Thadden, E.-L. 2004. Bank Capital Adequacy Regulation under the New Basel Accord.
- We estimate the following regression: !"#$!,#,$ = &% + &&())*+#,#,$ + &'-./0*$ + 1(2),* + 1+2,,* + 3$ + 4- + 5!,#,$ Where ())*+#,#,$ are bank-specific aggregate risk proxies, 2),* (2,,*) are bank (firm) characteristics, 3$ are year and 4- industry fixed effects. 80 The results are reported in Table E.1. We first show that idiosyncratic drawdown risk (measured using a firm’s realized equity volatility over the past 12 months) and systematic drawdown risk (measured using a firm’s stock beta) are priced in both commitment fee (AISU) and spread (AISD). This is consistent with, for example, Acharya et al. (2013) and Berg et al. (2015).
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- We measure Liquidity Risk.as undrawn commitments plus wholesale finance minus cash or cash equivalents (all relative to assets) and use a median split to distinguish between banks with Low vs. High Liquidity Risk. Panel A shows the stock prices of both group of banks indexed at Jan 1, 2007, Panel B shows the difference between the stock prices (in percentage point). All variables are defined in Appendix II. 46 Figure 6. Net vs. gross drawdowns This figure shows the time-series of Gross Drawdowns (Panel A) and Net Drawdowns (Panel B) over the Q1 2010 to Q3 2020 period. Gross Drawdowns is the percentage change in a bank’s off balance sheet unused C&I loan commitments (measured during Q1 2020). Net Drawdowns are defined as the change in a bank’s off balance sheet unused C&I loan commitments minus the change in deposits (all measured during Q1 2020) relative to total assets. All variables are defined in Appendix II.
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- We use quarterly debt capital structure data from CapitalIQ and investigate changes in different debt capital structure components during Q4 2019 and Q4 2020 (Table A.1) and quarterly from Q4 2019 to Q3 2020 (Table A.2). Specifically, we inspect the following: drawn credit lines (Drawn CL/Assets), credit line usage (Drawn CL/(Drawn CL + Undrawn CL)), bond debt (Bonds /Assets), term loans (Term loans/Assets), total debt (Total Debt / Assets), and preference for cash (Cash / (Cash + Undrawn CL)).
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