Measuring Moral : The Impact of Systemic on Loan Portfolios Yu Shan

Abstract This paper examines the endogenous adjustments in the risk of bank’s loan portfolio in response to systemic risk levels. In particular, I consider whether individual increase or decrease their loan portfolio risk when systemic risk levels vary. I examine the tightness of loan covenants and loan spreads to determine bank responses to systemic risk at microlevel (Δ퐶표푉푎푅) and macrolevel (CATFIN). I find that banks require tighter loan covenants and higher loan spreads following high Δ퐶표푉푎푅 and high CATFIN, indicating that banks increase loan portfolio risk when microlevel and macrolevel systemic are high. These are consistent with hypothesis. I also find that loan spreads are less sensitive to Δ퐶표푉푎푅 after the , suggesting weaker moral hazard by systemic important banks after the crisis. I also find that loan covenants and loan spreads are less sensitive to Δ퐶표푉푎푅 for those TARP recipients during periods of recession, or periods when CATFIN exceeds the early warning threshold. This suggests lower loan portfolio risk and weaker moral hazard effects, and indicates that systemic important banks adjust the risk of their loans in order to pull back from the brink of delinquency and .

1. Introduction

Banks are special because they are critically important to the capital allocation process inherent in a well- functioning financial system. Individual bank behavior has systemic implications on overall macroeconomic conditions. Concerns about adverse macroeconomic consequences justify the vast array of regulatory policies and governmental safety nets designed to bail out banking systems in crisis. However, it is well known that while these policies may reduce the severity of macroeconomic downturns during financial crises, they induce banks to engage in strategic behavior that results in moral hazard risk. In this paper, I examine the role of bank-level systemic risk on the basic characteristics of the loan portfolio. In particular, I examine bank responses to moral hazard temptations to measure their responses as reflected in their loan portfolios. I focus on two fundamental bank choice variables: loan risk exposure and the tightness of loan covenants.

How does the bank’s systemic risk impact the portfolio characteristics of the bank’s loan portfolio? There are two possible channels. The first is the moral hazard channel, which implies that TBTF safety nets and subsidies encourage bank risk taking behavior. Failures of systemically important banks may cause significant disruptions to the financial system and economic activity. Therefore, upon a financial crisis, governments usually don’t have many options but to reach out to bail out systemically important banks to recover lending activities and restore economic growth. Although governments wouldn’t like to make any explicit

1 commitment to these actions to prevent moral hazard, there still exists an expectation of implicit public guarantee among banks and banks’ creditors. The expectation of “implicit public guarantee” weakens market discipline (Acharya, Anginer, and Warburton, 2016) and may induce systemically important banks to shift their lending toward higher-risk, higher-return projects. Expectation of bailout also incentivizes borrowers to choose banks with higher bailout expectations because they are perceived as safer and are more able to provide stable liquidity during economic downturn when liquidity is scarce. Berger and Roman (2015) suggest that because TARP banks gained market share and market power because they are perceived as safer and are less likely to fail, and are associated with larger loan or deposit growth after TARP. Koetter and Noth (2015) find that higher bailout expectations for the unsupported banks increase loan rates, reduce deposit rates. Overall, the moral hazard channel indicates that banks with higher bailout expectation will shift their lending to riskier loans, and therefore ask for higher loans spread and set tighter covenants.

The second channel is the systemic risk response channel. The explicit and implicit cost of funds from government support may be high. First, the injected preferred equity has priority over common equity, which may amplify the losses of common equity holders in the event of failure, resulting in falls in the value of common equity, and increasing the difficulty of raising equity (Berge, Roman and Sedunov, 2016). Berger and Roman (2015) suggest that the TARP funds may be relatively expensive. If banks’ shareholders are concerned about the negative implications of , they may adjust the risk of their loans in order to pull back from the brink of delinquency and bailout. Second, the supported banks may have to undertake certain social welfare responsibilities through lending expansion, which may not be an equilibrium choice from shareholders’ perspective. The initial objective of bailouts is to stabilize the financial system and macroeconomy, but public also often expect the bailed-out banks may expand their lending. However, during financial crisis, safe and profitable projects are very rare and expanding lending in this circumstances may be at shareholders’ best interest. Due to all these potential costs, systemically risk banks may try to avoid bailouts by reducing the bank’s systemic risk exposure. This could take the form of adjustments to the risk of the bank’s loan risk. I denote this the systemic risk response channel1.

1 Another channel that is not captured in this paper but could be very important in the future is the systemic risk pricing channel. The systemic risk pricing channel implies that as regulations have imposed greater regulatory costs, stricter , and higher disciplinary pressure on systemically important banks, the opportunity for moral hazard risk shifting may be reduced. For example, G-SIBs are subject to higher capital buffer requirement, Total Loss-Absorbing Capacity (TLAC) standard, resolvability requirement, and higher supervisory expectations. With all these higher regulatory requirements, systemically important banks are at a competitive disadvantage relative to other banks, so they have to charge higher loan spreads and require tighter covenants per unit of borrower risk in order to reduce the bank’s systemic risk levels.

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Under the moral hazard channel, systemically risky banks would have incentives to increase the risk of their loans as the likelihood of bailout increases. In contrast, under the systemic risk response channel, systemically risky banks would reduce their risk exposures in order to reduce the likelihood that their bank would require a bailout. In this paper, I examine the risk characteristics of the bank loans using loan spreads and covenant tightness. That is, I relate overall systemic risk and each bank’s microlevel systemic risk exposure to the bank loan risk and find support for the moral hazard hypothesis.

In this paper, I test these hypotheses using two complementary measures of systemic risk. I consider overall systemic risk, as well as microlevel systemic risk imposed by each individual bank. To measure the macro- level aggregate systemic risk, I utilize CATFIN (Allen, Bali and Tang (2012)). This is a cross-sectional measure that identifies the overall level of systemic risk in the financial system at each point in time. To measure the micro-level systemic risk, I utilize Δ퐶표푉푎푅 (see Adrian and Brunnermeier (2016)) to determine the impact of an individual bank’s insolvency on overall systemic risk. The greater the contribution of an individual bank’s insolvency to market-wide systemic risk, the greater the individual bank’s imposition of systemic risk onto the macroeconomy.

First, I look at how the two systemic risks individually and jointly determine the loan spread and covenant strictness. Systemically risky banks increase their loan spreads as well as their covenant tightness as the risk of their loans increases. My empirical results first indicate that microlevel systemic risk is positively associated with loan spread and covenant tightness when financial system and macroeconomy are in health states. This is consistent with moral hazard hypothesis. With the expectation of bailout, systemically important banks shift their lending towards riskier loans. I also find that loan spread is positively associated with the likelihood of bailout, as measured by the overall systemic risk levels (CATFIN), indicating that banks generally increase their risk taking when the overall systemic risk is high. However, I also find that both loan spread and covenant strictness are negatively associated with the interaction of microlevel and overall systemic risk. The results are similar if I replace the overall systemic risk with an early-warning dummy determined by the overall systemic risk. These results indicate that when systemic risk is system-wide, the systemic risk response effect dominates the moral hazard effect for systemically risky banks, who reduce their risk exposure by reducing the loan spreads and relaxing the strictness of covenants. That means, when the whole financial system is risky, for those banks who are more likely to be bailed out, instead of participating in more moral hazard activities, they are trying hard to reduce their risk-taking and pull back from the brink of delinquency and bailout.

Then I conducted a diff-in-diff analysis to investigate how the relationship between bank risk taking and systemic risks is affected by another proxy for bailout out expectation, Troubled Asset Relief Program (TARP). On the one hand, even though TARP itself may not create a new government safety net, the support programs create the perception about the likelihood of banks being bailed out in the event of future losses,

3 which may cause moral hazard and incentivizes banks to shift their lending toward higher-risk, higher-return projects. On the other hand, systemic risk response channel suggests that due to all the explicit and implicit economic and social cost of bail out, banks that are most likely to be bailed out, which are supported banks with high CoVaR during the period of high overall systemic risk, may try harder to reduce their risk-taking and avoiding being bailed out. My results from diff-in-diff regressions show that although TARP support generally increases the risk-taking of supported banks (TARP support is positively associated with loan spread), it also significantly reduces the risk-taking of banks with high Δ퐶표푉푎푅 during periods of high CATFIN. For these banks, systemic risk response channel dominates the moral hazard channel more significantly and economically.

My main empirical results indicate that the moral hazard channel dominates the systemic risk response channel when the macro-level systemic risk is low and the economy is in non-recession periods, but the systemic risk response channel dominates during periods of high overall systemic risk and economic recessions.

My methodology is not free from endogeneity and sample selection bias. First, my analysis can be subject to reverse causality. Although it is hard to imagine an equilibrium model in which banks optimally choose systemic risk, where as we can image the bank’s systemic risk exposure impacting loan decision variables, the reverse causality also occurs. Therefore, I have to use an IV, diff in diff, etc. methodology to address the endogeneity problem. Second, my analysis may be subject to the classic identification problem as the composition of the loan portfolio is determined by the interaction of bank loan supply and borrower loan demand. To address this endogeneity, I plan to use two alternative instrument variables. First, I can use the Federal Loan Officer Opinion Survey as an instrument variable. Federal Loan Officer Opinion Survey a quarterly survey providing information about the availability and terms of credit in securities financing and over-the counter (OTC) derivatives markets. Second, I will also use the overall median tightness and loan rates for my entire sample of banks as an instrument variable.

The paper proceeds as follows. Section 2 provides a brief review of the literature and describes my methodology and empirical hypotheses. In Section 3, I discuss the construction of my database and present the empirical results in Section 4. Section 5 offers robustness tests. Section 6 concludes.

2. Literature and hypothesis

The recent financial crisis highlights some features of the risk-taking by financial institutions. A growing literature has shown that financial institutions have incentives to take on tail risk, which generates nice returns most of the time and leads to severe consequences with small probability (Rajan (2005), Kashyap, Rajan, and

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Stein (2008), Hoenig (2008) and Strahan (2013)). Researchers also show that financial institutions have incentives to take on similar and systemically important investments and correlate risk exposures, with the expectation to be bailed out in the event of a systemic crisis (Farhi and Tirole (2011)). The reliance on "market discipline" in controlling institutions’ systemic risk taking is shown to be ineffective because investors don’t fully price systemic tail risk, and the implicit public guarantees exacerbate moral hazard in lending behaviors, distortions in bond and stock pricing and weakens market discipline (Di Gong (2014), Chava, Ganduri and Yerramilli (2014), Acharya, Anginer, and Warburton (2015)).

This paper uses the Troubled Asset Relief Program (TARP) and macro-level systemic risk to proxy for bail- out expectations, and use difference-in-difference regressions to investigate whether the sensitivity of loan spread and covenant tightness can be affected by them. TARP, a program of the U.S. Treasury to purchase equity in financial institutions and recapitalize the financial sector, was the largest of the U.S. government’s measures implemented in 2008 to address the financial crisis. The provision for TARP by Congress allowed the Treasury to purchase or insure up to $700 billion of troubled assets or to purchase equity in the banks themselves. On October 28, 2008, Treasury Secretary Henry Paulson authorized the first wave of TARP equity capital injections for nine of the largest banks. Shortly thereafter, more banks received funds from the government under the TARP program. Although the original focus of TARP appears to be stabilization of the banking sector, public discourse subsequent to the program’s implementation revealed that TARP was implicitly expected to increase bank lending.

TARP is the first large capital injections program in US history, which allowed the Treasury to purchase or insure up to $700 billion of troubled assets or to purchase equity in the banks. TARP programs cover the areas of banking institutions, auto industry and struggling families, and main program to stabilize the financial system is the Capital Purchase Program (CPP), which is a preferred stock and equity warrant purchase program that provided approximately $250 billion of capital to 707 financial institutions in 48 states in US. Since CPP is the program where banks directly get capital injection, in this paper I will mainly focus on the recipients of CPP.

As with all other government support programs, a moral hazard problem arises if the support programs create the perception about the likelihood of banks being bailed out in the event of future losses, even though the support itself may not create a new government safety net. This moral hazard incentivizes banks to shift their lending toward higher-risk, higher-return projects, and if public also expects the future government support, they may price the implicit government guarantee and thus reduce their required return from these banks. There are two implications. First, compared with similar unsupported banks, the supported banks may have higher loan risk-profile; Second, compared with loans of similar risk-profile but were issued by unsupported banks, the loans from supported banks should have lower loan spreads.

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Several papers investigate banks’ risk-taking and moral hazard under the TARP program. Wilson and Wu (2010) find banks under capital infusion and the taxpayer subsidy may choose to shift the risk to their creditors, leading to inefficiency in the TARP programs. Elyasiani et al. (2011) find that shareholders reacted positively to the TARP injections, which were associated with an increase in the banks’ . Duchin and Sosyura (2011) analyze the effect of TARP on bank lending and risk-taking. The results indicate that the TARP banks approve riskier loans, even after controlling for the selection of TARP banks based on political connections. Black and Hazelwood (2013) contrast the results for large and small banks and find that, relative to non-TARP banks, the risk of loan originations increased at large TARP banks but decreased at small TARP banks, and loan levels also moved in different directions for large and small banks. They suggest that, for large banks, the increase in risk-taking without an increase in lending is suggestive of moral hazard due to government support. Berger and Roman (2015) look at TARP from the competition perspective and find that TARP recipients received competitive advantages and increased both their market shares and market power, which is driven primarily by the safety channel (TARP banks may be perceived as safer).

This paper complements the existing literature on TARP by investigating the risk-taking behaviors of TARP recipients that are systemically risky. Although bank size is significantly related to bank systemic risk (Adrian and Brunnermeier, 2016), systemic risk better calibrates the systemic importance of a bank, and thus better captures banks’ bailout expectation under TARP assistance. This paper also contributes to the literature by investigating how TARP affects the relationship between bank systemic risk and loan characteristics (e.g. covenant tightness and loan spread).

The main question this paper asks is how bank’s systemic risk impact the bank loan characteristics. There are two major channels whose effects may offset each other. First, the moral hazard channel implies that TBTF safety nets and subsidies encourage bank risk taking behavior. That is, high risk loan portfolios with high potential returns could benefit the bank if successful. If, however, the projects fail, the bank will be bailed out at a cost to the general taxpayer, because failures of systemically important banks may cause significant disruptions to the financial system and economic activity. The expectation of public guarantee also weakens market discipline. As shown in Chava, Ganduri and Yerramilli (2014) and Acharya, Anginer, and Warburton (2015), the bond spread of big financial institutions is less sensitive to risk than others. Alternatively, the systemic risk response channel suggests that the explicit and implicit cost of funds from government support may discourage banks from taking excessive-risk. For example, some governments may inject capital to distressed financial institutions by purchasing preferred equity. The injected preferred equity has priority over common equity, which may amplify the losses of common equity holders in the event of failure, resulting in falls in the value of common equity, and increasing the difficulty of raising equity (Berge, Roman and Sedunov, 2016). If banks’ shareholders are concerned about the cost of bailouts, they may adjust the risk of their loans in order to pull back from the brink of delinquency and bailout. Moreover, the supported banks may have to undertake certain social welfare responsibilities through lending expansion, which may not be an

6 equilibrium choice from shareholders’ perspective. Although the major objective of bailouts is to stabilize the financial system and macroeconomy, the public and even government may also hope the bailed-out banks would expand their lending when safe and profitable projects are very rare. Due to all these potential costs, systemically risk banks may try to avoid bailouts by reducing the bank’s systemic risk exposure. This could take the form of adjustments to the risk of the bank’s loan risk.

Financial covenants of syndicated bank loans are accounting-based ratios and values that the borrower must meet to be in compliance with the credit agreement. Financial covenants can be motivated as contractual devices that strengthen a lender’s monitoring ability (Rajan and Winton (1995)). A covenant violation triggers a renegotiation, shifts control rights to creditors and allows creditors to intervene before the occurrence of more severe consequences. Renegotiations are rarely a consequence of distress or default (Robert and Sufi (2009)), and usually result in large changes to the amount, maturity, and pricing of the contract. After a renegotiation there usually follow sharp declines in capital expenditures, acquisitions, leverage and shareholder payouts, and increases in CEO turnovers (Nini et al., 2011).

A number of literature has been investigating the determinants of the covenant strictness and the evidence strongly suggests that, on average, riskier firms receive contracts with stricter covenants (see Berlin andMester (1992), Billett, King, andMauer (2007), Rauh and Sufi (2010), Demiroglu and James (2010)). Murfin (2012) documents that banks write tighter contracts than their peers after suffering payment defaults to their own loan portfolios. Financial covenants are good indicators of financial institution’s risk taking for the following reasons. First, stricter loan covenants serve as more effective screening devices and reflect more rigorous lending standards. Based on information asymmetry, good firms signal their high quality by accepting strict loan covenants, as it is costly for bad firms that are close to covenant violation to mimic. Hence unobserved firm quality is expected to be positively related to covenant strictness. Bradley and Roberts (2004) find that large lending syndicates incorporate more covenants into their debt contracts, indicating more rigorous. Second, stricter covenants attribute more ex-post control rights to creditors in case of default or technical default (Garleanu and Zwiebel (2008)). The creditors have the option to early terminate the loan before a severe deterioration in firmvalue, although the creditors often choose to renegotiate the contract instead (Smith andWarner (1979), Smith (1993), Chen and Wei (1993), Beneish and Press (1995), Chava and Roberts (2008), and Roberts and Sufi (2009b)). Thrid, stricter covenants improve borrowers’ market value, reducing the their credit risk. Nini, Smith, and Sufi (2009) empirically find that firms obtaining contracts with a new covenant restriction have subsequent increasing market value and improving operating performance. Zhang (2009) finds that strict loan covenants preserve the liquidation value of the borrower, and help improve default recovery. Robert and Sufi (2009) file that net debt issuing activity experiences a sharp and persistent decline following debt covenant violations, when creditors use their acceleration and termination rights to increase interest rates and reduce the availability of credit. They also find that the decline in net debt issuances is significantly larger for firms with high leverage and low market-to-book ratios at the time of the violation.

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In this paper I borrow the Δ퐶표푉푎푅 in Adrian and Brunnermeier (2016) as a measure of micro-level systemic risk. According to the classification by Brunnermeier, Crocket, Goodhart, Perssaud, and Shin (2009), a systemic risk measure should identify the risk to the system by “individually systemic” institutions, which are so interconnected and large that they can cause negative risk spillover effects on others. Based on this criteria, Adrian and Brunnermeier (2016) proposed Δ퐶표푉푎푅, which is defined as the change in the of the financial system conditional on an institution being under distress relative to its median state. Δ퐶표푉푎푅 has several favorable properties, such as clone property, systemic as part of a herd, and endogeneity of systemic risk (see Adrian and Brunnermeier (2016)), which make it a useful analytical tool for financial institutions, academic researchers, and policymakers. The construction of Δ퐶표푉푎푅 is introduced in the data section.

In this paper I use the CATFIN proposed by Allen, Bali, and Tang (2012) to quantify macro-level systemic risk. Investigating systemic risk at the macro-level is important because even if the individual bank’s micro level of risk taking may be low, their collective interconnection could create tremendous macro-level systemic risk. CATFIN measures this collective tail risk of the banking system. CATFIN quantifies the risk of catastrophic losses in the financial system and has predictive power in forecasting future macroeconomic downturns.

3. Data The sample period of my study spans 1995-2015. All bank financial data is taken from Reports of Condition and Income (Call Reports) and Consolidated Financial Statements for Holding Companies (FR Y-9C) by the System and all market data is from CRSP. Bank loan information is obtained from Loan Pricing Corporation’s (LPC) Dealscan loan database. The Dealscan database contains historical information on the terms and conditions of deals in the global commercial loan market. Borrowers’ financial data is obtained from Compustat and is linked to Dealscan using the Dealscan-Compustat linking data provided by Roberts and Sufi (2009). All observations are quarterly.

Financial covenants of syndicated bank loans are accounting-based ratios and values that the borrower must meet to be in compliance with the credit agreement. Financial covenants can be motivated as contractual devices that strengthen a lender’s monitoring ability (Rajan and Winton (1995)) and control rights in case of covenant violation (Garleanu and Zwiebel (2008)). Covenants are also effective tools for risk control since it can improve borrowers’ market value and reduce borrowers’ credit risk (Nini, Smith, and Sufi (2009)). Following the approach outlined in Murfin (2012) and the standard covenant definitions specified by Demerjian (2016), I construct a Dealscan-based and simulated-based measure of aggregate probability of covenant violation (푃푣푖표퐿) across the entire set of covenants included in a loan. This measure considers four properties of financial covenants: number of covenants, initial covenant slack, scale of covenants, and correlation between financial ratios, and is better than other measures in the literature that only considers one

8 or two dimensions of loan covenants. Specifically, 푃푣푖표퐿 measures the probability of violating at least one covenant in the deal, with 0 meaning zero probability and 1 meaning 100% probability.

To construct 푃푣푖표퐿, first consider a single log of the financial ratio 푟 that receives a shock in the period after the loan is granted, ln(푟′) = ln(푟) + 휀~푁(0, 𝜎2) Where 푟 is the initial financial ratio right before the loan origination, 푟′ is the financial ratio after a shock, and 휀 is the shock to logged financial ratios. If a covenant for 푟 is written such that 푟′ < 푟 allocates control rights to the lender, then 푙푛(푟) − 푙푛(푟) 푝 ≡ 1 − 훷 ( ) 𝜎 is the ex ante probability of technical default, where Φ is the standard normal cumulative distribution function. For a contract with more than one financial covenant, I estimate Σ is variance-covariance matrix associated with quarterly changes in the logged financial ratios of levered Compustat firms. I estimate one variance- covariance matrix for each industry based on the first digit of SIC code 2. Thus, for contracts with 푁 financial covenants, let 푟 be an 푁×1 vector of initial financial ratios and 푟′ be the vector of financial ratios after an N dimensional shock, then ′ 푙푛(푟 ) = 푙푛(푟) + 휖 ~ 푁푁(0, 훴) 푡ℎ ′ ′ Therefore, if the 푛 element of r migrates to 푟푛 after a shock such that 푟푛 < 푟푛, a covenant violation occurs, then

푃푣푖표푙 ≡ 푝 = 1 − 퐹푁(푙푛(푟) − 푙푛(푟)) where 퐹푁 is the multivariate normal cumulative distribution function with mean 0 and variance Σ.

Although Dealscan provides information on the general types of covenants that are used and their violation threshold values, it does not provide definitional details of the actual construction of the covenant in the loan market, which inhibits precise calculation of violation probability. However, the standard definitions determined by Demerjian (2016) for financial covenants effectively minimizes the measurement error caused by the absence of actual contract-level covenant definitions. Combing their work, I construct the single measure of contract strictness (Pviol) following Murfin (2012) and borrow the standard definitions from Demerjian (2016).

I use the information provided by the Federal Reserve System via its National Information Center (NIC) database to identify financial institutions acting as lead arrangers in my sample. I didn’t use the identity

2 In Murfin (2012), the covariance matrix allows for both cross-sectional and over time by estimating distinct matrices each year using rolling 10-year windows of backward looking data. But Murfin (2012) also reports that the result is not significantly different if he uses one single pooled covariance matrix across all firms and time.

9 variables for lender and ultimate owner in Dealscan because Dealscan overwrites the ultimate owner of the lenders after mergers and acquisitions, i.e., the ultimate owner in Dealscan is the ultimate owner at the end of the merger chain. However, for this paper, I need to know the ultimate owner at the time of the loan issuance for analyses at the holding company level. NIC provides detailed information about financial institutions, including types of institutions, establishment time, ownership information, address changes, name changes, merger & acquisition history. NIC also provides each financial institution’s RSSD ID, a unique identifier assigned to each financial institution by the Federal Reserve System. Based on the lender information provided by Dealscan, including name, location and lending history, I manually find each lead arranger’s RSSD ID. Using their RSSD ID, which is item RSSD9001 in Call Report and Y-9C, lead arrangers’ ultimate owner at the time of loan origination is determined by cross-checking the information contained in Call Report items RSSD ID of Regulatory High Holder 1 (RSSD9348), Financial Higher Holder ID (RSSD9364), and Financial High Holder Percent of Equity (RSSD9365). The three items provide the RSSD ID (RSSD9001) of lead arrangers’ ultimate bank holding company during the time when a lead arranger has a RSSD ID. For a lead arranger who was acquired by another bank and lost its RSSD ID but kept its lending activity afterwards, the acquirer’s RSSD ID was shared to the lead arranger as its new RSSD ID, and the new ultimate owner can be found from the Call Report and Y-9C items RSSD9348, RSSD9364, and RSSD9365. The full bank and bank holding companies merge and acquisition history is obtained from Federal Bank of Chicago. Using the RSSD ID, I can link Dealscan to the Y-9C to obtain bank financial data, and I can also link Dealscan to CRSP to collect market data through the PERMCO-RSSD link table provided by the Federal Reserve Bank of New York. I collect bank data at the level of the bank holding company using Y-9C reports, including the banks’ total assets, liabilities, risk-based capital, equity, and net income.

To measure micro-level systemic risk, I follow the methodology used in Adrian and Brunnermeier (2016) to generate time-varying Δ퐶표푉푎푅. First, run the following quantile regressions in the weekly data (where i is an institution): 푖 푖 푖 푖 푋푡 = 훼푞 + 훾푞푀푡−1 + 휖푞,푡,

푠푦푠푡푒푚|푖 푠푦푠푡푒푚|푖 푠푦푠푡푒푚|푖 푠푦푠푡푒푚|푖 푖 푠푦푠푡푒푚|푖 푋푡 = 훼푞 푀푡−1 + 훽푞 푀푡−1 + 훽푞 푋푡 + 휖푞,푡 ,

푖 푠푦푠푡푒푚|푖 Where 푋푡 is the weekly return of institution I in week t, 푋푡 is the financial sector return in week t, and

푀푡−1 is a vector of seven systematic state variables in week , including three-month yield change, term spread change, TED spread, credit spread change, market return, real estate excess return, equity volatility. Then I generate the predicted values from these regressions to obtain 푡 푖 푖 푉푎푅푞,푡 = 훼̂푞 + 훾̂푞푀푡−1, 푖 푠푦푠푡푒푚|푖 푠푦푠푡푒푚|푖 ̂푠푦푠푡푒푚|푖 푖 퐶표푉푎푅푞,푡 = 훼̂푞 + 푟̂푞 푀푡−1 + 훽푞 푉푎푅푞,푡 푖 Finally, compute the 훥퐶표푉푎푅푞,푡 for each institution: 푖 푖 푖 ̂푠푦푠푡푒푚|푖 푖 푖 훥퐶표푉푎푅푞,푡 = 퐶표푉푎푅푞,푡 − 퐶표푉푎푅50,푡 = 훽 (푉푎푅푞,푡 − 푉푎푅50,푡)

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푖 푖 From these regressions, I get a panel of weekly 훥퐶표푉푎푅푞,푡. Then obtain a quarterly time series of 훥퐶표푉푎푅푞,푡 by averaging the weekly risk measures within each quarter. Throughout the paper, I use q equals 99%.

To construct the macro-level systemic risk, CATFIN, Allen, Bali, and Tang (2012) first estimate VaR at the 99% confidence level using three different methodologies - the generalized Pareto distribution (GPD), the skewed generalized error distribution (SGED) and the non-parametric estimation method based on the left tail of the actual empirical distribution without any assumptions about the underlying return distribution. CATFIN is defined as the arithmetic average of the GPD, SGED and non-parametric VaR measures. I obtain the quarterly CATFIN data from Allen, Bali, and Tang (2012) spanning 1995 to 2013.

I also use a set of control variables to control for loan characteristics, borrower characteristics and lender characteristics. The definitions for the variables are introduced in the appendix.

Figure 1 depicts the relationship between CATFIN and the two measures of loan risk. The first figure shows the relationship between simple average covenant tightness of all deals originated by all lenders in each month and monthly CATFIN lagged by a quarter. Each dot stands for a month. We can see a positive relationship between covenant tightness and CATFIN, showing that banks increase their loan risk during higher aggregate systemic risk periods. That positive relationship is more obvious in the second figure, which depicts the relationship between all-in-drawn spreads and CATFIN. The two figures show that banks increase their loan portfolio risk during high aggregate systemic risk periods.

4. Empirical Analysis 4.1 Systemic Risk and the Probability of Covenant Violation In this section, I investigate the impact of systemic risks on the probability of covenant violation. The question of central interest is whether bank-level and aggregate systemic risks predict the covenant violation probability of newly issued loans. I estimate the following equation using a pooled regression:

푃푣푖표퐿푖,푗,푡 = 훼0 + 훼1Δ퐶표푉푎푅푗,푡−1 + 훼2퐶퐴푇퐹퐼푁푡−1 + 훼3Δ퐶표푉푎푅푗,푡−1×퐶퐴푇퐹퐼푁푡−1 + 훼4ln (퐷푒푎푙 퐴푚표푢푛푡)푖,푡

+ 훼5ln (푀푎푡푢푟푖푡푦)푖,푡 + 훼6ln (푁푢푚퐿푒푎푑푒푟푠)푖,푡 + 훼7푆푒푐푢푟푒푑푖,푡 + 훼8퐴푙푡푚푎푛푍푖,푡−1

+ 훼9ln (푇표푡푎푙 퐴푠푠푒푡푠)푗,푡−1 + 훼10퐶푎푝푖푡푎푙푅푎푡푖표푗,푡−1 + 훼11푅푒푡푢푟푛 표푛 퐸푞푢푖푡푦푗,푡−1

+ 훼12퐺퐷푃푡−1 + 훼13푏푎푛푘푗 + 휀푖,푗,푡 (1)

푃푣푖표퐿푖,푡 is the aggregate probability of covenant violation for deal 푖, and 푡 is the time of loan origination.

Δ퐶표푉푎푅푗,푡−1 is lender j’s systemic risk at time t-1, a quarter before loan origination. 퐶퐴푇퐹퐼푁푡−1 is the aggregate systemic risk in quarter t-1. ln (퐴푚표푢푛푡)푖,푡 is the natural log of the deal amount. ln (푀푎푡푢푟푖푡푦)푖,푡 is the natural log of the maturity (in months) of deal i. Deal maturity is calculated as the weighted average maturity of all facilities in the deal. ln (푁푢푚퐿푒푎푑푒푟푠)푖,푡 is the natural log of the number of lead arrangers for deal i. 푆푒푐푢푟푒푑푖,푡 is a dummy variable with value 1 if the deal is secured, and 0 otherwise. 퐴푙푡푚푎푛푍푖,푡−1 is

11 the borrower’s Altman Z-score in quarter t-1. ln (푆푖푧푒)푗,푡−1 is the natural log of lead lender j’s total asset in quarter t-1. 퐶푎푝푖푡푎푙푅푎푡푖표푗,푡−1 is lender j’s book equity over total assets in period t-1. 푅푂퐸푗,푡−1 is lender j’s net income over book equity in period t-1. I also include lender fixed effects to control for heterogeneity that is constant over time and correlated with risk. At last, I include the percentage change in GDP (퐺퐷푃푡−1) to control for macroeconomic trends in the overall economy.

Table 2 and Table 4 show the summary statistics and spearman correlation matrix for all variables included in the regression above. As shown in the correlation matrix, 푃푣푖표퐿푖,푡 is positively correlated with Δ퐶표푉푎푅푗,푡−1 (𝜌 = 0.0784) and CATFIN (𝜌 = 0.0604) at 1% significance level.

Table 5 reports coefficients from the pooled estimation of (1). Column I only includes Δ퐶표푉푎푅푗,푡−1, whose coefficient is positive and significant (p-value <0.01), which indicates that on average banks with higher systemic risk invest in riskier loans, and therefore require tighter loan covenants. This result persists in other specifications. In column II, 퐶퐴푇퐹퐼푁푡−1 is positive and significant (p-value<0.05), and this is consistent with what we observe in Figure 1. In column III and IV, I add both 퐶퐴푇퐹퐼푁푡−1 and 푁퐵퐸푅푡−1 as independent variables, and interact them with Δ퐶표푉푎푅푗,푡−1. Both columns show that the coefficients on Δ퐶표푉푎푅푗,푡−1 are significant (p-value <0.01). Column IV indicates that even though both high Δ퐶표푉푎푅푗,푡−1 and high CATFIN predict higher covenant tightness, the impact of Δ퐶표푉푎푅푗,푡−1 on covenant tightness is weaker if the loan is originated during periods of high aggregate systemic risk. Column V also shows that the sensitivity of covenant tightness on Δ퐶표푉푎푅푗,푡−1 is weaker if the loan is originated in a recession period. Take Column V as an example, on average, when the loan was not originated in a quarter of recession, then for each one standard deviation increase in Δ퐶표푉푎푅푗,푡−1, the probability of covenant violation will increase by 2.39%. When the loan was originated in a quarter of recession, then for each one standard deviation increase in

Δ퐶표푉푎푅푗,푡−1, the probability of covenant violation will increase by 0.42%.

It is also interesting to look at the aggregate effect. Take Column III in Table 5 as an example, Starting from median CoVaR and median CATFIN, a one standard deviation increase in BOTH CoVar and CATFIN leads to an increase in probability of covenant violation by 0.9%. That means, starting from median CoVaR and median CATFIN, the CoVaR effect dominates economically.

The results in Table 5 document that banks with higher systemic risk may increase loan portfolio risk and thus require higher covenant tightness, and also do so when aggregate systemic risk is high. This is consistent with the moral hazard hypothesis. However, the results also indicate that during periods of high aggregate systemic risk or economic downturn, the impact of systemic risk on internalization is weaker, which is consistent with systemic risk response hypothesis.

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All control variables have the expected signs and significance. The coefficient for ln (퐴푚표푢푛푡)푖,푡 is negative and significant. Loans with larger amounts is usually borrowed by larger firms which should be less risky. The coefficient for 푆푒푐푢푟푒푑푖,푡 is positive and significant, because secured loans are usually riskier. The coefficients for ln (푇표푡푎푙 푎푠푠푒푡푠)푗,푡−1 and 퐶푎푝푖푡푎푙 푅푎푡푖표푗,푡−1 suggest that bigger banks and banks with higher capital ratio requires lower loan covenants. Covenants are also looser when GDP growth rate is higher, and this is consistent with Zhang (2009).

However, although Eq. (1) indicates that moral hazard effect is stronger during periods of high systemic risk, it doesn’t show what type of banks are most subject to the moral hazard. The results from Eq. (1) can be driven by all banks or only by banks that have access to TBTF safety nets or subsidies. If the moral hazard hypothesis is true, then banks with the highest benefit from TBTF safety nests and subsidies should loosen their requirement on covenant tightness the most. Thus, I consider the following diff-in-diff estimation:

푃푣푖표퐿푖,푗,푡 = 훽0 + 훽1Δ퐶표푉푎푅푗,푡−1 + 훽2푇퐴푅푃푗,푡−1×푃푒푟푖표푑푡−1×Δ퐶표푉푎푅푗,푡−1 + 훽3푇퐴푅푃푗,푡−1 + 훽4푃푒푟푖표푑푡−1

+ 훽5ln (퐴푚표푢푛푡)푖,푡 + 훽6ln (푀푎푡푢푟푖푡푦)푖,푡 + 훽7ln (푁푢푚퐿푒푎푑푒푟푠)푖,푡 + 훽8푆푒푐푢푟푒푑푖,푡

+ +훽9ln (푆푖푧푒)푗,푡−1 + 훽10퐶푎푝푖푡푎푙푅푎푡푖표푗,푡−1 + 훽11푅푂퐸푗,푡−1 + 훽12퐺퐷푃푡−1

+ 휀푖,푗,푡 (2)

Where 푇퐴푅푃푖,푡−1 is either (i) a dummy variable with value 1 if the lead lender has any TARP CPP funds outstanding in period t-1 and 0 otherwise, or (ii) the outstanding TARP CPP fund amount for lender j in period t-1 scaled by total assets. 푃푒푟푖표푑푡−1 is one of the following aggregate economic condition variables: (i) a dummy variable with value 1 if CATFIN breaches the out-of-sample time consistent early warning point in period t-1 and 0 otherwise, called Warn, (ii) CATFIN in period t-1, or (iii) a dummy variable with value 1 if period t-1 is defined as recession by the National Bureau of Economic Research’s Business Cycle Dating Committee and 0 otherwise, called NBER. The idea is that if the moral hazard hypothesis holds, then during periods of high aggregate systemic risk or economic recession, TARP recipients, who benefit the most from

TBTF subsidy, should loosen covenant tightness (훽2<0) more than other banks who do not have access to TARP funds.

Table 6 presents the results from estimating Eq. (2). All interactions show negative and significant coefficients.

They indicate that the impact of Δ퐶표푉푎푅푗,푡−1 on covenant strictness is furthered weakened if the loan is originated during periods of high aggregate systemic risk (or economic downturn) and if the lender has any TARP funds outstanding in quarter t-1. The results are robustness to the choice of TARP recipient dummy or TARP fund outstanding amount.

4.1 Systemic Risk and All-in Drawn Spread

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I use the “All-in Drawn Spread” (AIS) as the measure of loan spread. The AIS is the amount the borrower pays in basis points over LIBOR or LIBOR equivalent plus any annual or facility fees paid to the lender. AIS is a more complete measure of the ongoing costs for the borrower and the income for the lender, and is used in the literature as a standard measure of loan spread. My loan spread analysis is made on facility level, because AIS is facility specific and within a deal, lenders may not participate in all facilities. Therefore, analyzing loan spread on the deal level cannot reveal the true relationship between loan spread and lenders’ systemic risk.

The pool regression:

퐴퐼푆푖,푗,푡 = 훼0 + 훼1Δ퐶표푉푎푅푗,푡−1 + 훼2퐶퐴푇퐹퐼푁푡−1 + 훼3Δ퐶표푉푎푅푗,푡−1×퐶퐴푇퐹퐼푁푡−1

+ 훼4ln (퐹푎푐푖푙푖푡푦 푎푚표푢푛푡)푖,푡 + 훼5ln (퐹푎푐푖푙푖푡푦 푚푎푡푢푟푖푡푦)푖,푡

+ 훼6ln (푁표. 표푓 푝푎푟푡푖푐푖푝푎푛푡푠)푖,푡 + 훼7푆푒푐푢푟푒푑푖,푡 + 훼8ln (푆푖푧푒)푗,푡−1 + 훼9퐶푎푝푖푡푎푙푅푎푡푖표푗,푡−1

+ 훼10푅푂퐸푗,푡−1 + 훼11퐺퐷푃푡−1

+ 휀푖,푗,푡 (3)

All independent variables have the same definitions as in the previous section. 퐴퐼푆푖,푗,푡 is the all-in drawn spreads for facility i, lender j, and facility was originated in quarter t.

Table 2 and Table 4 shows the summary statistics and spearman correlation matrix for all variables included in the regression above. As shown in the correlation matrix, 퐴퐼푆푖,푗,푡 is positively correlated with Δ퐶표푉푎푅푗,푡−1

(p-value<0.01%) with correlation coefficient of 0.0893, and positively correlated with 퐶퐴푇퐹퐼푁푡−1 (p- value<0.01%) with correlation coefficient of 0.1467.

Table 7 presents the results for the regression Eq (3). 퐴퐼푆푖,푗,푡 is positively and significantly associated with

Δ퐶표푉푎푅푗,푡−1 and CATFIN. Column I shows that on average, one standard deviation increase in Δ퐶표푉푎푅푗,푡−1 leads to an increase in loan spread by 28.9 basis point. Combining this result with the results from the OLS regression for covenant tightness, we see that banks with higher systemic risk increase their loan risk and ask for higher loan spreads and tighter financial covenants, which is consistent with moral hazard channel. CATFIN is positively significantly associates with all-in drawn spread. Combing this result with the results from the OLS regression for covenant tightness, we see that when macro-level systemic risk is higher, banks increase their loan risk and thus raise the loans spread and tighten loan covenants. Then, from the interaction term of Δ퐶표푉푎푅푗,푡−1 and CATFIN, higher macro-level systemic risk weakens the positive impact of

Δ퐶표푉푎푅푗,푡−1 on both loan spread and covenants.

The impact of systemic risks on loan risk may be different before, during and after the financial crisis. I thus divide my sample into pre-crisis periods, financial crisis period, and post crisis period, and run regressions separately. The results are shown in Table 8. Column I includes pre-crisis subsample, which spans 2003Q1 to

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2007Q3. Column II includes financial crisis subsample and includes observations between 2007Q4 and 2009Q2. Column III includes subsample between 2009Q3 and 2013Q2. From the three columns we see that

Δ퐶표푉푎푅푗,푡−1 has higher impact on loan spreads before financial crisis, and that impact is weaker during the financial crisis, and even weaker after the crisis. That indicates that the moral hazard effect is weaker for the systemic important banks during and after the financial crisis, which could be due to tougher regulations and stricter market discipline. However, CATFIN still significantly predicts loan risk before and after crisis, suggesting that moral hazard due to bailout expectation still exists after the financial crisis.

Then diff-in-diff regression:

퐴퐼푆푖,푗,푡 = 훽0 + 훽1Δ퐶표푉푎푅푗,푡−1 + 훽2푇퐴푅푃푗,푡−1×푃푒푟푖표푑푡−1×Δ퐶표푉푎푅푗,푡−1 + 훽3푇퐴푅푃푗,푡−1 + 훽4푃푒푟푖표푑푡−1

+ 훽5ln (퐹푎푐푖푙푖푡푦 푎푚표푢푛푡)푖,푡 + 훽6ln (퐹푎푐푖푙푖푡푦 푚푎푡푢푟푖푡푦)푖,푡 + 훽7ln (푁표. 표푓 푝푎푟푡푖푐푖푝푎푛푡푠)푖,푡

+ 훽8푆푒푐푢푟푒푑푖,푡 + 훽9ln (푆푖푧푒)푗,푡−1 + 훽10퐶푎푝푖푡푎푙푅푎푡푖표푗,푡−1 + 훽11푅푂퐸푗,푡−1 + 훽12퐺퐷푃푡−1

+ 휀푖,푗,푡 (4)

All independent variables have the same definitions as in the previous section. 퐴퐼푆푖,푗,푡 is the all-in drawn spreads for facility i, lender j, and facility was originated in quarter t. 훽2 measures how the impact of Δ퐶표푉푎푅푗,푡−1 on

퐴퐼푆푖,푗,푡 is affected if the lead bank is a TARP recipient and if the loans was originated in a period of high macro-level systemic risk or recession. If the moral hazard channel works, then 훽2 should be negative.

Table 9 shows the results for Eq (4). The coefficient for 퐴퐼푆푖,푗,푡 is still positive and significant (p-value<0.01), and coefficients for Warn and CATFIN are both positive and significant. However, banks with higher systemic risk will decease 퐴퐼푆푖,푗,푡 if the bank is a TARP recipient in quarter t-1, and quarter t-1 is a quarter of high systemic risk. The overall results indicate that banks generally require higher loan spread during periods of high macro-level systemic risk, but for those systemically important banks that were bailed out, the loan spreads they charge during systemic risky periods are much lower. Moral hazard effect is more apparent during non-recession and low systemic risk period, and systemic risk response effect emerges for those TARP recipients during systemic crisis.

Conclusion This paper investigates how banks’ systemic importance impact the characteristics of bank loan portfolios, and how that relationship is affected by bailout expectations proxied by TARP funds and macro-level systemic risk. Specifically, this paper test the moral hazard hypothesis and systemic risk response hypothesis. I find that covenant tightness and loan spread is positively associated with both microlevel and macrolevel systemic risk, suggesting that, i) systemic important banks increase their loan risk and thus require tighter loan covenant and loan spread, and ii) banks increase their loan risk during high aggregate systemic risk period. This is consistent with moral hazard hypothesis. However, during periods of recession or high macro-level systemic risk, covenant tightness and loan spread are less sensitive to lead banks’ micro-level systemic risk, especially if the lead bank is a TARP recipient. This shows a weaker moral hazard effect and stronger systemic risk response effect during high

15 aggregate systemic risk periods and recession periods. Higher bailout expectations during a high aggregate systemic risk periods or recession periods induce banks to decrease their loan risk to pull them bank from the brink of delinquency and bailout.

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Appendix

Variable Definitions Variable Definition Pviol A single measure of contract strictness for each contract, with 1 standing for tightest, and 0 for loosest. (Murfin, 2012; Demerjian, 2016) All-in-drawn spread Basis point spread over LIBOR plus the annual fee and the up-front fee spread, if there is any. Source: Dealscan. Δ퐶표푉푎푅 Micro-level systemic risk at 1% significance level. (Adrian and Brunnermeier, 2016) CATFIN Macro-level systemic risk. (Allen, Bali and Tang, 2012) Log (amount) Natural log of the deal size. Amount is in millions. Source: DealScan Log(maturity) Natural log of the maturity of the deal in months. Deal maturity is the weighted average of the facility maturities. Source: DealScan Log(No. of leaders) Natural log of the number of lead lenders in the deal syndicate. Source: DealScan Log (facility amount) Natural log of the facility size. Amount is in millions. Source: DealScan Log(facility maturity) Natural log of the maturity of the facility in months. Source: DealScan Log (No. of participants) Natural log of the number of participating lenders in the facility syndicate. Source: DealScan Secured An indicator variable that takes a value of one if the facility is secured. Source: DealScan Z-score Altman's Z-score for the borrower at the end of fiscal year prior to the current loan. Z-score is calculated as Z=1.2X1+1.4X2+3.3X3+0.6X4+0.99X5, where X1 is working capital/total assets, X2 is retained earnings/total assets, X3 is EBIT/total assets, X4 is market value of equity/book value of total liabilities, and X5 is sales/total assets (Altman, 1968). Log (total assets) Natural log of the total assets of the lender at the end of fiscal year prior to the current loan. Source: Y-9c Capital Ratio Total capital of the lender over total assets of the lender. Source: Y-9c Return on Equity Lenders return on book equity. Source: Y-9c GDP Quarterly GDP per capita growth rate. TARPDum An indicator variable that takes a value of one if the lender has any TARP fund remaining one quarter before loan (facility) origination, and zero otherwise. TARPAmt The remaining amount of TARP fund one period before loan (facility) origination. The amount if scaled by lender’s total assets. Warn An indicator variable that takes a value of one if CATFIN exceeds the early warning threshold of 35.1855%. NBER An indicator variable that takes a value of one if the quarter right before loan origination is a recession period. Source: National Bureau of Economic Research

Covenant standard definitions Dealscan covenant Standard definition Compustat implementation Min. Interest Coverage EBITDA/Interest Expense OIBDPQ/XINTQ Min. Cash Interest Coverage EBITDA/Interest Paid OIBDPQ/INTPNY Min. Fixed Charge Coverage EBITDA/(Interest Expense + Principal + Rent OIBDPQ/XINTQ + lag(DLCQ) + XRENT Expense) Min. Debt Service Coverage EBITDA/(Interest Expense + Principal) OIBDPQ/XINTQ + lag(DLCQ) Max. Debt-to-EBITDA Debt/EBITDA DLTTQ + DLCQ/OIBDPQ Max. Senior Debt-to-EBITDA Senior Debt/EBITDA DLTTQ + DLCQ – DS/OIBDPQ

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Max. Leverage Debt/Assets DLTTQ + DLCQ/ATQ Max. Senior Leverage Senior Debt/Assets DLTTQ + DLCQ – DS/ATQ Max. Debt-to-Tangible Net Worth Debt/TNW DLTTQ + DLCQ/ATQ – INTANQ – LTQ Max. Debt-to-Equity Debt/NW DLTTQ + DLCQ /ATQ–LTQ Min. Current Ratio Current Assets/Current Liabilities ACTQ/LCTQ Min. Quick Ratio Account Receivable + Cash and RECTQ + CHEQ/LCTQ Equivalents/Current Liabilities Min. EBITDA EBITDA OIBDPQ Min. Net Worth NW ATQ –LTQ Min. Tangible Net Worth TNW ATQ –INTANQ +LTQ

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Table 1: Covenant Types and Frequency This presents summary statistics on all types of financial covenants in Dealscan. “Frequency” is the number of times the covenant is used in Dealscan loans. “Percentage” is the frequency of each covenant type divided by the total observations of covenants.

Covenant Type Frequency Percentage Max. Debt to EBITDA 10282 22% Min. Interest Coverage 8621 18% Min. Fixed Charge Coverage 7390 15% Max. Leverage ratio 4144 9% Max. Debt to Tangible Net Worth 2830 6% Min. Current Ratio 2677 6% Min. Debt Service Coverage 2291 5% Max. Senior Debt to EBITDA 1801 4% Min. Quick Ratio 689 1% Max. Debt to Equity 328 1% Min. Cash Interest Coverage 308 1% Max. Senior Leverage 132 0% Min. EBITDA 1772 4% Max. Capex 4337 9% Max. Loan to Value 90 0% Min. Equity to Asset Ratio 8 0% Max. Total Debt to Tangible Net Worth 6 0% Max. Net Debt to Assets 2 0% Min. Net Worth to Total Asset 2 0% Other Ratio 32 0% Sum 47742 100%

Table 2: Summary statistics This table presents summary statistics for the variables used in hypothesis test regressions and other variables of interest. Variable Mean P50 Std. Dev. Deal Characteristics Covenant tightness 38.8600 13.6000 41.9755 Deal amount (millions) 780.1725 400.0000 1327.9936 Deal maturity (months) 47.9735 56.8113 20.1231 Number of lead lenders 4.2611 4.0000 2.6882 Secured 0.5314 1.0000 0.4990 Facility Characteristics All-in-Drawn spread (basis point) 192.1286 175.0000 131.3500 Facility amount (millions) 528.8130 250.0000 953.7019 Facility maturity (months) 50.7753 60.0000 23.1407 Number of participants 12.6597 10.0000 11.2513 Facility Secured 0.6315 1.0000 0.4824 Lender Characteristics Delta CoVaR 1.3270 1.1431 0.7389 Total assets (billions) 725.7935 369.6450 712.3386 Capital ratio 0.0840 0.0830 0.0158 Return on equity 0.0785 0.0721 0.0516 Macroeconomic Conditions CATFIN 2.4454 2.3009 1.0391 GDP growth 2.7729 2.8900 1.5648

21 Table 3: Summary Statistics: Package level This table reports univariate spearman correlations for the variables used in covenant tightness and systemic risks regressions. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (1) Covenant tightness 1.0000 (2) L1.Delta CoVaR 0.0784* 1.0000 (3) L1.CATFIN 0.0604* 0.3981* 1.0000 (4) Deal amount -0.2599* -0.0877* -0.0450* 1.0000 (5) Deal maturity -0.0285* -0.1535* -0.2072* 0.2047* 1.0000 (6) Number of lead lenders -0.1764* -0.0299* -0.0580* 0.6321* 0.1721* 1.0000 (7) Secured 0.3804* 0.0562* 0.0331* -0.2807* 0.1630* -0.2442* 1.0000 (8) L1.Total assets -0.1636* -0.2343* -0.0175 0.2431* 0.1203* 0.0338* -0.0877* 1.0000 (9) L1.Capital ratio -0.0990* 0.0238 -0.0688* 0.0102 0.1274* 0.0335* 0.0073 0.0875* 1.0000 (10) L1.Return on equity 0.0757* 0.0595* -0.0640* -0.0790* -0.0215 -0.0072 0.0329* -0.2815* -0.2335* 1.0000 (11) L1.GDP growth 0.0747* 0.0023 -0.0931* -0.0530* 0.0416* 0.0061 0.0130 -0.3754* -0.3370* 0.2957* 1.0000

Table 4: Summary Statistics: Facility level This table reports univariate spearman correlations for the variables used in loan spreads and systemic risks regressions. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

22 (1) All-in-Drawn spreads 1.0000 (2) Covenant tightness 0.3749* 1.0000 (3) L1.Delta CoVaR 0.0893* 0.0776* 1.0000 (4) L1.CATFIN 0.1467* 0.0583* 0.2835* 1.0000 (5) Facility amount -0.3866* -0.2869* -0.0719* -0.0726* 1.0000 (6) Facility maturity 0.1663* -0.0074 -0.1076* -0.1409* 0.0983* 1.0000 (7) Number of participants -0.4085* -0.2008* -0.0152* -0.0403* 0.6420* 0.0958* 1.0000 (8) Facility Secured 0.6365* 0.3529* 0.0127 0.0149* -0.3721* 0.2485* -0.3172* 1.0000 (9) L1.Total assets 0.0794* -0.1452* -0.2054* -0.0832* 0.2947* 0.1010* 0.0546* -0.0216* 1.0000 (10) L1.Capital ratio 0.0889* -0.1149* 0.0204* -0.1301* 0.0793* 0.0379* -0.0385* 0.0054 0.1908* 1.0000 (11) L1.Return on equity -0.1222* 0.0816* 0.0058 -0.0515* -0.0905* -0.0161* 0.0016 -0.0161* -0.2650* -0.2184* 1.0000 (12) L1.GDP growth -0.1660* 0.0841* 0.0060 -0.0707* -0.1006* 0.0307* 0.0703* -0.0320* -0.3878* -0.3389* 0.3236* 1.0000 Figure 1. Monthly Loan risk and CATFIN. The first figure above shows the relationship between simple averaged covenant tight- ness of all deals originated in each month and monthly CATFIN. Covenant tightness is scaled 0 to 10. CATFIN is lagged by one quarter. Each dot is for each month. The blue dots are monthly average covenant tightness, and the red dots are the monthly CATFIN The second figure shows the relationship between simple averaged all-in-drawn spreads of all facilities originated in each month and monthly CATFIN. Average all-in-drawn spreads are in percentage. The blue dots are monthly average loan spreads and the red dots are CATFIN.

23 Table 5: OLS: Covenant Strictness and Systemic Risks I II III IV V VI L.CoVaR 2.607∗∗∗ 2.688∗∗∗ 5.674∗∗∗ 3.229∗∗∗ 3.716∗∗∗ (0.621) (0.751) (1.284) (0.684) (0.740)

L.CATFIN 0.502∗∗ -0.0528 0.983∗∗ (0.228) (0.276) (0.454)

L.CoVaR_L.CATFIN -0.827∗∗∗ (0.289)

L.CoVaR_L.NBER -2.656∗∗∗ (0.992)

L.NBER 4.357∗∗ (1.850)

CoVaR_Warn -1.508∗ (0.834)

L.Warn 0.830 (1.503) ln(Deal Amount) -3.311∗∗∗ -3.343∗∗∗ -3.306∗∗∗ -3.343∗∗∗ -3.329∗∗∗ -3.333∗∗∗ (0.375) (0.376) (0.376) (0.376) (0.375) (0.376) ln(Maturity) -0.0505 -0.213 -0.0564 0.0323 0.0316 -0.0150 (0.484) (0.483) (0.485) (0.486) (0.485) (0.485) ln(Number of Leads) 0.329 0.420 0.325 0.325 0.334 0.336 (0.502) (0.501) (0.502) (0.502) (0.501) (0.501)

Secured 10.27∗∗∗ 10.34∗∗∗ 10.27∗∗∗ 10.24∗∗∗ 10.25∗∗∗ 10.27∗∗∗ (0.724) (0.724) (0.724) (0.724) (0.724) (0.724)

L.ln(Total Assets) -1.858∗∗∗ -1.861∗∗∗ -1.859∗∗∗ -1.806∗∗∗ -1.894∗∗∗ -1.837∗∗∗ (0.535) (0.535) (0.535) (0.535) (0.538) (0.535)

L.Capital Ratio -102.1∗∗∗ -106.8∗∗∗ -102.3∗∗∗ -100.3∗∗∗ -98.91∗∗∗ -102.5∗∗∗ (21.032) (21.028) (21.060) (21.068) (21.296) (21.047)

L.Return on Equity -1.110 -1.450 -1.145 -1.133 -0.675 -1.384 (4.671) (4.675) (4.674) (4.674) (4.681) (4.671)

L.GDP Growth -0.736∗∗∗ -0.864∗∗∗ -0.739∗∗∗ -0.730∗∗∗ -0.776∗∗∗ -0.768∗∗∗ (0.166) (0.163) (0.167) (0.167) (0.191) (0.167) Observations 24295 24295 24295 24295 24295 24295 R2 0.666 0.666 0.666 0.666 0.666 0.666 Adjusted R2 0.586 0.585 0.586 0.586 0.586 0.586 Borrower fixed effects Yes Yes Yes Yes Yes Yes Lender fixed effects Yes Yes Yes Yes Yes Yes Standard errors in parentheses p 0.10, p 0.05, p 0.01 ∗ < ∗∗ < ∗∗∗ <

24 Table 6: Diff-in-Diff: Covenant Strictness, TARP and Systemic Risks I II III IV V VI L.CoVaR 3.585∗∗∗ 3.124∗∗∗ 3.787∗∗∗ 3.354∗∗∗ 3.044∗∗∗ 3.491∗∗∗ (0.679) (0.692) (0.702) (0.652) (0.674) (0.682) TARPDum_Warn_CoVaR -3.368∗∗∗ (0.917) TARPDum_CATFIN_CoVaR -0.325∗∗ (0.138) TARPDum_NBER_CoVaR -3.583∗∗∗ (0.785) TARPAmt_Warn_CoVaR -1.787∗∗∗ (0.472) TARPAmt_CATFIN_CoVaR -0.195∗∗ (0.076) TARPAmt_NBER_CoVaR -1.793∗∗∗ (0.408) L.TARPDum 2.305 -0.0421 0.828 (1.885) (1.738) (1.549) L.TARPAmt 1.437 0.446 0.409 (0.931) (0.888) (0.732) L.Warn -0.162 -0.147 (0.543) (0.541) L.CATFIN 0.0176 -0.0805 -0.00642 -0.0882 (0.260) (0.263) (0.258) (0.261) L.NBER 0.632 0.549 (0.943) (0.942) ln(Deal Amount) -3.240∗∗∗ -3.213∗∗∗ -3.220∗∗∗ -3.234∗∗∗ -3.207∗∗∗ -3.208∗∗∗ (0.369) (0.370) (0.370) (0.369) (0.370) (0.370) ln(Maturity) -0.106 -0.157 -0.0781 -0.119 -0.162 -0.108 (0.464) (0.464) (0.464) (0.463) (0.464) (0.464) ln(Number of Leads) 0.283 0.279 0.243 0.289 0.283 0.249 (0.500) (0.501) (0.501) (0.500) (0.501) (0.501) Secured 10.27∗∗∗ 10.28∗∗∗ 10.27∗∗∗ 10.27∗∗∗ 10.27∗∗∗ 10.26∗∗∗ (0.724) (0.725) (0.724) (0.725) (0.725) (0.724) L.ln(Total Assets) -1.941∗∗∗ -1.937∗∗∗ -1.881∗∗∗ -1.938∗∗∗ -1.939∗∗∗ -1.895∗∗∗ (0.533) (0.533) (0.535) (0.534) (0.534) (0.536) L.Capital Ratio -100.9∗∗∗ -99.53∗∗∗ -103.0∗∗∗ -103.8∗∗∗ -101.4∗∗∗ -105.6∗∗∗ (20.809) (20.838) (21.045) (20.945) (20.960) (21.174) L.Return on Equity -0.975 -1.256 -1.128 -1.239 -1.450 -1.448 (4.332) (4.368) (4.372) (4.332) (4.368) (4.373) L.GDP Growth -0.969∗∗∗ -0.901∗∗∗ -0.920∗∗∗ -0.936∗∗∗ -0.871∗∗∗ -0.899∗∗∗ (0.177) (0.176) (0.193) (0.173) (0.172) (0.189) Observations 24295 24295 24295 24295 24295 24295 R2 0.666 0.666 0.666 0.666 0.666 0.666 Adjusted R2 0.586 0.586 0.586 0.586 0.586 0.586 Borrower fixed effects Yes Yes Yes Yes Yes Yes Lender fixed effects Yes Yes Yes Yes Yes Yes Standard errors in parentheses p 0.10, p 0.05, p 0.01 ∗ < ∗∗ < ∗∗∗ <

25 Table 7: OLS: All-in-Drawn Spreads and Systemic Risks I II III IV V VI VII L.CoVaR 39.16∗∗∗ 18.05∗∗∗ 36.44∗∗∗ 43.99∗∗∗ 36.94∗∗∗ 16.61∗∗ (7.681) (6.360) (11.390) (8.633) (7.772) (7.485)

L.CATFIN 17.74∗∗∗ 14.19∗∗∗ 21.16∗∗∗ 12.79∗∗∗ (3.967) (4.226) (6.283) (4.095)

L.CoVaR_L.CATFIN -5.409∗∗ (2.400)

L.CoVaR_L.NBER -1.866 (6.461)

L.NBER -19.68 (13.312)

L.CoVaR_L.Warn -13.00∗∗ (6.133)

L.Warn 37.96∗∗ (16.799)

L.CoVaR_L.TARP -10.94 (8.825)

L.TARP 69.02∗∗∗ (14.676) ln(Facility Amount) -10.97∗∗∗ -10.85∗∗∗ -10.74∗∗∗ -10.75∗∗∗ -10.94∗∗∗ -10.89∗∗∗ -10.75∗∗∗ (1.408) (1.410) (1.398) (1.395) (1.402) (1.411) (1.431) ln(Facility Maturity) -13.55∗∗∗ -13.43∗∗∗ -12.79∗∗∗ -12.70∗∗∗ -13.82∗∗∗ -13.12∗∗∗ -11.94∗∗∗ (3.506) (3.649) (3.531) (3.515) (3.468) (3.520) (3.443) ln(No. of Participants) -12.59∗∗∗ -12.25∗∗∗ -12.40∗∗∗ -12.44∗∗∗ -12.59∗∗∗ -12.56∗∗∗ -12.65∗∗∗ (2.616) (2.599) (2.613) (2.597) (2.597) (2.622) (2.667)

Secured 64.92∗∗∗ 64.43∗∗∗ 64.43∗∗∗ 64.40∗∗∗ 64.68∗∗∗ 64.67∗∗∗ 64.53∗∗∗ (3.948) (3.880) (3.887) (3.891) (3.945) (3.937) (3.862)

Covenant Dummy -19.00∗∗∗ -15.43∗∗∗ -16.86∗∗∗ -16.95∗∗∗ -18.60∗∗∗ -18.68∗∗∗ -17.38∗∗∗ (3.217) (3.180) (2.948) (2.894) (3.112) (3.121) (3.012)

L.ln(Total Assets) 23.90∗∗∗ 26.98∗∗∗ 25.56∗∗∗ 25.94∗∗∗ 23.55∗∗∗ 24.48∗∗∗ 26.99∗∗∗ (5.180) (4.542) (4.815) (4.755) (5.066) (5.105) (5.322)

L.Capital Ratio 252.7 283.5 316.4 319.8∗ 190.3 282.4 222.5 (163.740) (191.352) (186.500) (185.598) (165.362) (169.947) (184.797)

L.Return on Equity -89.77∗∗ -80.18∗∗ -79.02∗∗ -78.02∗∗ -96.41∗∗ -86.73∗∗ -70.37∗∗ (36.355) (32.227) (31.600) (30.495) (35.720) (34.712) (29.535)

L.GDP Growth -11.91∗∗∗ -11.32∗∗∗ -10.72∗∗∗ -10.74∗∗∗ -13.55∗∗∗ -11.36∗∗∗ -8.924∗∗∗ (2.909) (2.944) (2.796) (2.826) (3.038) (2.902) (2.558) Observations 65112 65112 65112 65112 65112 65112 65112 R2 0.713 0.717 0.718 0.718 0.714 0.714 0.720 Adjusted R2 0.681 0.685 0.686 0.687 0.682 0.683 0.689 Borrower fixed effects Yes Yes Yes Yes Yes Yes Yes Lender fixed effects Yes Yes Yes Yes Yes Yes Yes Loan type dummy Yes Yes Yes Yes Yes Yes Yes Loan purpose dummy Yes Yes Yes Yes Yes Yes Yes Standard errors in parentheses p 0.10, p 0.05, p 0.01 ∗ < ∗∗ < ∗∗∗ <

26 The table below shows the relationship between all-in-drawn spreads and systemic risks during pre-crisis period, financial crisis period, and post-crisis period. Observations are at the facility level. Column I is from 2003Q1 to 2007Q3. Column II is from 2007Q4 to 2009Q2. Column III is from 2009Q3 to 2013Q2.

Table 8: All-in-Drawn Spreads and Systemic Risks: Pre-crisis, During Crisis and Post-crisis I II III L.CoVaR 59.31∗∗∗ 40.10∗ 11.77∗∗ (8.089) (21.862) (5.945)

L.CATFIN 14.19∗∗∗ 10.95 18.97∗∗∗ (3.241) (8.201) (2.448)

L.CoVaR_L.CATFIN -11.56∗∗∗ -4.065 -1.512 (2.921) (3.160) (1.620)

ln(Facility Amount) -9.905∗∗∗ -4.352∗ -5.897∗∗∗ (0.802) (2.533) (0.962)

ln(Facility Maturity) 5.993∗∗∗ 16.87∗∗∗ 3.505 (1.980) (6.062) (2.999)

ln(No. of Participants) -9.869∗∗∗ -33.90∗∗∗ -16.66∗∗∗ (1.236) (6.596) (1.926)

Secured 31.19∗∗∗ -39.23∗∗∗ -1.029 (2.632) (12.240) (5.053)

Covenant Dummy -2.042 -41.28∗∗∗ -21.68∗∗∗ (2.260) (13.955) (3.272)

L.ln(Total Assets) -99.87∗∗∗ -22.42 -74.81∗∗∗ (4.961) (18.326) (14.875)

L.Capital Ratio 249.0∗∗∗ -655.4 -756.5∗∗∗ (74.246) (431.067) (129.167)

L.Return on Equity -16.84 -6.003 -110.5∗∗∗ (15.064) (81.405) (29.294)

L.GDP Growth -2.776∗∗∗ -42.10∗∗∗ -9.133∗∗∗ (0.872) (4.735) (0.651) Observations 18361 2702 12321 R2 0.787 0.873 0.814 Adjusted R2 0.751 0.828 0.781 Borrower fixed effects Yes Yes Yes Lender fixed effects Yes Yes Yes Loan type dummy Yes Yes Yes Loan purpose dummy Yes Yes Yes Standard errors in parentheses p 0.10, p 0.05, p 0.01 ∗ < ∗∗ < ∗∗∗ <

27 Table 9: Diff-in-Diff: All-in-Drawn Spread, TARP and Systemic Risks I II III IV V VI L.CoVaR 29.42∗∗∗ 17.07∗∗∗ 9.134∗∗∗ 28.08∗∗∗ 14.91∗∗∗ 8.476∗∗∗ (2.994) (3.252) (3.384) (2.776) (3.013) (3.106) TARPDum_Warn_CoVaR -15.07∗∗∗ (2.734) TARPDum_CATFIN_CoVaR -2.895∗∗∗ (0.506) TARPDum_NBER_CoVaR -3.381 (2.910) TARPAmt_Warn_CoVaR -7.180∗∗∗ (1.547) TARPAmt_CATFIN_CoVaR -1.496∗∗∗ (0.297) TARPAmt_NBER_CoVaR -0.614 (1.586) L.TARPDum 56.39∗∗∗ 52.52∗∗∗ 38.73∗∗∗ (4.536) (5.008) (4.504) L.TARPAmt 27.16∗∗∗ 26.17∗∗∗ 17.02∗∗∗ (2.533) (2.915) (2.452) L.Warn 33.42∗∗∗ 33.52∗∗∗ (3.033) (3.040) L.CATFIN 21.52∗∗∗ 21.65∗∗∗ 21.92∗∗∗ 22.12∗∗∗ (1.133) (1.171) (1.132) (1.164) L.NBER -5.123 -4.954 (4.959) (4.901) GDP Growth -6.830∗∗∗ -6.077∗∗∗ -5.919∗∗∗ -7.881∗∗∗ -6.861∗∗∗ -6.794∗∗∗ (0.820) (0.812) (0.882) (0.781) (0.775) (0.852) ln(Facility Amount) -8.480∗∗∗ -8.171∗∗∗ -8.322∗∗∗ -8.567∗∗∗ -8.244∗∗∗ -8.354∗∗∗ (1.088) (1.076) (1.077) (1.090) (1.077) (1.078) ln(Facility Maturity) 6.626∗∗ 6.783∗∗ 6.499∗∗ 6.412∗∗ 6.680∗∗ 6.340∗∗ (2.844) (2.811) (2.821) (2.849) (2.815) (2.825) ln(No. of Participants) -27.46∗∗∗ -27.05∗∗∗ -27.00∗∗∗ -27.33∗∗∗ -26.96∗∗∗ -26.84∗∗∗ (1.867) (1.846) (1.848) (1.870) (1.848) (1.850) Secured 1.646 -0.541 -0.382 1.747 -0.606 -0.334 (4.921) (4.867) (4.877) (4.929) (4.873) (4.882) L.ln(Total Assets) 117.6∗∗∗ 110.4∗∗∗ 110.2∗∗∗ 120.5∗∗∗ 112.6∗∗∗ 112.4∗∗∗ (6.385) (6.314) (6.522) (6.398) (6.325) (6.532) L.Capital Ratio 897.8∗∗∗ 1130.7∗∗∗ 1122.1∗∗∗ 899.8∗∗∗ 1123.2∗∗∗ 1159.2∗∗∗ (106.107) (106.137) (109.500) (106.643) (106.830) (110.156) L.Return on Equity -122.9∗∗∗ -86.98∗∗∗ -96.60∗∗∗ -114.9∗∗∗ -81.29∗∗∗ -88.42∗∗∗ (21.467) (21.350) (21.835) (21.502) (21.350) (21.757) Observations 13199 13199 13199 13199 13199 13199 R2 0.758 0.764 0.763 0.757 0.763 0.763 Adjusted R2 0.710 0.716 0.715 0.709 0.715 0.715 Borrower fixed effects Yes Yes Yes Yes Yes Yes Lender fixed effects Yes Yes Yes Yes Yes Yes Loan type dummy Yes Yes Yes Yes Yes Yes Loan purpose dummy Yes Yes Yes Yes Yes Yes Standard errors in parentheses p 0.10, p 0.05, p 0.01 ∗ < ∗∗ < ∗∗∗ <

28