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Info
ecb
SUP
Supervisory Banking Statistics
2025-08-28
[2025-08-28]
bdf
FM
Marché financier, taux
2025-08-28
[2025-08-28]
bdf
MIR
Taux d'intérêt - Zone euro
2025-08-04
[2025-08-28]
bdf
MIR1
Taux d'intérêt - France
2025-08-04
[2025-08-28]
bis
CBPOL
Policy Rates, Daily
2025-08-28
[2025-08-28]
ecb
BSI
Balance Sheet Items
NA
[2025-08-29]
ecb
BSI_PUB
Balance Sheet Items - Published series
NA
[2025-08-29]
ecb
FM
Financial market data
2025-08-28
[2025-08-29]
ecb
ILM
Internal Liquidity Management
NA
[2025-08-29]
ecb
ILM_PUB
Internal Liquidity Management - Published series
2024-09-10
[2025-08-29]
ecb
MIR
MFI Interest Rate Statistics
2025-08-28
[2025-08-29]
ecb
RAI
Risk Assessment Indicators
2025-08-28
[2025-08-29]
ecb
YC
Financial market data - yield curve
NA
[2025-08-29]
ecb
YC_PUB
Financial market data - yield curve - Published series
NA
[2025-08-28]
ecb
liq_daily
Daily Liquidity
2025-06-06
[2025-08-29]
eurostat
ei_mfir_m
Interest rates - monthly data
2025-08-28
[2025-08-28]
eurostat
irt_st_m
Money market interest rates - monthly data
2025-08-28
[2025-08-28]
fred
r
Interest Rates
2025-08-28
[2025-08-28]
oecd
MEI
Main Economic Indicators
2025-07-24
[2024-04-16]
oecd
MEI_FIN
Monthly Monetary and Financial Statistics (MEI)
2025-07-24
[2024-09-15]
Last
Code
SUP %>%
group_by (TIME_PERIOD, FREQ) %>%
summarise (Nobs = n ()) %>%
ungroup %>%
group_by (FREQ) %>%
arrange (desc (TIME_PERIOD)) %>%
filter (row_number () == 1 ) %>%
print_table_conditional ()
2025-Q1
Q
11879
2024-S2
H
1768
Info
Data Structure Definition (DSD). html
TITLE
Code
SUP %>%
group_by (TITLE) %>%
summarise (Nobs = n ()) %>%
arrange (- Nobs) %>%
{if (is_html_output ()) datatable (., filter = 'top' , rownames = F) else .}
BS_SUFFIX
Code
SUP %>%
left_join (BS_SUFFIX, by = "BS_SUFFIX" ) %>%
group_by (BS_SUFFIX, Bs_suffix) %>%
summarise (Nobs = n ()) %>%
arrange (- Nobs) %>%
print_table_conditional ()
E
Euro
332941
PCT
Percentage
110265
LAF
NA
30171
Z
Not applicable
14983
CB_EXP_TYPE
Code
SUP %>%
left_join (CB_EXP_TYPE, by = "CB_EXP_TYPE" ) %>%
group_by (CB_EXP_TYPE, Cb_exp_type) %>%
summarise (Nobs = n ()) %>%
arrange (- Nobs) %>%
print_table_conditional ()
ALL
All exposures
330007
_Z
Not applicable
113531
N_
Non-performing exposures
17909
ST2
Assets with significant increase in credit risk since initial recognition but not credit-impaired (Stage 2)
9632
P_
Performing exposures
4492
NFM
Non-performing exposures with forbearance measures
4054
PFM
Performing exposures with forbearance measures
4047
ST1
Assets without significant increase in credit risk since initial recognition (Stage 1)
1672
ST3
Credit-impaired assets (Stage 3)
1672
PCI
Purchased or originated credit-impaired financial assets
1344
CB_ITEM
Code
SUP %>%
left_join (CB_ITEM, by = "CB_ITEM" ) %>%
group_by (CB_ITEM, Cb_item) %>%
summarise (Nobs = n ()) %>%
arrange (- Nobs) %>%
print_table_conditional ()
SBS_DI_1
Code
SUP %>%
left_join (SBS_DI_1, by = "SBS_DI_1" ) %>%
group_by (SBS_DI_1, Sbs_di_1) %>%
summarise (Nobs = n ()) %>%
arrange (- Nobs) %>%
print_table_conditional ()
SII
Significant institutions
371287
LSI
Less significant institutions
117073
SBS_BREAKDOWN
Code
SUP %>%
left_join (SBS_BREAKDOWN, by = "SBS_BREAKDOWN" ) %>%
group_by (SBS_BREAKDOWN, Sbs_breakdown) %>%
summarise (Nobs = n ()) %>%
arrange (- Nobs) %>%
print_table_conditional ()
_T
Total
284224
AMC
Classification by business model - asset manager & custodian
12162
CWH
Classification by business model - corporate/wholesale lenders
12162
DEV
Classification by business model - development/promotional lenders
12162
DIV
Classification by business model - diversified lenders
12162
NC
Classification by business model - others/ not classified
12162
RCCL
Classification by business model - retail lenders and consumer credit lenders
12162
UNI
Classification by business model - universal and investment banks
12162
GSIB
Classification by size/business model - G-SIBs
8798
SL30
Classification by size - banks with total assets less than 30 billion of EUR
8798
SM20
Classification by size - banks with total assets more than 200 billion of EUR
8798
ST10
Classification by size - banks with total assets between 30 billion and 100 billion of EUR
8798
ST20
Classification by size - banks with total assets between 100 billion and 200 billion of EUR
8798
LORI
Classification by risk - banks with low risk
8468
MHRI
Classification by risk - banks with medium, high risk and non-rated
8468
SML
Classification by business model - small market lenders
8468
DOM
Classification by geographical diversification - banks with significant domestic exposures
8444
EEA
Classification by geographical diversification - banks with largest non-domestic exposures in non-SSM EEA
8444
NEEA
Classification by geographical diversification - banks with largest non-domestic exposures in non-EEA Europe
8444
ROW
Classification by geographical diversification - banks with largest non-domestic exposures in RoW
8444
SSM
Classification by geographical diversification - banks with largest non-domestic exposures in the SSM
8444
CSCB
Classification by business model-central savings and cooperative banks
3694
EML
Classification by business model-emerging markets lenders
3694
COUNT_AREA
Code
SUP %>%
left_join (COUNT_AREA, by = "COUNT_AREA" ) %>%
group_by (COUNT_AREA, Count_area) %>%
summarise (Nobs = n ()) %>%
arrange (- Nobs) %>%
print_table_conditional ()
FREQ
Code
SUP %>%
left_join (FREQ, by = "FREQ" ) %>%
group_by (FREQ, Freq) %>%
summarise (Nobs = n ()) %>%
arrange (- Nobs) %>%
print_table_conditional ()
Q
Quarterly
467552
H
Half-yearly
20808
REF_AREA
Code
SUP %>%
left_join (REF_AREA, by = "REF_AREA" ) %>%
group_by (REF_AREA, Ref_area) %>%
summarise (Nobs = n ()) %>%
arrange (- Nobs) %>%
print_table_conditional ()
B01
EU countries participating in the Single Supervisory Mechanism (SSM) (changing composition)
231742
AT
Austria
12606
BE
Belgium
12606
CY
Cyprus
12606
DE
Germany
12606
EE
Estonia
12606
ES
Spain
12606
FI
Finland
12606
FR
France
12606
GR
Greece
12606
IE
Ireland
12606
IT
Italy
12606
LT
Lithuania
12606
LU
Luxembourg
12606
LV
Latvia
12606
MT
Malta
12606
NL
Netherlands
12606
PT
Portugal
12606
SI
Slovenia
12606
SK
Slovakia
12606
BG
Bulgaria
8552
HR
Croatia
8552
COUNTERPART_SECTOR
Code
SUP %>%
left_join (COUNTERPART_SECTOR, by = "COUNTERPART_SECTOR" ) %>%
group_by (COUNTERPART_SECTOR, Counterpart_sector) %>%
summarise (Nobs = n ()) %>%
arrange (- Nobs) %>%
print_table_conditional ()
_Z
Not applicable
381812
S13
General government
28245
S11
Non financial corporations
23371
S14
Households
21779
S12R
Other financial corporations
11661
S122Z
Deposit-taking corporations except the central bank and excluding electronic money institutions principally engaged in financial intermediation
7430
S121
Central bank
7399
S1V
Non-financial corporations, households and NPISH
6663
Liquidity
Liquidity coverage ratios (LCR)
The LCR is the percentage resulting from dividing the bank’s stock of high-quality assets by the estimated total net cash outflows over a 30 calendar day stress scenario.
Code
SUP %>%
filter (CB_ITEM == "I3017" ,
REF_AREA %in% c ("U2" , "FR" , "IT" , "DE" ),
! (SBS_DI_1 == "_Z" )) %>%
left_join (REF_AREA, by = "REF_AREA" ) %>%
left_join (SBS_DI_1, by = "SBS_DI_1" ) %>%
quarter_to_date %>%
select_if (~ n_distinct (.) > 1 ) %>%
arrange (desc (date)) %>%
left_join (colors, by = c ("Ref_area" = "country" )) %>%
mutate (OBS_VALUE = OBS_VALUE/ 100 ) %>%
ggplot (.) + theme_minimal () + xlab ("" ) + ylab ("Liquidity coverage ratio" ) +
geom_line (aes (x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) +
add_flags (6 ) + scale_color_identity () +
scale_x_date (breaks = seq (1960 , 2030 , 1 ) %>% paste0 ("-01-01" ) %>% as.Date,
labels = date_format ("%Y" )) +
scale_y_continuous (breaks = 0.01 * seq (0 , 500 , 50 ),
labels = percent_format (accuracy = 1 ),
limits = c (0 , 3.5 )) +
theme (legend.position = c (0.2 , 0.90 ),
legend.title = element_blank ())
Liquidity buffer
Code
SUP %>%
filter (CB_ITEM == "A6310" ,
REF_AREA %in% c ("U2" , "FR" , "IT" , "DE" )) %>%
left_join (REF_AREA, by = "REF_AREA" ) %>%
left_join (SBS_DI_1, by = "SBS_DI_1" ) %>%
quarter_to_date %>%
select_if (~ n_distinct (.) > 1 ) %>%
arrange (desc (date)) %>%
left_join (colors, by = c ("Ref_area" = "country" )) %>%
mutate (OBS_VALUE = OBS_VALUE/ 100 ) %>%
ggplot (.) + theme_minimal () + xlab ("" ) + ylab ("Liquidity buffer" ) +
geom_line (aes (x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) +
add_flags (6 ) + scale_color_identity () +
scale_x_date (breaks = seq (1960 , 2030 , 1 ) %>% paste0 ("-01-01" ) %>% as.Date,
labels = date_format ("%Y" )) +
scale_y_continuous (breaks = seq (0 , 1000 , 2 )) +
theme (legend.position = c (0.2 , 0.90 ),
legend.title = element_blank ())
Net liquidity outflow
Code
SUP %>%
filter (CB_ITEM == "A6320" ,
REF_AREA %in% c ("U2" , "FR" , "IT" , "DE" )) %>%
left_join (REF_AREA, by = "REF_AREA" ) %>%
left_join (SBS_DI_1, by = "SBS_DI_1" ) %>%
quarter_to_date %>%
select_if (~ n_distinct (.) > 1 ) %>%
arrange (desc (date)) %>%
left_join (colors, by = c ("Ref_area" = "country" )) %>%
mutate (OBS_VALUE = OBS_VALUE/ 100 ) %>%
ggplot (.) + theme_minimal () + xlab ("" ) + ylab ("Net liquidity outflow" ) +
geom_line (aes (x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) +
add_flags (6 ) + scale_color_identity () +
scale_x_date (breaks = seq (1960 , 2030 , 1 ) %>% paste0 ("-01-01" ) %>% as.Date,
labels = date_format ("%Y" )) +
scale_y_continuous (breaks = seq (0 , 1000 , 2 )) +
theme (legend.position = c (0.2 , 0.90 ),
legend.title = element_blank ())
Net liquidity outflow
Code
SUP %>%
filter (grepl ("Net liquidity outflow" , TITLE),
REF_AREA %in% c ("B01" , "FR" , "IT" , "DE" )) %>%
left_join (REF_AREA, by = "REF_AREA" ) %>%
left_join (SBS_DI_1, by = "SBS_DI_1" ) %>%
quarter_to_date %>%
mutate (OBS_VALUE = OBS_VALUE/ 100 ,
Ref_area = ifelse (REF_AREA == "B01" , "Europe" , Ref_area)) %>%
select_if (~ n_distinct (.) > 1 ) %>%
left_join (colors, by = c ("Ref_area" = "country" )) %>%
na.omit %>%
ggplot (.) + theme_minimal () + xlab ("" ) + ylab ("Net liquidity outflow" ) +
geom_line (aes (x = date, y = OBS_VALUE, color = color, linetype = paste0 (SBS_DI_1, SBS_BREAKDOWN))) +
add_flags (6 ) + scale_color_identity () +
scale_x_date (breaks = seq (1960 , 2030 , 1 ) %>% paste0 ("-01-01" ) %>% as.Date,
labels = date_format ("%Y" )) +
scale_y_continuous (breaks = 0.01 * seq (0 , 500 , 50 ),
labels = percent_format (accuracy = 1 ),
limits = c (0 , 3.5 )) +
theme (legend.position = c (0.55 , 0.50 ),
legend.title = element_blank ())
Net Liquidity outflow - EU
B01 - EU countries participating in the Single Supervisory Mechanism (SSM)
Capital adequacy
https://www.bankingsupervision.europa.eu/press/pr/date/2023/html/ssm.pr2301114cb4953fd6.en.html#: :text=The%20aggregate%20capital%20ratios%20of,capital%20ratio%20stood%20at%2018.68%25.
Code
SUP %>%
filter (grepl ("Common equity Tier 1 ratio" , TITLE),
REF_AREA %in% c ("U2" , "FR" , "IT" , "DE" )) %>%
left_join (REF_AREA, by = "REF_AREA" ) %>%
left_join (SBS_DI_1, by = "SBS_DI_1" ) %>%
quarter_to_date %>%
select_if (~ n_distinct (.) > 1 ) %>%
left_join (colors, by = c ("Ref_area" = "country" )) %>%
mutate (OBS_VALUE = OBS_VALUE/ 100 ) %>%
ggplot (.) + theme_minimal () + xlab ("" ) + ylab ("Net Interest Margin" ) +
geom_line (aes (x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) +
add_flags (6 ) + scale_color_identity () +
scale_x_date (breaks = seq (1960 , 2030 , 1 ) %>% paste0 ("-01-01" ) %>% as.Date,
labels = date_format ("%Y" )) +
scale_y_continuous (breaks = 0.01 * seq (- 10 , 50 , 1 ),
labels = percent_format (accuracy = .1 )) +
theme (legend.position = c (0.25 , 0.90 ),
legend.title = element_blank ())