Impact of the UK QE on banks’ balance sheets – Bank Underground

Mahmoud Fatouh

Quantitative easing (QE) entails creating new central financial institution reserves to fund asset purchases. Deposited within the reserves account of the vendor’s financial institution, these reserves can have implications for banks’ asset mixes. In our paper, we use stability sheet information for 118 UK banks to empirically examine whether or not the asset compositions of banks concerned within the UK QE operations reacted otherwise compared to banks not concerned within the preliminary rounds of QE between March 2009 and July 2012.

QE and central financial institution reserves

QE contains the creation of central financial institution reserves (reserves hereafter), which significantly will increase the scale of central financial institution stability sheet. Chart 1 exhibits that reserves within the UK elevated considerably after the introduction of UK QE.

Chart 1: Financial institution of England liabilities and capital (£million, weekly)

Supply: Financial institution of England

When the Financial institution of England conducts QE, reserves are credited to the reserves account of the vendor’s financial institution, and that financial institution then credit the vendor’s deposit account with the identical quantity. Therefore, banks concerned in QE operations (QE banks) initially obtain further liquidity (as reserves), whereas different banks (non-QE banks) don’t.

Nevertheless, the distribution of the extra reserves between QE banks and non-QE banks might change. Among the further liquidity might ‘leak’ to non-QE banks because the sellers (principally non-bank financials within the UK) make investments their cash in different belongings, ensuing of their deposits shifting across the banking system. The magnitude of this ‘leak’ is determined by whether or not these deposits find yourself with different QE banks or not

Finally, we argue that whereas a number of the further reserves would leak to non-QE banks, most would stick with QE banks. It’s because non-bank financials principally do enterprise with a small subset of banks who’re additionally members within the Financial institution’s QE operations. Because of the dynamic nature of reserves and the a number of elements affecting them, it’s laborious to isolate the influence of QE on the distribution of further reserves between QE banks and non-QE banks. But, evaluating the inventory of reserves earlier than and after QE introduction helps our argument (see Chart 2).

Chart 2: Modifications in lending and securities – QE banks versus non-QE banks

QE and financial institution lending

Conventionally, within the presence of reserve necessities, any improve in reserves can probably improve whole credit score provided by the banking system by a number of multiples of the preliminary improve in reserves (cash multiplier results). This is able to be the case if there are not any different constraining elements similar to capital and liquidity necessities (which might be extra binding than reserve necessities), or low demand for credit score. There are not any reserve necessities within the UK (ie the cash multiplier is undefined), which means that the provision of credit score is especially pushed by banks’ means and/or incentives to lend.

Whereas the supply of liquidity and capital positions govern banks’ means to lend, their incentives are largely decided by the regulatory framework in operation particularly in downturns. Submit-crisis, the extra reserves from QE purchases elevated the supply of liquidity which alleviated liquidity constraints. Nevertheless, banks had depleted capital positions and operated in a regulatory framework that assigns low danger weights to funding in authorities securities and far increased weights to lending to the true financial system. In consequence, elevated reserves could or could not have led to increased lending, and therefore the Financial Coverage Committee didn’t emphasise the transmission of QE influence through the financial institution lending channel (BLC).

In different phrases, QE improves banks’ means to lend when applied in a liquidity-scarce surroundings, however different elements, similar to financial institution capital positions and danger weighting, can have an effect on the influence of QE on financial institution lending.

What we do

We’re all in favour of understanding the influence of QE, whereas controlling for different developments that might additionally have an effect on financial institution lending, such because the post-crisis Basel III reforms. These reforms tightened capital necessities, requiring weakly capitalised banks to boost further capital assets. To isolate the influence of QE, we assemble a distinction in variations (DiD) strategy, during which we examine adjustments in stability sheets of QE banks (handled group) to these of comparable non-QE banks (management group). We decide QE banks utilizing a confidential Financial institution of England’s information set, which exhibits which banks obtained reserves by way of UK-QE operations and the scale of the extra reserves. Our pattern contains stability sheet information for 118 UK banks from 2000 to 2018.

To attract significant conclusions in regards to the position performed by QE, we have to assess the (statistical) significance of the variations between the 2 teams, isolate the influence of different elements, and guarantee any results we observe are particular to the UK-QE interval.

You will need to word that our evaluation covers the influence of the early QE rounds, ie these till July 2012. The consequences of the Brexit and Covid-19 rounds are coated in one other paper assessing the interplay between QE and the federal government lending assist schemes in the course of the Covid stress.

QE banks versus non-QE banks

QE banks are on common greater and maintain comparatively extra securities than non-QE banks. Therefore, with none modifications, our outcomes can be vulnerable to choice bias, within the sense that any variations detected between QE banks and non-QE banks may very well be because of variations in financial institution traits moderately than QE remedy. To alleviate the results of choice bias on the validity of outcomes, we use a propensity rating matching methodology to eradicate common variations between the remedy and management teams, and therefore enhance the validity of our conclusions based mostly on comparisons between the 2 teams. Propensity scores depend on completely different financial institution traits, and are used to create a matched set from non-QE banks for every QE financial institution, based mostly on a sure matching ratio (1:5 in our baseline setup). That’s, every QE financial institution is assigned a matched set that features 5 non-QE banks with propensity scores most much like it. Matching would achieve success if it manages to eradicate pre-matching variations between the remedy and management teams. We test that by regressing a remedy standing dummy on variables reflecting the scale, profitability and stability sheet profile, earlier than and after matching. As Desk A illustrates, with out matching, QE banks, on common, are bigger and maintain extra securities, and these variations are statistically vital. Submit matching, common variations between QE banks and the (matched) management group turn out to be statistically insignificant. We run our matching utilizing various matching ratios from 1:1 to 1:8, and observe comparable patterns.

Desk A: Propensity rating matching

Notes: Probit regressing the remedy on financial institution traits in 2008h2. The dependent variable is the financial institution remedy standing. The impartial variables are dimension because the pure log of whole belongings, fairness as whole belongings minus whole liabilities, return on belongings (ROA), whole securities over whole belongings and web curiosity revenue over whole belongings. Mannequin (1) experiences the pre-matching outcomes whereas mannequin (2) experiences the submit matching outcomes with matching ratio 1:5. Coefficients and normal errors are reported for every variable. Customary errors are clustered on the financial institution stage and reported in brackets, * p<0.10 ** p<0.05 *** p<0.01.

DiD Outcomes

Our DiD mannequin controls for variations in dimension (whole belongings), stage of leverage (fairness to whole belongings), profitability (return on belongings), and securities to whole belongings and web curiosity revenue to whole belongings (variations in enterprise fashions).

Financial institution lending

Desk B presents the remedy coefficients for lending DiD regressions. According to the typical tendencies in Chart 2, remedy coefficients for all (however one) lending regressions are statistically insignificant. That’s, we discover no proof of the choice financial institution lending channel (BLC); the extra liquidity didn’t incentivise QE-bank to extend lending, relative to the management group. There isn’t a proof suggesting that these outcomes had been pushed by adjustments in relative demand for loans the 2 teams confronted.

Desk B: Therapy coefficients for DiD lending regressions

Notes: Therapy standing (Handled_i) equals to 1 for QE banks and 0 for non-QE banks. Controls are dimension as log of whole belongings, fairness over whole belongings, return on belongings (ROA), securities over whole belongings and web curiosity revenue over whole belongings. The reported p-values check the coefficient inequality between QE1 and QE2. Customary errors are clustered on the financial institution stage and reported in brackets, * p<0.10 ** p<0.05 *** p<0.01.

Securities and different belongings

Desk C exhibits the DiD coefficients for different financial institution belongings. Relative to the management group, QE banks elevated reserves and lowered lending to different banks after QE1. Additionally they elevated holdings of presidency securities, particularly after QE2. This means that QE banks reallocated their assets from lending in direction of authorities securities with low danger weights.

Desk C: Therapy coefficients for DiD regressions for different belongings

Conclusion

We check whether or not the extra reserves created through QE led to a rise in financial institution credit score. We accomplish that by evaluating the behaviour of QE banks’ belongings, in comparison with different banks, utilizing a DiD strategy.

We discover no proof of transmission through the BLC. We predict that the optimisation of regulatory capital motivated QE banks to take a position the extra liquidity in high-yield low danger weight sovereigns moderately than enterprise loans that appeal to increased danger weights.

Mahmoud Fatouh works within the Financial institution’s Prudential Framework division.

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