E52 | Jotup

Liquidity transformation and Eurosystem credit operations

Retrieved on:

Jeudi, avril 25, 2024

Key Points:

Measuring market-based core inflation expectations

Retrieved on:

Jeudi, février 15, 2024

Abstract

Key Points:

Abstract
We build a novel term structure model for pricing synthetic euro area core inflation-linked
swaps, a hypothetical swap contract indexed to core inflation.
The model provides estimates of market-based expectations for core inflation, as
well as core inflation risk premia, at daily frequency, whereas core inflation expectations from
surveys or macroeconomic projections are typically only available monthly or quarterly.
We
find that core inflation-linked swap rates are generally less volatile than headline inflationlinked swap rates and that market participants expected core inflation to be substantially
more persistent than headline inflation following the 2022 energy price spike.
In this paper, we aim to infer market-based core inflation expectations, which are otherwise
not directly observable because no financial asset directly tied to core inflation exists.
We deem this second assumption reasonable because HICP inflation itself is a linear combination
of core as well as energy and food inflation.
The level of 2 percent and relatively low volatility of
long-term inflation expectations suggests that inflation expectations are firmly anchored at the
ECB?s 2 percent inflation target.
This assumption appears reasonably uncontroversial,
as core inflation is a sub-component of headline inflation, which the observable headline ILS
rates are tied to.
Our estimates of core ILS rates reflect both market participants? genuine core
inflation expectations and a core inflation risk premium, but our model explicitly allows for
this decomposition.
The model-implied estimates of core ILS rates appear reasonable along several dimensions:
(i) like realized core inflation is less volatile than headline inflation, the core ILS rates are less
volatile than headline ILS rates, (ii) core ILS rates comove less with oil prices than headline
ILS rates, (iii) the core inflation expectations, as reflected in core ILS rates, typically evolve
similarly as the core inflation projections by Eurosystem staff, and (iv) consistent with market
commentary at the time, core ILS rates suggest that market participants expected core inflation
to be substantially more persistent than headline inflation following the 2022 energy price spike.
To the best of our knowledge, we are the first to price core ILS rates and decompose them into
market-based expectations for and risks around the core inflation outlook.
Our approach to inferring core ILS
rates from headline ILS rates, realized headline and core inflation as well as survey expectations
for headline and core inflation is also related to Ang et al.
Relative
to their study, we separately measure core inflation expectations and risk premia, we provide
core inflation expectations at a higher-frequency, and we provide evidence on the causal effects
ECB Working Paper Series No 2908

6

of monetary policy shocks on core inflation expectations and risk premia.
Specifically, we decompose the synthetic core ILS rates
into average expected core inflation over the lifetime of the swap contract and a core inflation
risk premium that compensates investors for core inflation risk.
In
our model below, this term is constant over time and relatively small, so we will simply refer
to the core inflation risk premium as the difference between the core ILS rate and the average
expected core inflation over the lifetime of the swap contract.
3.2
Core ILS rates

To have a joint model for headline and core ILS rates, we need one further assumption on the
dynamics of realized core inflation.
The assumption that core inflation is driven by the same set of factors as headline inflation
should be relatively uncontroversial: since headline inflation is a weighted average of core and
food and energy inflation, it should reflect any factors driving core inflation.
If there are factors
driving food and energy inflation, which do not show up in core inflation, then those factors
should still show up in headline inflation.
In step two, to be able to infer the factor
loadings of core inflation, we would regress realized core inflation onto the estimated latent
factors to identify the additional parameters in equation (12).
Before the fourth
quarter of 2016, the SPF did not ask respondents for their core inflation expectations, so we
are not able to use survey-based information about core inflation before then.
Before
2016, the fitted core inflation series is somewhat above the realized one, potentially reflecting
that the model has limited information about core inflation over this early period due to the
lack of information about core inflation from surveys.
This could have been the
case if one of the factors moved core inflation and energy and food inflation in exactly offsetting
direction, so the overall impact on headline inflation was exactly zero.
During 2021, for example, there were
ECB Working Paper Series No 2908

25

Figure 7: Decomposition of synthetic core ILS rates
2y core ILS

5y core ILS

5
4

5
ILS

premia

exp

4

ILS

premia

exp

3

3

2

2

1

1

0

0

-1

-1

-2
2017 2018 2019 2020 2021 2022 2023

-2
2017 2018 2019 2020 2021 2022 2023

10y core ILS

5y5y core ILS

5
4

5
ILS

premia

exp

4

ILS

premia

exp

3

3

2

2

1

1

0

0

-1

-1

-2
2017 2018 2019 2020 2021 2022 2023

-2
2017 2018 2019 2020 2021 2022 2023

Note: Synthetic core ILS rates decomposed into genuine core inflation expectations and core inflation risk
premia.
ECB Working Paper Series No 2908
26

Figure 8: Decomposition of ILS rates
2y ILS

5y ILS

5
4

5
ILS

premia

exp

4

3

3

2

2

1

1

0

0

-1

-1

-2
2006

2010

2014

2018

2022

-2
2006

ILS

2010

10y ILS

2018

2022

5
ILS

premia

exp

4

3

3

2

2

1

1

0

0

-1

-1

-2
2006

2014

exp

5y5y ILS

5
4

premia

2010

2014

2018

2022

-2
2006

ILS

2010

premia

2014

2018

exp

2022

Note: ILS rates decomposed into genuine core inflation expectations and core inflation risk premia.
We find that the headline inflation risk premium
indeed does responds more strongly than the core inflation risk premium.
The key
assumption underlying our approach is that traded headline ILS rates span core inflation, which
ECB Working Paper Series No 2908

35

should be reasonably uncontroversial as core inflation is a sub-component of headline inflation.
We fit the model to euro area headline ILS rates, realized headline and core inflation, and
both headline and core inflation expectations reported in the SPF.
Decomposing our core ILS rates into genuine core inflation expectations and core
inflation risk premia shows that shorter maturities mainly reflect core inflation expectations,
while the core inflation risk premium matters relatively more for longer maturities.
Our results suggest that a monetary policy tightening surprise significantly lowers
near-term core inflation expectations, although less so than it lowers headline inflation expectations.

Households' response to the wealth effects of inflation

Retrieved on:

Samedi, février 10, 2024

Key Points:

What drives banks’ credit standards? An analysis based on a large bank-firm panel

Retrieved on:

Mercredi, février 7, 2024

NPL, Seven Bank, Xi, Name, AnaCredit, Data, Bank, ECB, SAFE, JEL, Bias, Knowledge, XB, P50, Ciccarelli, Credit, Regression analysis, Shock, Central bank, Mand, E51, Movement, E44, SNL, Government, Access, Environment, Literature, P90, BLS, P25, CDS, Darst, Risk, European Central Bank, Bureau van Dijk, OBS, Probability, Complementarity, ROA, Policy, COVID-19, Research, Standard score, Uncertainty, R2, Heider, E52, Holmström, CSB, Health, P75

An analysis based on a large

Key Points:

An analysis based on a large
bank-firm panel
No 2902

Disclaimer: This paper should not be reported as representing the views of the European Central Bank
(ECB).
We find
that weaker capitalised banks adjust their credit standards more than healthier banks, especially for
firms with a higher default risk.
Here we find t hat w eaker b anks r espond m ore f orcefully by
tightening their credit standards more than better capitalised banks.
On the contrary, weaker banks
may be more prone to adopt looser credit standards, with the aim of increasing their revenues.
To answer these questions, we analyse the determinants of banks? credit standards, i.e., their internal
guidelines or loan approval criteria applied when deciding on granting credit.
2 Altavilla
ECB Working Paper Series No 2902

2

area banks tighten their credit standards more when linked to riskier firms, measured via firms? leverage
and default risk.
We assess how euro area banks adjusted their credit standards in response to
the negative COVID-19 pandemic shock, after accounting for government support measures.
When deciding on their credit standards, banks assess risks
based on both their own loss absorption capacity and the credit risk of their borrowers.
On the contrary,
weaker banks may be more prone to adopt looser credit standards, with the aim of increasing their
revenues.
We provide evidence that
euro area banks tighten their credit standards more when linked to riskier firms, measured via firms?
leverage and default risk based on the Altman Z-score.
In
addition, they focus on a different research question and use data from the IBLS only as a control.
ECB Working Paper Series No 2902
5

capital position implies less tightening of lending criteria, possibly reflecting the fact that banks can
afford to adjust their credit standards more moderately.
Based on our results, this implies a stronger deterioration of their lending conditions compared
with less vulnerable firms.
We assess how euro area banks adjusted their credit standards in response to
the negative COVID-19 pandemic shock, after accounting for government support measures.
This is in line with the role of government support
measures such as loan guarantees mitigating banks? exposure to firms? credit risks as they shield banks
from firms? increased credit risks.
2
Related literature

Our paper is closely related to studies analysing credit supply based on BLS indicators and the impact
of monetary policy shocks on bank lending conditions in the euro area.
Hempell and Kok (2010) disentangle
pure loan supply based on the BLS factors and investigate the role played by such factors for loan growth.
Several other studies link confidential individual BLS data with actual bank-level data, but not firm
data, allowing an analysis of bank characteristics relevant for bank lending conditions.
They find that a short-term interest rate shock decreases both loan supply
and demand, but more for less healthy banks.
Their findings are consistent with the results of our paper on the favourable impact of bank health on lending standards.
Both papers tend to find no evidence of higher risk taking of banks as a result
of accommodative monetary policy.
More recent studies are based on
confidential bank and firm-level data from national credit registers.
(2012) who focus on the bank-firm-relationship in Spain, based on credit register data.
Ferrero, Nobili, and Sene (2019) arrive at a corresponding conclusion on the risk-taking
channel based on a confidential loan-level dataset of Italian banks.
In another paper, Altavilla, Boucinha, and Bouscasse (2022)
disentangle credit demand and supply based on euro area credit register data (AnaCredit) for the period
of the pandemic.
Our results emphasise the
mitigating impact of government guarantees on a tightening of credit standards during the pandemic.
This mitigating impact played a major role in loan demand and not credit supply being decisive for lending volumes during the pandemic.
Based on their model, accommodative monetary policy is part of the optimal policy mix, combined with social insurance.
To keep the wealth of information
available in the BLS, we run our analysis at the quarterly frequency of the survey.
of employees
101.4

2456.9

2.0

4.0

12.0

37.0

116.0

14944589

Panel (a): Banks
Credit standards

Loan loss provisions
Panel (b): Firms

Notes: Descriptive statistics for the bank-firm sample included in the regression analysis.
Specifically, a one
standard deviation increase in the CET1 ratio leads to 0.2 standard deviations lower credit standards,
i.e., easier credit standards.
In their lending decisions, banks assess risks based on both their own
loss absorption capacity and the credit risk of their borrowers.
?Credit supply and monetary policy: Identifying the bank balance-sheet channel with loan applications.? American Economic
Review 102 (5):2301?2326.
?Hazardous times for monetary policy: What do twenty-three million bank loans say
about the effects of monetary policy on credit risk-taking?? Econometrica 82 (2):463?505.
?The credit cycle and the business cycle: new findings using
the loan officer opinion survey.? Journal of Money, Credit and Banking 38 (6):1575?1597.
guarantees: proxy from BLS, bank level
0

.1

.2

.3

.4

Government guarantees exposure

-.5

-.25

0

.25

.5

Government guarantees exposure

Notes: Based on results from columns (3) and (6) of Table 4.

Deposit market concentration and monetary transmission: evidence from the euro area

Retrieved on:

Dimanche, février 4, 2024

Neumark, American Economic Journal, Paper, A18, A21, Money, A4, Financial crisis, International, A5, A1, Exercise, Timothy, A14, Monetary policy, IRF, Interest rate, Economy, Quarterly Journal of Economics, NII, A8, Policy, HICP, MMF, Control, Statistics, Series, ECB, DNB, Index (economics), Finance, Section 3, GDP, History, American Economic Review, De Nederlandsche Bank, Rigidity, XB, Habit, Website, Federal funds, Social science, A11, 95.1 FM, IMF, MPS, A9, Credit, University of Amsterdam, Wang, A26, HHI, European Economic Review, Reproduction, Shock, University, Journal of Political Economy, Central bank, A30, Journal of Monetary Economics, Interval (mathematics), A25, G21, Politics, Auerbach, Silvana Tenreyro, XT, Research Papers in Economics, Causality, Deposit, Yb, A27, Time series, Dynamics, Literature, Hey, SSI, Interpolation, O'Driscoll (surname), BSI, A23, United, Domestic, European Central Bank, VAR, A28, Rigid transformation, A6, OIS, Role, PDF, Research, Feedback, The Journal of Finance, A15, A17, NFC, A22, E43, DSGE, Quarterly Journal, E52, MIR, Appendix, A10, Information, Surface finishing

Abstract

Key Points:

Abstract
I study the transmission of monetary policy to deposit rates in the euro area with a
focus on asymmetries and the role of banking sector concentration.
Moreover, the
gap between deposit rates across euro area member states - despite being exposed to the same
key ECB interest rates - has widened.
This begs the question whether deposit rates are more
sluggish in response to both policy rate increases and cuts, and what factors might influence the
transmission of monetary policy to deposit rates.
Whether banks are indeed able to adjust deposit rates asymmetrically to positive and
negative changes in policy rates could thus well depend on how much market power they hold
in the deposit market.
Arguing that market power increases in the degree of market concentration,
I further consider whether more concentrated banking sectors set rates (more) asymmetrically.
The response of deposit rates in banking sectors with an average degree of concentration does
not appear asymmetric.
The degree of market concentration is often pointed at, but recent evidence
for the euro area is scarce.
In this paper, I provide empirical evidence on the asymmetric response of deposit rates to
monetary policy, and relate this to the degree of concentration within a country?s banking sector.
Both papers
provide empirical evidence based on US deposit markets showing that deposit rates respond
more rigidly to upward changes in market rates than downward changes, especially so in more
concentrated markets.
Recent research on euro area deposit markets,
instead, has focused more on the transmission of negative policy rates (see e.g.
Whether banks are able to set deposit rates that materially differ from policy rates is affected
ECB Working Paper Series No 2896

4

by market concentration: market power is assumed to increase in the degree of concentration in
the banking sector.
Concentration thus appears to matter for how quickly ECB monetary policy has
been transmitted to deposit rates across the euro area.
Banks thus have a motive to be
rigid in adjusting deposit rates to a ?positive? monetary policy shock.
While customers are generally (and potentially rationally) inattentive, swift and substantial
nominal deposit rate declines may trigger deposit outflows.
relative deposit rate = deposit rate - short term rate
The inverse of the wedge, the relative deposit rate will allow us to see more clearly how
the deposit rate evolves in comparison to the short-term rate.
This then translates to (more
pronounced) effects on the transmission of policy to the deposit wedge, reinforcing the asymmetry discussed before.
More concentration would mean more rigid deposit rates (and thus an
increase in the deposit wedge) in case of positive surprises, and more flexible deposit rates (and
thus a decrease in the deposit wedge) in case of negative surprises (see also e.g.
I add an identical
altered-linex adjustment cost for deposit rates, to capture the upward rigidity and downward
flexibility of deposit rates as well.
As discussed
previously, the deposit rate is particularly rigid in case of a positive shock, illustrating the dividend smoothing motive and bank market power.
Without the asymmetric adjustment cost,
the response of the deposit rates to positive and negative changes in policy would have been
symmetric.
This appears a reasonable assumption
in general, as market concentration or market shares are slow-moving concepts.
3
Methods and data

I study the dynamic response to an unexpected change in monetary policy on deposit rates
in different countries in the euro area.
deposit rate - short-term rate), which for the sake of
brevity I will refer to as the ?relative deposit rate?.
Positive IRFs for the relative deposit rate imply that
the deposit rate has increased by more than the short-term rate, narrowing the wedge between
the short-term rate and the deposit rate.
0
?2
?2
?4
?6

?4
4

8

12

4

Months

8

12

Months

Figure 9: NFC rate response - linear combination of ?0 and ?1

Relative deposit rate at 1 month

Relative deposit rate at 4 months

0.0

0
?1

p.p.
0
0
?2
?1
?4
4

8

12

4

8

Months

12

Months

Figure 12: NFC rate response - linear combination of ?0 and ?1

Relative deposit rate at 1 month

Relative deposit rate at 4 months
2.0

1.5

p.p.
And, (2) how quickly
households and NFCs learn about changes in monetary policy, via the deposit rate, may vary
across the monetary union.
?0 , ?1 )
Figure A16: NFC overnight deposits, small member states
Relative deposit rate (average)

Relative deposit rate (interaction)

2

10
5

p.p.
?0 , ?1 )
Figure A19: NFC overnight deposits, four lags
Relative deposit rate (average)

Relative deposit rate (interaction)
5

0

p.p.
?0 , ?1 )
Figure A28: NFC overnight deposits, small member states
Relative deposit rate (average)

Relative deposit rate (interaction)

3

5.0

2

2.5

p.p.
?0 , ?1 )
Figure A31: NFC overnight deposits, four lags
Relative deposit rate (average)

Relative deposit rate (interaction)

3
2

p.p.