The nuanced origins of exchange rate fluctuations

Simon Lloyd, Daniel Ostry and Balduin Bips

How much capital flows move exchange rates is a central question in international macroeconomics. A major challenge in addressing this has been the difficulty in identifying external cross-border flows, as flows and exchange rates can co-evolve with factors such as risk sentiment. In this post, we summarize a staff working paper that seeks to solve this impasse by using bank-level data capturing the external position of UK-based global intermediaries to construct new ‘Granular Instrumental Variables’ (GIVs). Does. Using these GIVs, we found that banks’ demand for United States dollars (USD) is inelastic – a 1% increase in net-dollar assets pushes the dollar against sterling by 2% – dependent on the state – when banks The effect is doubled if there is a standard deviation below the mean – and that banks are ‘marginal investors’ in the dollar-sterling market.

Our bank-level data set

To reach these findings, we use a detailed data set that captures, at a quarterly frequency from 1997 to 2019, the cross-border assets and liabilities of global banks – both UK and foreign owned – based in the UK. Are. Two features of our data set make it particularly suitable for estimating the causal effects of international banking flows on the USD.

First, due to the UK’s position as the world’s largest international financial centre, our data cover a large portion of international flows – both in absolute terms and relative to other studies. Specifically, it captures more than 38% of the UK’s total external asset position in our 1997-2019 sample and more than 5% of the overall global cross-border position. Furthermore, as Chart 1 shows, compared to lending in other financial centres, cross-border lending by UK-resident banks leads the way. In particular, cross-border lending by UK-based banks comprises, on average, about one fifth of total cross-border bank claims over the period 1997–2019. Therefore, our data is representative of both UK and global cross-border lending and borrowing.

Chart 1: Limits on cross-border claims of UK-resident banks

Notes: Aggregate cross-border banking claims for the UK and other selected countries, 1997 Q1–2019 Q3.

Source: BIS Spatial Banking Statistics.

Second, our data set shows that cross-border lending and borrowing by global banks is concentrated among a relatively small number of large financial players. Specifically, in our sample of 451 banks holding positions in USD, we see that banks’ cross-border lending meets the Pareto principle: about 20% of global banks hold 80% of cross-border USD positions. Chart 2 presents this fact graphically by plotting the Lorenz curve and the corresponding Gini coefficient for UK-resident banks’ cross-border USD assets (both debt and equity) as well as cross-border deposit liabilities. Overall, this diversity in the size of global banks indicates ‘granularity’ in cross-border borrowing and lending.

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Chart 2: Detailed information on cross-border claims of UK-resident banks

Notes: Lorenz curve and Gini coefficient for average cross-border loan assets, equity assets and deposit liabilities of global banks in 2019 Q2. The 45-degree line depicts a hypothetical Lorenz curve in which all banks have equal amounts of cross-border positions and the Gini coefficient is 0.

Our Granular Instrumental Variables (GIV)

We use substantial variation in the size of banks’ cross-border USD positions to construct GIV as exogenous variation in aggregate capital flows.

The idea behind our GIV is to construct a time-series of exogenous cross-border capital flows from a panel of bank-level capital flows by extracting only specific moves by large banks. For this to work, some banks must be large enough that their flows, in response to an external shock, can affect total capital flows – that is, they must be relevant. As discussed above, we find clear evidence of this in the data. Second, we require that both large and small banks react in the same way to unobserved aggregate shocks. This is because we construct our GIV as the difference between USD flows by large banks – formally, the size-weighted average of banks’ flows – and USD flows to average banks – that is, equal to the flows of banks. -Weighted average. GIV can be considered exogenous as long as the equally weighted average strips are subtracted from the general shocks driving banks’ capital flows. In this case, the specific flows in and out of USD assets by large banks remain, meaning that our GIVs are valid for both aggregate flows and exogenous ones.

As evidence of this exogeneity, and – as other papers have shown – in contrast to many other instruments used in the literature, we show that our GIV proxies for the global financial cycle are uncorrelated. Furthermore, a detailed examination of our GIVs shows that the vast majority of moves, as expected, were driven by management changes, mergers or legal penalties for larger banks, as well as specific shocks such as stress-test failures and computer-system failures. Are inspired. ,

Three major empirical results

Controlling for a wide range of bank-level and aggregate factors, we use our GIV to empirically estimate the causal relationship between capital flows and exchange rates. We emphasize three key results.

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First, we find that changes in the net USD position of UK-based global banks – that is, when the stock of USD-denominated external assets changes relative to the stock of USD-denominated external liabilities – has a significant causal effect on USD/GBP. Is. Exchange rate. Specifically, regressing exchange-rate movements directly on our net dollar-loan GIV, we find that a 1% increase in the net dollar-loan position of UK-resident banks leads to a 0.4% increase in USD against GBP. There is an increase of 0.8%. Effect. These effects also persist. Using a local-estimated specification, we estimate that this shock would result in about 2% cumulative USD appreciation one year after the shock, as Chart 3 shows. Consistent with theory, this effect does not reverse even two years after the initial shock.

Chart 3: Dynamic impact of exogenous changes in the net USD debt position on the USD/GBP exchange rate

Notes: The increase reflects USD appreciation (GBP depreciation) in response to a 1% shock to the USD position. The shaded area represents the 95% confidence band.

Second, we use our GIV to estimate the slope of the supply curve for USD by the rest of the world investors – such as hedge funds and mutual funds, the focus of Camanho et al (2022) – and the demand curve for USD by the UK. We do. Resident global banks are using two-step least squares. On the supply side, we find that USD supply from the rest of the world is elastic with respect to the USD/GBP exchange rate. Stated otherwise, the supply curve for dollars traded by non-UK bank intermediaries is relatively flat: a 1% exchange-rate change results in a more than proportionate change in the supply of USD. However, on the demand side, our estimates show that the demand for USD by UK-resident banks is inelastic, i.e. the demand curve is relatively inelastic. Chart 4 presents the estimated demand and supply relationships graphically.

Chart 4: UK-bank inelastic demand and elastic to remaining world supply of USD

Notes: The supply and demand relationship between exchange rate changes and changes in net USD-denominated loan volume implied by the elasticity estimate. Shaded areas indicate one standard deviation error band.

Third, to examine the drivers of this inelastic demand, we extend our empirical setup to examine the role of banks’ time-varying risk-carrying capacity for FX dynamics. The interaction of banks’ Tier-1 capital ratio with our GIV shows that when banks’ capital ratio is one standard deviation below the mean, the causal effect of capital flows on exchange rates is twice as large. Furthermore, it shows that banks’ demand curve for dollars becomes steeper (inefficient) as their capital decreases. This finding complements that of Baker et al (2023), who find – using data on a specific form of bank lending, cross-country syndicated loans – that arbitrage barriers affect FX mobility.

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Implications and Conclusion

Our finding of inelastic demand for USD by UK-resident global banks has at least two key implications. First, in relative terms, the fact that the elasticity of demand is well below the elasticity of supply implies that, due to their relative price insensitivity, the USD/GBP exchange-rate fluctuates in response to UK-based bank shocks. -Have a greater impact on the ups and downs (average of other financial intermediaries in the market). That is, UK-resident banks are ‘marginal investors’ in the dollar-sterling market.

Second, inelastic demand for USD by UK-resident banks implies that changes in the supply of USD from the rest of the world – for example, from US monetary policy and other drivers of the global financial cycle – could have a material impact on the value of sterling. Is. à-vis dollars. This can have a major impact on the broader economy through export and import prices. That being said, our results suggest that the extent of volatility of UK-domiciled banks can be reduced when banks are better capitalized. Thus, domestic prudential policies (linked to capital ratios) can help contribute to greater exchange rate stability and thereby help insulate domestic economies from global financial cycles.


Simon Lloyd works in the Bank’s Monetary Policy Outlook Division, Daniel Ostry works in the Bank’s Global Analysis Division and Balduin Bips is a PhD student at the University of Cambridge.

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