“In the Short Run Blasé, In the Long Run Risqué

On the Effects of Monetary Policy on Bank Credit Risk-Taking in the Short Versus Long Run

Abstract

We identify the impact of short-term interest rates on credit risk-taking in the short and long run by analyzing a comprehensive credit register from Spain, a country where for the last twenty years monetary policy was mostly decided abroad. Duration analyses show that lower overnight rates prior to loan origination lead banks to lend more to borrowers with a worse credit history and to grant more loans with a higher per-period probability of default. Lower overnight rates during the life of the loan reduce this probability. Bank, borrower and market characteristics determine the impact of overnight rates on credit risk-taking.

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Notes

  1. 1.

    In that paper we employ a two-stage model that analyzes the granting of loan applications in the first stage and loan outcomes for the applications granted in the second stage. This setup makes it harder to draw the inferences derived in this paper. On the risk-taking channel of monetary policy see e. g. for the U.S. (Delis et al. 2011; Paligorova and Santos 2013; Altunbas et al. 2014; Buch et al. 2014a, 2014b; Dell’Ariccia et al. 2016), Austria (Gaggl and Valderrama 2010), Colombia (López et al. 2011, 2012), the Czech Republic (Geršl et al. 2015), and Sweden (Apel and Claussen 2012). Lower real interest rates preceded banking crises in 47 countries (von Hagen and Ho 2007).

  2. 2.

    An exception is Ioannidou et al. (2015) who analyze the risk-pricing by banks in Bolivia during the period 1999 to 2003. They find that when the U.S. federal funds rate decreases, bank credit risk increases while loan spreads drop and that increases of the funds rate during the life of the loan has the opposite effect. Expansionary monetary policy and credit risk-taking followed by restrictive monetary policy possibly led to the financial crisis during the 1990s in Japan (see Allen and Gale 2004 for example).

  3. 3.

    Lower interest rates may reduce the threat of deposit withdrawals (Diamond and Rajan 2006), abate adverse selection problems in credit markets (Dell’Ariccia and Marquez 2006) or improve banks’ net worth (Stiglitz and Greenwald 2003), for example, allowing banks to relax their lending standards and to increase their credit risk-taking. Low levels of short-term interest rates may further make riskless assets less attractive for financial institutions and lead to a search-for-yield (Rajan 2006; Blanchard 2008). On the other hand, higher interest rates increase the opportunity costs for banks to hold cash thus making risky alternatives more attractive (Smith 2002), or may reduce the banks’ net worth or charter value enough to make a “gambling for resurrection” strategy attractive (Kane 1989; Hellman et al. 2000), thus making the impact of short-term interest rates on credit risk-taking ultimately a critical empirical question.

  4. 4.

    Banks may not only arise to overcome key informational and contractual problems (as in Diamond 1984) to lend to a potentially wide range of intermediately opaque firms in modern theoretical work (as in Diamond 1991; Bolton and Freixas 2000), but banks also haven been and still are the main providers of credit in most economies (Source: International Finance Statistics of the International Monetary Fund). Credit risk may be the most important risk type banks face (Kuritzkes and Schuermann 2010).

  5. 5.

    The Credit Register has been employed in, e. g., Jiménez and Saurina (2004) and Jiménez et al. (2006, 2012, 2014b, 2016).

  6. 6.

    In Bernanke and Blinder (1992) and Christiano et al. (1996), among others, the overnight interest rate is an indicator for the stance of monetary policy. The ECB targets the overnight rate as a measure of the stance of its monetary policy.

  7. 7.

    The credit channel comprises both a balance sheet and a bank-lending channel. The latter channel can also be viewed as a balance sheet channel for banks (see Bernanke 2007 for a recent review of this literature).

  8. 8.

    Den Haan et al. (2007) find that monetary policy differently affects consumer, real estate, and business lending by banks. In fact, contractive monetary policy does not decrease the volume of business loans for example. Their findings may be caused by a decline in bank risk-taking when short-term interest rates are high.

  9. 9.

    In Diamond and Rajan (2006) an entrepreneur gets either an early or a late payoff that is fixed (and known). Introducing uncertainty about the payoff level will influence the amount lent to the entrepreneur, we conjecture, without altering the main results of the model. In consequence, monetary policy may affect both the level of liquidity and credit risk taken by the banks.

  10. 10.

    A decline in the cost of funds in Sengupta (2014) likewise facilitates entry of outside banks into “high-risk” credit markets, as inclusion of non-credit worthy borrowers in their loan portfolio becomes possible. And in Ruckes (2004) improvements in economic outlook and declines in the average default probability of the borrowers lowers the lenders’ screening activity, intensifies price competition and boosts lending to low quality borrowers.

  11. 11.

    Because of equity rationing, the shocks to the banks’ net worth may not be immediately reversible, explaining their potentially large adverse macro-economic consequences. Lower interest rates may also reduce moral hazard and adverse selection problems in credit markets, thereby lessening credit rationing (Stiglitz and Weiss 1981; see Berger and Udell 1992 for empirical evidence).

  12. 12.

    Lower policy rates may for example reduce the loan-deposit rate spread, shrink the financial intermediation margin and whet bankers’ incentive to take risk to meet some profitability target (European Central Bank Financial Stability Review, December 2007). See also Caballero (2006).

  13. 13.

    Ahrend et al. (2008), Taylor (2007) and Shiller (2007) discuss excess risk-taking in general. Borio (2003) and Borio and Lowe (2002) assert that monetary policy narrowly focused on controlling short-run goods price inflation is less likely to exert control over credit expansions and asset price inflation (followed by subsequent busts). The increase in the number of booms and busts in recent years is thus, in part, a corollary to “the death of inflation”. Borio and Zhu (2012) maintain that recent changes in banking regulation and the financial system may have amplified the impact of monetary policy on the risk-taking by financial intermediaries. Adalid and Detken (2007) find a correspondence between liquidity shocks and aggregate asset prices during asset price boom or bust episodes for 18 OECD countries since the 1970s. Kiyotaki and Moore (1997) show how falling interest rates and rising asset prices cause a lending boom by increasing collateral values. As asset prices and collateral values decrease, loan defaults occur. Finally, Allen and Gale (2004) point out that while monetary policy may influence risk taking and hasten the creation of a bubble, it may also help solve credit problems once the bubble bursts.

  14. 14.

    See Gertler and Gilchrist (1993, 1994), Bernanke and Gertler (1995) and Bernanke et al. (1996) for example. Incomplete coverage of the widely used U.S. (National) Survey of Small Business Finances or the more recent Loan Pricing Corporation datasets (e. g., Petersen and Rajan 1994; Berger and Udell 1995; Bharath et al. 2007; Calomiris and Pornrojnangkool 2009) may therefore complicate any analysis of bank risk-taking.

  15. 15.

    As in Martin-Oliver et al. (2006) we calculate a risk premium for each individual bank and subtract the average risk premium of all banks in that quarter.

  16. 16.

    Den Haan et al. (2007) document that contractive monetary policy does not necessarily reduce the volume of business lending, while Hernando and Martínez-Pagés (2003) find no bank lending channel operative in Spain. Both findings suggest that focusing on new business loans allows us to determine the changes in loan composition without overlooking the concurrent changes in loan volume across loan categories (despite these priors we control for the growth in total and individual bank loan volume in the robustness subsection).

  17. 17.

    The entire database contains more than 32,000,000 loans. We focus on the 22,470,900 commercial and financial loans (80% of total loans), excluding leasing, factoring and other specialized loans, granted by commercial banks, savings banks or credit cooperatives (95% of total credit market). Given the way in which tax identification numbers are assigned, all firms with the same last digit for example would comprise a 10% random sample. We randomly re-sample another 3% of the loans and re-run all specifications. We also sample 6% of the loans and run selected specifications. Results (available upon request) are virtually unchanged and illustrate the robustness of our results to the sampling procedure.

  18. 18.

    Internal or external credit ratings of loans or borrowers are not available. However, these measures are often criticized as coarse and unreliable. Internal credit ratings of banks in the U.S., Germany and Argentina for example are partly based on subjective, non-financial factors (Treacy and Carey 2000; Grunert et al. 2005; Liberti 2004). Loan officers can therefore manipulate the ratings (Hertzberg et al. 2010) and give better ratings when interest rates are low. External ratings provided by credit rating agencies were widely blamed during the financial crisis of 2007 as having been uninformative, even deceptive, in the years prior to the crisis.

  19. 19.

    The loan rate as a proxy for risk may additionally suffer from the variation over time in the price of risk. Evidence from equity prices (Bernanke and Kuttner 2005), bond yields (Manganelli and Wolswijk 2007), buyout pricing (Axelson et al. 2013) and loan rates (Ioannidou et al. 2015) suggest this time variation is common across many financial assets.

  20. 20.

    In 1986 Spain joined the European Union. Consequently, monetary policy started to pay more attention to the exchange rate and, in particular, to the Peseta/Deutsche Mark exchange rate. The monetary policy authorities in this way intended to incorporate more discipline and credibility in their fight against inflation. At the same time capital restrictions were being eliminated. As of mid-1988, Spanish monetary policy was no longer independent from the German monetary policy according to the textbook “Mundell-Fleming trilemma” (see Blanchard 2006 or Krugman and Obstfeld 2006 for example). Spain did devalue its currency three times between 1992 and 1993 and also had temporary credit controls the second half of 1989 and during 1990. In non-reported robustness regressions, we include time dummies for the quarters involved and results do not change significantly.

  21. 21.

    Very few banks in certain periods record negative equity values. Removing these observations does not alter our results and, for consistency reasons, we decided to retain them.

  22. 22.

    Delgado et al. (2007) explain the main features of the Spanish banking system, focusing in particular on the differences in characteristics and behavior of commercial banks (both listed and non-listed), savings banks and credit cooperatives. All of them compete under the same rules although savings banks do not have shareholders.

  23. 23.

    This variable is therefore by construction left censored but removing it or limiting its backward looking horizon does not alter our results.

  24. 24.

    Replacing the spread with the time-varying International Country Risk Guide index does not alter results. We also include measures of banking system efficiency or credit growth, individual bank credit growth, German/Euro inflation and GDP growth, GDP growth forecasts, the volatility of GDP, yield curve measures and house prices. Results are unaffected and we opted to report the more parsimonious models. We revisit the inclusion of these variables in our duration analysis.

  25. 25.

    As in McDonald and Van de Gucht (1999) for example. Loans to small firms typically carry a relatively short maturity, often without early repayment possibilities; hence, we choose to ignore early repayment behavior captured in their competing risk model. Cameron and Trivedi (2005), Heckman and Singer (1984), Kiefer (1988), Kalbfleisch and Prentice (2002) and Greene (2003) provide comprehensive treatments of duration analysis. Shumway (2001), Chava and Jarrow (2004) and Duffie, Saita and Wang (2007) discuss and employ empirical bankruptcy models.

  26. 26.

    We use “default” in the common sense of “a failure to pay financial debts” (Merriam-Webster Dictionary). We classify loans that are ninety days overdue as “in default”. Ninety days overdue is a standard period to classify a loan as non-performing but some countries use different overdue dates depending on the credit product (see for example Beattie et al. 1995).

  27. 27.

    Our main results are unaffected either if we use a log-logistic specification which allows for a non-monotonic duration dependence or a Cox (1972) proportional hazard model for which the baseline hazard is a loan-specific constant. We find weak evidence for non-monotonicity at longer maturities. We return to estimation with time-varying covariates in the robustness subsection.

  28. 28.

    We alternatively cluster at the loan vintage (quarter) level. Results are unaffected.

  29. 29.

    ***Significant at 1%, **significant at 5%, and *significant at 10%. For convenience we also indicate the significance levels of the coefficients in the text.

  30. 30.

    The coefficient on collateral turns statistically significant when we do not control for unobservable borrower heterogeneity (as in Model III for example). Collateral may be set for the borrower in beginning of the relationship and may be only infrequently adjusted.

  31. 31.

    For credit lines we take the total amount that is made available to the borrower. Dropping credit lines that are not drawn or dropping all credit lines does not alter the results. Some loans are also flagged as renewals. Dropping renewals does not change the results.

  32. 32.

    The choice of maturity matters also because the estimated parameter of duration dependence is significantly larger than one: In Model IV it equals e0.816 = 2.261. The hazard rate will therefore increase over the life of the loan. Integrating the hazard rate over the life of the loan yields the probability of default of the loan.

  33. 33.

    While suggestive of the impact of changes in monetary policy on the loan hazard rates, the estimates are calculated for one loan cohort only. To obtain a correct assessment of a monetary policy path on the aggregate hazard rate, cohort size and timing needs to be properly accounted for (loans granted during the period of the increase in the policy interest rate will have a lower and lower hazard rate for example). We leave such an exercise for future work.

  34. 34.

    Our methodology is similar to Thoma (1994). Weise (1999) for example also finds no asymmetric effects of U.S. monetary shocks on prices or output.

  35. 35.

    To obtain interpretable estimates it is required that the variables be either “defined” or “ancillary” with respect to the duration of the loan. A defined variable follows a deterministic path. Age is an example of a defined variable because its path is set in advance of the loan and varies deterministically with loan duration. An ancillary variable has a stochastic path but the path cannot be influenced by the duration of the loan. Collateralization for example is probably not ancillary as banks may eventually tighten collateral requirements when hazard rates increase.

  36. 36.

    Higher inflation may amplify the standard deviation of the spread between bank loan and deposit rates, yet cut bank profitability and lead to banking instability (Boyd and Champ 2003). The evidence seems sometimes mixed (Beck et al. 2003; Demirgüç-Kunt and Detragiache 1998, 2002).

  37. 37.

    Similarly more profitable banks also respond more (not tabulated), possibly by virtue of their higher retained earnings.

  38. 38.

    The measure for bank liquidity we employ is often used in the literature that investigates the credit channel of monetary policy (see Kashyap and Stein 2000 for example). In non-reported regressions we explore other measures of liquidity such as the ratios of loans to deposits and interbank deposits (a measure for the importance of wholesale depositors). We find that the impact of interest rates on credit risk-taking also depends on the structure of the deposits.

  39. 39.

    The insignificant coefficient on the interaction term is positive, possibly because banking supervisors face a capacity constraint (when low interest rates spur risk-taking by many banks) and focus on curtailing risk-taking by banks with high NPL ratios.

  40. 40.

    The correlation is even weaker when we consider the Euro period (i. e., it equals 0.01).

  41. 41.

    A censoring scheme is said to be independent if the probability of censoring at each time t depends only on random processes that are independent of the failure times in the trial, the observed pattern of failures and censoring up to time t in the trial, or (as in our case) on a covariate (Kalbfleisch and Prentice 2002, p. 13).

  42. 42.

    The estimates show that controlling for Spanish macro conditions the German rate is a strong instrument (Staiger and Stock 1997), with a t-statistic on its coefficient that is larger than eight. Jointly the variables explain 97% of the variation in the Spanish interest rate. Notice that the correlation between the two countries’ GDP growth rates is only 36%! These results are consistent with Boivin et al. (2008) who finds that German interest-rate shocks triggered stronger responses to interest rates and consumption in Spain than in Germany itself.

  43. 43.

    The required adjustment of the standard errors on the estimated coefficients is not immediately available. The difference in fit of the models estimated with the actual and the projected Spanish interest rate, however, suggests that the adjustment factor is likely to be close to one. We further calculate the adjustment factor for a model estimated using ordinary least squares for which the dependent variable equals the logarithm of the time to default (censored observations are set equal to their maximum, i. e., 48 months). Again the adjustment factor is close to one and the estimated coefficients themselves in this linear model are very similar to those of the duration model.

  44. 44.

    For comparability reasons we use the annual growth rate. Using the more noisy quarterly growth leaves results virtually unaffected.

  45. 45.

    While we cannot control for loss given default, empirical evidence actually shows a negative correlation between default probabilities and average recovery rates (Altman et al. 2005; Acharya et al. 2007). In addition to house prices, industry, province and borrower effects may have absorbed differences in recovery rates.

  46. 46.

    We control at the firm level for the level of debt. Lower interest rates make debt cheaper, which may increase debt levels and make firm defaults more likely. However, comparing the results in Models I and II in Table 2 (no debt level variable) with all subsequent models suggest that the level of debt is not the only channel through which the stance of monetary policy influences the hazard rate.

  47. 47.

    In a non-reported regression we also use OECD forecasts for the whole period and control for German GDP growth. Results are virtually the same.

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Correspondence to Steven Ongena.

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These are our views and do not necessarily reflect those of the Banco de España and/or the Eurosystem.

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Jiménez, G., Ongena, S., Peydró, JL. et al. “In the Short Run Blasé, In the Long Run Risqué”. Schmalenbach Bus Rev 18, 181–226 (2017). https://doi.org/10.1007/s41464-017-0038-7

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Keywords

  • Monetary Policy
  • Low Interest Rates
  • Financial Stability
  • Lending Standards
  • Credit Risk-Taking
  • Credit Composition
  • Business Cycle
  • Liquidity Risk

JEL Classification

  • E44
  • E5
  • G21