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Info
ecb
SUP
Supervisory Banking Statistics
2024-06-30
[2024-06-19]
bdf
FM
Marché financier, taux
2024-06-18
[2024-06-30]
bdf
MIR
Taux d'intérêt - Zone euro
2024-06-30
[2024-06-30]
bdf
MIR1
Taux d'intérêt - France
2024-06-30
[2024-06-30]
bis
CBPOL
Policy Rates, Daily
2024-06-07
[2024-06-19]
ecb
BSI
Balance Sheet Items
2024-05-21
[2024-06-30]
ecb
BSI_PUB
Balance Sheet Items - Published series
2024-06-30
[2024-06-30]
ecb
FM
Financial market data
2024-06-30
[2024-06-30]
ecb
ILM
Internal Liquidity Management
2024-06-30
[2024-06-30]
ecb
ILM_PUB
Internal Liquidity Management - Published series
2024-01-25
[2024-06-30]
ecb
MIR
MFI Interest Rate Statistics
2024-06-30
[2024-06-19]
ecb
RAI
Risk Assessment Indicators
2024-06-30
[2024-06-07]
ecb
YC
Financial market data - yield curve
2024-05-21
[2024-06-20]
ecb
YC_PUB
Financial market data - yield curve - Published series
2024-06-30
[2024-06-20]
ecb
liq_daily
Daily Liquidity
2024-05-21
[2024-06-30]
eurostat
ei_mfir_m
Interest rates - monthly data
2024-06-08
[2024-06-23]
eurostat
irt_st_m
Money market interest rates - monthly data
2024-06-30
[2024-06-23]
fred
r
Interest Rates
2024-06-30
[2024-06-30]
oecd
MEI
Main Economic Indicators
2024-06-30
[2024-04-16]
oecd
MEI_FIN
Monthly Monetary and Financial Statistics (MEI)
2024-05-21
[2024-06-20]
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 ()
2024-Q1
Q
11445
2023-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
270621
PCT
Percentage
89095
LAF
NA
26083
Z
Not applicable
13003
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
273999
_Z
Not applicable
91989
N_
Non-performing exposures
10285
ST2
Assets with significant increase in credit risk since initial recognition but not credit-impaired (Stage 2)
7616
P_
Performing exposures
3980
NFM
Non-performing exposures with forbearance measures
3630
PFM
Performing exposures with forbearance measures
3623
ST1
Assets without significant increase in credit risk since initial recognition (Stage 1)
1336
ST3
Credit-impaired assets (Stage 3)
1336
PCI
Purchased or originated credit-impaired financial assets
1008
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
313621
LSI
Less significant institutions
85181
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
229938
AMC
Classification by business model - asset manager & custodian
9849
CWH
Classification by business model - corporate/wholesale lenders
9849
DEV
Classification by business model - development/promotional lenders
9849
DIV
Classification by business model - diversified lenders
9849
NC
Classification by business model - others/ not classified
9849
RCCL
Classification by business model - retail lenders and consumer credit lenders
9849
UNI
Classification by business model - universal and investment banks
9849
GSIB
Classification by size/business model - G-SIBs
7457
SL30
Classification by size - banks with total assets less than 30 billion of EUR
7457
SM20
Classification by size - banks with total assets more than 200 billion of EUR
7457
ST10
Classification by size - banks with total assets between 30 billion and 100 billion of EUR
7457
ST20
Classification by size - banks with total assets between 100 billion and 200 billion of EUR
7457
LORI
Classification by risk - banks with low risk
7163
MHRI
Classification by risk - banks with medium, high risk and non-rated
7163
SML
Classification by business model - small market lenders
7163
DOM
Classification by geographical diversification - banks with significant domestic exposures
7155
EEA
Classification by geographical diversification - banks with largest non-domestic exposures in non-SSM EEA
7155
NEEA
Classification by geographical diversification - banks with largest non-domestic exposures in non-EEA Europe
7155
ROW
Classification by geographical diversification - banks with largest non-domestic exposures in RoW
7155
SSM
Classification by geographical diversification - banks with largest non-domestic exposures in the SSM
7155
CSCB
Classification by business model-central savings and cooperative banks
2686
EML
Classification by business model-emerging markets lenders
2686
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
381530
H
Half-yearly
17272
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)
191921
AT
Austria
10233
BE
Belgium
10233
CY
Cyprus
10233
DE
Germany
10233
EE
Estonia
10233
ES
Spain
10233
FI
Finland
10233
FR
France
10233
GR
Greece
10233
IE
Ireland
10233
IT
Italy
10233
LT
Lithuania
10233
LU
Luxembourg
10233
LV
Latvia
10233
MT
Malta
10233
NL
Netherlands
10233
PT
Portugal
10233
SI
Slovenia
10233
SK
Slovakia
10233
BG
Bulgaria
6227
HR
Croatia
6227
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
324410
S13
General government
23529
S11
Non financial corporations
12125
S14
Households
10869
S12R
Other financial corporations
9613
S122Z
Deposit-taking corporations except the central bank and excluding electronic money institutions principally engaged in financial intermediation
6250
S121
Central bank
6219
S1V
Non-financial corporations, households and NPISH
5787
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 ())