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US or Domestic Monetary Policy: Which Matters More for Financial Stability?

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Abstract

We study the impact of sustained monetary policy easing on risk-taking behavior using firm-level data for nearly 1000 financial institutions in 21 countries over 15 years. We find that both banks and nonbanks increase leverage following domestic monetary policy easing. Surprisingly, following easing in the USA (but not in the euro area), leverage in non-US firms increases as well, by more than it does following a domestic easing. We go on to show that spillovers from US policy are stronger in countries that are more financially developed, less open to trade, and have smaller gross US dollar liabilities. These results lend support to concerns raised by emerging market policymakers that US monetary policy spillovers complicate domestic policymakers’ decisions.

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Fig. 1

Source: Bloomberg, Datastream, Haver, Worldscope, and authors’ calculations

Fig. 2

Source: Bloomberg, Datastream, Haver, and authors’ calculations

Fig. 3

Source: Bloomberg, Datastream, WEO, Worldscope, and authors’ estimates

Fig. 4

Source: Bloomberg, Datastream, WEO, Worldscope, and authors’ estimates

Fig. 5

Source: Bloomberg, Datastream, WEO, Worldscope, and authors’ estimates

Fig. 6

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Notes

  1. See the discussion in Gopinath and Stein (2018).

  2. See Table A5 of BIS locational banking statistics https://www.bis.org/statistics/bankstats.htm?m=6%7C31%7C69.

  3. See https://www.treasury.gov/resource-center/data-chart-center/tic/Pages/index.aspx.

  4. See Cecchetti and Schoenholtz (2017).

  5. See, for example, Blanchard et al. (2015).

  6. This result is consistent with findings in the previous studies that monetary policy easing induces greater risk-taking. For example, see Altunbas et al. (2010), Maddaloni and Peydró (2011), Chodorow-Reich (2014), Dell’Ariccia et al. (2014), Dell'Ariccia et al. (2017), Feroli et al. (2014), Jiménez et al. (2014), and IMF (2015).

  7. Exemplified by the work of Gürkaynak et al. (2005) and Chen et al. (2014), this alternative line of research identifies what is arguably an exogenous shock and can thus investigate a causal relationship with other fast-moving variables such as asset prices. However, firm leverage is likely to be a slow-moving variable reacting more to monetary policy expectations than to very small monetary policy surprises which may offset each other over subsequent monetary policy announcements.

  8. As alternative measures of the trend component (\(\bar{i}_{t}\)), we considered a four-quarter moving average, a 12-quarter moving average, one-sided HP filter with a smoothing parameter of 1600, and Hamilton (2018)’s filter with four lags. Our baseline duration measure, based on the eight-quarter moving average, is positively correlated with alternative measures with correlation equal to 0.4, 0.6, 0.8, and 0.2, respectively (all at a one-percent level of statistical significance).

  9. Another measure is also possible, found by adding the extent—as opposed to counting the instances—of consecutive drops in interest rates (“Appendix 2”). We return to this continuous measure as a robustness check, where we find that results do not vary.

  10. We note that, since there are no cases in our data where \(\overline{{i_{kt} }} = \overline{{i_{kt - 1} }}\), this strict inequality condition is equivalent to the weaker one.

  11. For a survey of the debate over various specifications of the Taylor type interest rate rules, see Taylor (1993, 1999), Orphanides (2001), Carare and Tchaidze (2005), Rudebusch (2005), Christiano et al. (2010), and Nikolsko-Rzhevskyy and Papell (2013). On the natural interest rate, see Laubach and Williams (2003) and Wu (2005).

  12. For a discussion of how unconventional monetary policy acts through long-term rates, see, for example, Gagnon et al. (2011), Wright (2012), Swanson and Williams (2014) and Chen et al. (2014). In an earlier paper, Cecchetti et al. (2017), we show that the results are robust to various interest rate measures. There, we examine the nominal and real three-month sovereign rate, and the nominal 10-year rate.

  13. Other studies using two-year yields as the indicator of monetary policy include Gilchrist et al. (2014) and Gertler and Karadi (2015).

  14. As we note in Sect. 2.3, we confirm the robustness of these results to alternative measures of financial stability: the risk-adjusted return on equity and the z-score.

  15. We describe the sources of our data in “Appendix 1”.

  16. “Banks” are firms who derive their revenue primarily from conventional banking operations. “Insurance companies” include life- and non-life insurers, as well as reinsurance companies. “Investment banks” are firms primarily engaged in investment banking and brokerage services. “Asset management” are entities that invest third-party funds. “Real estate firms” consists of real estate investment trusts (REITs), as well as real estate management and development firms. And “other” includes holding companies, consumer finance firms, and firms that provide specialized or diversified financial services.

  17. In computing the distributional statistics in Table 1, we do not weight by asset size. We note that the measures not only show substantial variation across firms, but also over time (see Appendix Fig. 7).

  18. Using Choi’s (2001) unit-root test, which allows for unbalanced panels, we find little evidence of nonstationarity in the macroeconomic variables. Specifically, we perform Augmented Dickey–Fuller and Phillips–Perron tests with four-quarter lags, with and without the de-meaning. The results suggest in all cases, with the exception of the log of the sovereign bond rating, we can reject the null hypothesis that “all the panels contain a unit-root”.

  19. While our use of lagged macrovariables avoids endogeneity, we run the risk that we may mistakenly interpret correlation of leverage with contemporaneous aggregate conditions as duration effects. This is of particular concern in the case of the contemporaneous stock–bond correlation discussed by Christiansen and Ranaldo (2007). To address this concern, we examined the bond–stock correlations in our sample and found that the full-sample correlation is about zero, suggesting that our estimates of \(\alpha_{1}\) are not driven by a systematic bond–stock correlation.

  20. As Adrian and Shin (2010) point out, if other things are unchanged, leverage declines when stock prices rise.

  21. Since firms do not change countries, firm fixed effects control for both country- and firm-specific factors.

  22. Throughout the paper, we calculate standard errors for regression coefficients using the method from Driscoll and Kraay (1998) that is robust to heteroskedasticity and cross-sectional as well as temporal dependence with stationary variables.

  23. To compute the marginal impact of a change in duration on the level of leverage, first rewrite Eq. (2) as \(Y_{ikt} = \exp (\alpha_{1} D_{kt} + \cdots )\). Then take the derivative with respect to \(D_{kt}\) to obtain \([\partial Y_{ikt} /\partial D_{kt} ] = \alpha_{1} \exp (\alpha_{1} D_{kt} + \cdots ) = \alpha_{1} Y_{ikt}\), which is the marginal effect. Alternatively, differentiate (2) to obtain \((1 /Y_{ikt} ){\text{d}}Y_{ikt} = \alpha_{1} {\text{d}}D_{kt}\), so \(({\text{d}}Y_{ikt} / {\text{d}}D_{kt} ) = a_{1} Y_{ikt}\). We evaluate \(\alpha_{1} Y_{ikt}\) at the sample median leverage and report the result in the table. Standard errors are computing using the delta method, evaluated at this same sample median.

  24. We note that if we substitute four-quarter-lagged duration for the contemporaneous duration in Eq. (2), the results are somewhat weaker.

  25. Effects are slightly stronger with four lags (and more significant for investment banks), suggesting US policy’s impact is more persistent than that of domestic policy.

  26. Cecchetti et al. (2017) report a subset of these results in detail in the robustness section.

  27. For the purposes of this exercise, we choose the crisis period to start in July 2007, when Bear Stearns disclosed that the two subprime hedge funds had lost most of their value; and ends in July 2009, when Fed Chairman Bernanke (2009) said that "the extreme risk aversion of last fall has eased somewhat, and investors are returning to private credit markets".

  28. In principle, we could also examine spillovers from Japan, but the lack of interest rate variation over our sample period makes this difficult.

  29. The Financial Stability Board (2019) reports that in 2017, the euro area banks had assets of US$34.8 trillion, nearly 50 percent higher than US banks’ assets.

  30. Since the duration could be endogenous, we use lagged differences of the duration measure as instruments in the levels equation, and the second lags of the duration measure as instruments in the differenced equation. We conducted this GMM estimation using xtbond2 in Stata (Roodman 2009).

  31. We note that a paucity of data makes the country-level spillover estimates unreliable, so we do not report them here. The number of firms in each industry group in a country is in many cases in the single digits. See Appendix Table 7.

  32. For example, Bruno and Shin (2015a) document foreign currency assets and liabilities of banks outside the USA and point out the prominent role played by the US dollar. Please also see McGuire and von Peter (2009) and Bruno and Shin (2017).

  33. The data and descriptions are available at: http://data.imf.org/?sk=F8032E80-B36C-43B1-AC26-493C5B1CD33B.

  34. We use the four-quarter lag of the Z variables because they are only available at the annual frequency.

  35. We also examine a variety of other possible determinants, including sub-indices of the financial depth index, cross-border assets and liabilities, claims of US banks on a country, and the fraction of a country’s exports that are to the USA, and the change in US dollar claims. Overall, the results reported here are representative of what we find with this broader set of variables.

  36. As suggested in the previous footnote, these results hold for the component parts of the financial development index that we use.

  37. We note that the data only include banks in countries that report to the BIS locational banking statistics. The list, along with the dates when each country began reporting, is here: https://www.bis.org/statistics/rep_countries.htm. While nonbank financial flows are becoming more important over time, we focus on the bank flows in our analysis due to lack of data on liabilities to nonbanks by currency.

  38. It is worth noting that net claims have virtually no impact. Consistent with work of Obstfeld (2012) and others, we find that gross stocks are what matters.

  39. Our sample of banks does not include the Finland and the Netherlands, so they are not present in the figure. See Table 7 for a tally of the number of banks by country in our sample.

  40. We use the log-deviation of the bilateral US dollar exchange rates to account for fixed effects in the interaction terms and to ensure that units of the exchange rates do not matter.

  41. We thank Martin Saldias, who kindly shared a list of the constituents of the Bloomberg BWORLD Index in each quarter of the sample period.

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Authors and Affiliations

  1. Brandeis International Business School, Waltham, USA

    Stephen G. Cecchetti

  2. National Bureau of Economic Research, Cambridge, USA

    Stephen G. Cecchetti

  3. Centre for Economic Policy Research, London, UK

    Stephen G. Cecchetti

  4. Monetary and Capital Markets Department, International Monetary Fund, Washington, DC, USA

    Tommaso Mancini-Griffoli, Machiko Narita & Ratna Sahay

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  1. Stephen G. Cecchetti
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  2. Tommaso Mancini-Griffoli
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  3. Machiko Narita
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Correspondence to Stephen G. Cecchetti.

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This paper was prepared for the IMF’s Nineteenth Jacques Polak Annual Research Conference “International Spillovers and Cooperation”, November 1–2, 2018, Washington, DC. We thank Tobias Adrian, Stefan Avdjiev, Ryan Banerjee, Giovanni Dell’Ariccia, Kay Giesecke, Hibiki Ichiue, Futoshi Narita, Fabrizio Perri, Martin Saldias, Jeremy Stein, Linda Tesar, Jonathan Wright, two referees, many other IMF colleagues and visiting scholars who provided comments, and participants at the Jacques Polak Conference, a BOJ seminar and the 2019 BIS-BoE-ECB-IMF Joint Workshop. We also thank Zohair Alam and Alex Weng for superb assistance. The views expressed in this paper are those of the authors and do not necessarily represent those of the IMF.

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Appendices

Appendix 1: Data Sources and Definitions

We conduct our analysis using a panel data set of publicly listed financial firms in 21 countries from 1998 Q1 to 2014 Q4. Based on Global Industry Classification Standard, we divide the financial sector into six industry groups: banks, insurance companies, investment banks, asset managers, real estate firms, and other financials. Our sample countries include 18 advanced economies: Austria (AUT), Australia (AUS), Belgium (BEL), Canada (CAN), Finland (FIN), France (FRA), Germany (DEU), Ireland (IRL), Italy (ITA), Japan (JPN), Netherlands (NLD), Portugal (PRT), Republic of Korea (KOR), Spain (ESP), Sweden (SWE), Switzerland (CHE), the UK (GBR), and the USA). We also include three emerging market economies: Brazil (BRA), Mexico (MEX), and South Africa (ZAF).

Our firm-level financial data come from the Worldscope (Thomson Reuters), which harmonizes the definitions, allowing for cross-country analysis. It is important to use such harmonized data because accounting presentations and terminologies differ across countries. Since we use a firm’s market capitalization as a measure of the market value of equity, we restrict our sample to firms with actively traded equity. We construct the list of active financial firms from the constituents of the Bloomberg BWORLD Index since the start of its sample.Footnote 41 Table 7 shows the number of firms in each sector and country.

Table 7 The number of firms by financial industry and country.
Full size table

We merge our firm-level data with country-level macroeconomic indicators. The firm-country unbalanced panel data set covers 988 listed financial firms. In our sample, there are 38,363 firm-quarter observations based on 988 firms whose indicators of financial vulnerabilities are available. For example, Fig. 7 shows the time series of leverage measured by the asset-to-equity ratio, which is our key indicator of firms’ vulnerabilities, by industry.

Fig. 7
figure 7

Source: Worldscope and authors’ calculations

Evolution of the median asset-to-equity ratio. Note: The figure displays the time-series of the median asset-to-equity ratio by industry. The jagged patterns (e.g., banks during the early 2000s) arise because some firms only report their data semiannually.

Full size image

Table 8 summarizes data sources and definitions for each of the variables in our analysis.

Table 8 Definitions and sources
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Appendix 2: The Continuous Measure of the Duration

By focusing purely on the duration of policy easing (measured in quarters), our analysis fails to capture the intensity of monetary policy easing. That is, we do not distinguish periods of large declines from those of small declines. In this appendix, we consider whether the intensity of easing matters.

To shift from a discrete to a continuous measure of easing, we define the following measure of duration:

$$C_{kt} = \left\{ {\begin{array}{*{20}l} {C_{kt - 1 } - \Delta \overline{{i_{kt} }} } \hfill &\quad {{\text{if }} \overline{{i_{kt} }} < \overline{{i_{kt - 1} }} } \hfill \\ 0 \hfill &\quad {\text{otherwise,}} \hfill \\ \end{array} } \right.$$
(7)

where \(C_{kt}\) is the continuous measure of the cumulative decline in the interest rate in country k at time t; \(\Delta \overline{{i_{kt} }} = \overline{{i_{kt} }} - \overline{{i_{kt - 1} }}\); and \(\overline{{\iota_{kt} }}\) is the trend component of 2-year rates for country \(k\), measured as the moving average \(\overline{{i_{kt} }} = \frac{1}{8}\sum\nolimits_{\tau = 1}^{8} {i_{k,t - \tau + 1} }\). This measure tells us the cumulative decline in the trend component of the interest rate during the easing period.

As shown in Table 9, the average interest rate decline per quarter of the consecutive monetary policy easing in our sample is 0.2 percentage points (ppts). Furthermore, the cumulative decline in the 2-year rate is on average 1.7 percentage points during the monetary policy easing period, while it registers its max at 14.5 percentage points.

Table 9 Summary statistics of the duration measures of monetary policy easing.
Full size table

Table 10 reports the results of estimating the analog to Eqs. (2), (3) and (4) using \(C_{kt}\) for \(D_{kt}\) (and equivalently for the duration of US and euro area monetary policy easing). We report the marginal effect of a 1 percentage point easing, which (on average) takes 5 quarters.

Table 10 Baseline versus continuous measures of the duration of monetary easing
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Cecchetti, S.G., Mancini-Griffoli, T., Narita, M. et al. US or Domestic Monetary Policy: Which Matters More for Financial Stability?. IMF Econ Rev 68, 35–65 (2020). https://doi.org/10.1057/s41308-020-00108-2

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