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Granja, Joao , Gregor Matvos and Amit Seru, 2017. “Selling Failed Banks,” Journal of Finance, 72(4): 1723-1784.
N OTES : The dependent variable is 100 ∗ (1 − λ), that is, the bargaining power of the borrowers. A unit of observation is a day; therefore balance sheet variables and risk measures are averages of borrowers on each day. Percentage of core violations (av) is defined as the percentage of transactions that are violations of the core. “Actual LVTS cash balances” is the actual amount of liquidity in the payments system (in 100 million CAD); high balances means more central bank liquidity injections. The 1 month CDOR-OIS spread is the difference between the Canadian Dealer Offered Rate and one month Overnight Indexed Swap rate, where the former is the rate surveyed banks are willing to lend to other banks for one month and the latter is an over-the-counter agreement to swap, for one month, a fixed interest rate for a floating rate. I(Term PRA allocation at t − 1 > 0) is an indicator variable for whether or not a borrower accessed short-term liquidity in the Bank of Canada repo auctions, which became available in 2007; I(IMPP allocation at t − 1 > 0) is an indicator variable for whether or not a borrower accessed the Canadian government mortgage liquidity program, which became available in 2008. Distance to default is based on Merton’s 1974 model. A firm is considered in default if its value falls below its debt. High values of distance to default imply a bank is less likely to default. Wholesale funding is defined as fixed term and demand deposits by deposit-taking institutions plus banker acceptances plus repos. Wholesale funding as a fraction of assets is a banks’ exposure to risky short-term funding. CDS is a borrowers’ credit default swap spread—higher spreads indicate higher risk of default. All specifications include borrower fixed effects. Standard errors are in parentheses and are clustered at the borrower level. a p < 0.01, b p < 0.05, c p < 0.1.
transaction interest rate, loan size, and the amortization period (60 months) specified in borrower i’s contract. To construct negotiated discounts, we must first identify the posted rate valid at the time of negotiation. Since our contract data include only the closing date, to pin down the appropriate posted rate we infer the negotiation week that maximizes the aggregate fraction of con- sumers paying the posted rate (or 33 days prior to closing). Lastly, the loyalty variable is a dummy variable equal to one if a consumer has prior experience dealing with the chosen lender. Since 75% consumers are new home buyers, this most likely identifies the bank with which the borrower possess a savings or checking account. Note that this variable is not available for one lender, and we therefore treat the loyalty outcome as partly missing when constructing the likelihood function Finally, since the main dataset does not provide direct information on the number of quotes gathered by borrowers, we supplement it with survey evidence from the Altus Group (FIRM survey). The survey asks 841 people who purchased a house during our sample period about their shopping habits. We use the aggregate results of this survey to construct auxiliary moments characterizing the fraction of consumers who report searching for more than one lender, by demographic groups. We focus in particular on city size, regions, and income groups.
To perform our counterfactuals we need some identiication assumptions. The empirical model identiied only the ratio F /(c B − c A ). Based on our external estimate, we assume that F = €2,300, implying that c B − c A is about €0.60. The results were similar for higher values of ixed costs with correspondingly higher variable cost savings. The empirical model also does not identify the travel cost per unit of distance k (in €/km) from the price parameter a. In our welfare analysis, a high assumed value of k implies a high weight to consumer surplus relative to producer surplus. We therefore use two alternative values k = €0.10 and k = €0.25. The higher value is commonly used by companies and tax authorities to reimburse travel costs. The lower value roughly cor- responds to Gowrisankaran and Krainer’s (2007) estimate of ATM travel costs (using a different model and data ). We focus our discussion on the results for k = €0.25 and present the results for k = €0.10 as a robustness check in Table A3 of Web Appendix B.
JEL Classification: D44, E58, G01
Starting with Rochet and Tirole (1996) and Allen and Gale (2000), the importance of the intercon- nectivity structure of financial institutions for the stability of the financial system has been seen as a topic of first-order importance. Additional theoretical research by Freixas, Parigi and Ro- chet (2000), Eisenberg and Noe (2001), Elliot, Golub and Jackson (2014), Glasserman and Young (2015) and A¸cemoglu, Ozdaglar and Tahbaz-Salehi (2015) among others, has shown that certain network structures can lead to the presence of “systemic” institutions, i.e. institutions which, due to their connectedness, can transmit adverse shocks to the entire system. The empirical character- ization of the actual interconnectivity structure of banks, however, is relatively scarce. This delay is undoubtedly related to the lack of direct data on interbank credit relations, save for informa- tion available through intraday payment and settlement systems (see, for example, Boss, Elsinger, Summer and Thurner (2004), Bech and Atalay (2008), Iori, Masi, Precup, Gabbi and Caldarelli (2008), Wetherilt, Zimmerman and Soram¨ aki (2010) and Ashcraft, McAndrews and Skeie (2011)). In this paper, we attempt to provide empirical tools that would allow Central Banks or poten- tially other regulators to infer the structure of the network of spillover effects based on comovements of banks’ short-term funding costs. Such comovements can be generated as the result of the prop- agation of solvency shocks as modeled in Eisenberg and Noe (2001), and A¸cemoglu et al. (2015). Once we estimate the financial network, we use centrality measures typically used in network analy- sis (e.g., Ballester, Calv´ o-Armengol and Zenou (2006)) to estimate the extent to which a particular bank is crucial for the working of the whole system, i.e., to determine the set of systemic banks. Our method provides a measure of such “systemicness” as expressed by that bank’s impact on the future cost of funding of other banks. Similarly, we measure to what extent each bank is affected by shocks to the funding costs of other banks in the system, in other words, how vulnerable to contagion each bank is.
(measured by a Herfindahl index) also contributes positively to the value of the merger. On the other hand, we find little evidence that mergers were motivated by high (or low) performing target banks. Consistent with an efficiency rationale for value creation, we find that merger value is greater when there is a greater overlap between acquirer and target markets, and that these gains are greater for mergers between banks regulated by the same agency before the merger. These effects likely represent efficiencies rather than market power because we also control for market concentration in the target’s markets in these specifications. The magnitude of these efficiency effects on merger value are sensible, amounting to nearly the annual administrative cost of operating a single bank branch ( Radecki et al. 1996 ). These efficiencies may arise from the ability of the combined bank to pool fixed operating expenses such as advertising and ATM networks across the acquirer and target banks. 3
θ i and weakly decreasing in q. Horta¸csu and Kastl (2012) provide a formal method to test
for the null hypothesis of private values in the Bank of Canada’s three-month treasury-bill auctions, and do not reject private values in that application. Their test method relies on the specific institutional setup in the Canadian treasury market, which is not present here. We do however argue that their results provide support for our assumption of independent private values in the context of government debt auctions. It can reasonably be argued that banks have idiosyncratic shocks to their liquidity needs due to deposit flows and the corresponding reserve requirements. The assumption we impose in our empirical work is that these shocks are independent conditional on observed macro and secondary market conditions. In the sequel we drop the index i, as there is no danger of confusion
The irst instruments we use are based on the net amount of charged-off loans by a bank. Loan charge-offs measure the net value of loans and leases that were removed from the bank’s balance sheet because of uncollectibility, and are one mea- sure of the performance of a bank’s loan portfolio. We include bank ixed effects in the speciication, so our results are not driven by the fact that banks which give bad loans also offer poor services that make them unattractive to depositors. Instead, our instrument is identiied from changes in loan charge-offs within a bank over time. We use two types of charge-offs: those for all loans, and charge-offs for real estate secured loans. Quarter ixed effects absorb any aggregate activity, which would affect loan performance, and would also change depositors’ preferences. Moreover, loans that are written off in a given period have been made in the past, so we exploit the variation in the quality of the loans the bank has made in the past to generate variation in inancial distress in the present. Therefore, the possibility is small that changes in loan charge-offs within a bank over time would measure the services that depositors obtain from a bank.
In each period, the first part will be lecture given by the instructor, and the rest of the time will be used for practicing with the computer. The student will sometimes be asked to answer questions raised by the instructor, to discuss a topic, or to give a presentation.
5. Method of Assessment
c. A best-seller titled Retire Rich convinces the public to increase the percentage of their income devoted to saving MPC↓ the IS curve becomes steeper Y↓, C↓, r↓. The central bank’s response to keep Y unchanged: increases money supply, shifting the LM curve to the right ( draw the graph to confirm this).