Supervisory Banking Statistics

Data - ECB

Author

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Info

source	dataset	.html	.RData
ecb	SUP	2024-09-19	2024-10-08

Data on monetary policy

source	dataset	Title	Download	Compile
ecb	SUP	Supervisory Banking Statistics	2024-10-08	[2024-09-19]
bdf	FM	Marché financier, taux	2024-06-18	[2024-07-26]
bdf	MIR	Taux d'intérêt - Zone euro	2024-07-01	[2024-07-26]
bdf	MIR1	Taux d'intérêt - France	2024-07-01	[2024-07-26]
bis	CBPOL	Policy Rates, Daily	2024-09-15	[2024-08-09]
ecb	BSI	Balance Sheet Items	2024-09-16	[2024-10-08]
ecb	BSI_PUB	Balance Sheet Items - Published series	2024-10-08	[2024-10-08]
ecb	FM	Financial market data	2024-10-08	[2024-10-08]
ecb	ILM	Internal Liquidity Management	2024-10-08	[2024-10-08]
ecb	ILM_PUB	Internal Liquidity Management - Published series	2024-09-10	[2024-10-08]
ecb	MIR	MFI Interest Rate Statistics	2024-10-08	[2024-06-19]
ecb	RAI	Risk Assessment Indicators	2024-10-08	[2024-10-08]
ecb	YC	Financial market data - yield curve	2024-09-16	[2024-09-19]
ecb	YC_PUB	Financial market data - yield curve - Published series	2024-10-08	[2024-09-19]
ecb	liq_daily	Daily Liquidity	2024-09-11	[2024-10-08]
eurostat	ei_mfir_m	Interest rates - monthly data	2024-09-15	[2024-09-30]
eurostat	irt_st_m	Money market interest rates - monthly data	2024-10-08	[2024-09-30]
fred	r	Interest Rates	2024-09-18	[2024-09-18]
oecd	MEI	Main Economic Indicators	2024-06-30	[2024-04-16]
oecd	MEI_FIN	Monthly Monetary and Financial Statistics (MEI)	2024-05-21	[2024-09-15]

LAST_COMPILE

LAST_COMPILE
2024-10-09

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()

TIME_PERIOD	FREQ	Nobs
2024-S1	H	962
2024-Q2	Q	11949

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()

BS_SUFFIX	Bs_suffix	Nobs
E	Euro	289145
PCT	Percentage	95796
LAF	NA	27105
Z	Not applicable	13498

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()

CB_EXP_TYPE	Cb_exp_type	Nobs
ALL	All exposures	289526
_Z	Not applicable	98783
N_	Non-performing exposures	13610
ST2	Assets with significant increase in credit risk since initial recognition but not credit-impaired (Stage 2)	8120
P_	Performing exposures	4108
NFM	Non-performing exposures with forbearance measures	3736
PFM	Performing exposures with forbearance measures	3729
ST1	Assets without significant increase in credit risk since initial recognition (Stage 1)	1420
ST3	Credit-impaired assets (Stage 3)	1420
PCI	Purchased or originated credit-impaired financial assets	1092

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()

SBS_DI_1	Sbs_di_1	Nobs
SII	Significant institutions	334548
LSI	Less significant institutions	90996

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()

SBS_BREAKDOWN	Sbs_breakdown	Nobs
_T	Total	245402
AMC	Classification by business model - asset manager & custodian	10515
CWH	Classification by business model - corporate/wholesale lenders	10515
DEV	Classification by business model - development/promotional lenders	10515
DIV	Classification by business model - diversified lenders	10515
NC	Classification by business model - others/ not classified	10515
RCCL	Classification by business model - retail lenders and consumer credit lenders	10515
UNI	Classification by business model - universal and investment banks	10515
GSIB	Classification by size/business model - G-SIBs	7950
SL30	Classification by size - banks with total assets less than 30 billion of EUR	7950
SM20	Classification by size - banks with total assets more than 200 billion of EUR	7950
ST10	Classification by size - banks with total assets between 30 billion and 100 billion of EUR	7950
ST20	Classification by size - banks with total assets between 100 billion and 200 billion of EUR	7950
LORI	Classification by risk - banks with low risk	7647
MHRI	Classification by risk - banks with medium, high risk and non-rated	7647
SML	Classification by business model - small market lenders	7647
DOM	Classification by geographical diversification - banks with significant domestic exposures	7622
EEA	Classification by geographical diversification - banks with largest non-domestic exposures in non-SSM EEA	7622
NEEA	Classification by geographical diversification - banks with largest non-domestic exposures in non-EEA Europe	7622
ROW	Classification by geographical diversification - banks with largest non-domestic exposures in RoW	7622
SSM	Classification by geographical diversification - banks with largest non-domestic exposures in the SSM	7622
CSCB	Classification by business model-central savings and cooperative banks	2868
EML	Classification by business model-emerging markets lenders	2868

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()

FREQ	Freq	Nobs
Q	Quarterly	407310
H	Half-yearly	18234

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()

REF_AREA	Ref_area	Nobs
B01	EU countries participating in the Single Supervisory Mechanism (SSM) (changing composition)	204410
AT	Austria	10914
BE	Belgium	10914
CY	Cyprus	10914
DE	Germany	10914
EE	Estonia	10914
ES	Spain	10914
FI	Finland	10914
FR	France	10914
GR	Greece	10914
IE	Ireland	10914
IT	Italy	10914
LT	Lithuania	10914
LU	Luxembourg	10914
LV	Latvia	10914
MT	Malta	10914
NL	Netherlands	10914
PT	Portugal	10914
SI	Slovenia	10914
SK	Slovakia	10914
BG	Bulgaria	6884
HR	Croatia	6884

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()

COUNTERPART_SECTOR	Counterpart_sector	Nobs
_Z	Not applicable	338778
S13	General government	24786
S11	Non financial corporations	17065
S14	Households	15725
S12R	Other financial corporations	10125
S122Z	Deposit-taking corporations except the central bank and excluding electronic money institutions principally engaged in financial intermediation	6545
S121	Central bank	6514
S1V	Non-financial corporations, households and NPISH	6006

TIME_FORMAT

Code

SUP %>%
  group_by(TIME_FORMAT) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

TIME_FORMAT	Nobs
P3M	407310
P6M	18234

Performance Indicators

https://www.bankingsupervision.europa.eu/banking/statistics/html/index.en.html

Return on equity

Graph

significant institutions (SIs) and less significant institutions (LSIs):

Code

SUP %>%
  filter(grepl("Return on equity", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE"),
         BS_SUFFIX == "PCT") %>%
  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("Return on equity") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(7) + 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(-100, 100, 2),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

Cost-to-income ratio

Code

SUP %>%
  filter(grepl("Cost-to-income ratio", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE"),
         BS_SUFFIX == "PCT") %>%
  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("Cost-to-income Ratio") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(7) + 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, 100, 10),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

Net interest income

FR, IT, DE

Code

SUP %>%
  filter(grepl("Net interest income", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE"),
         BS_SUFFIX == "PCT") %>%
  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 Income/Total operating income") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(7) + 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, 100, 10),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

FR, IT, DE, ES, NL, SI

Net interest margin

FR, IT, DE

Code

SUP %>%
  filter(grepl("Net interest margin", 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, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

FR, IT, DE, ES, NL, SI

Code

SUP %>%
  filter(grepl("Net interest margin", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE", "ES", "NL", "SI"),
         SBS_DI_1 == "SII") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  quarter_to_date %>%
  arrange(desc(date)) %>%
  left_join(SBS_DI_1,  by = "SBS_DI_1") %>%
  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)) + 
  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, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

FR, IT, DE, ES, NL, BE, AT, PT

2015-

Code

SUP %>%
  filter(grepl("Net interest margin", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE", "ES", "NL", "BE", "AT", "PT"),
         SBS_DI_1 == "SII") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  quarter_to_date %>%
  arrange(desc(date)) %>%
  left_join(SBS_DI_1,  by = "SBS_DI_1") %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  mutate(color = ifelse(REF_AREA == "FR", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Net Interest Margin") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(8) + 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, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

2018-

Code

SUP %>%
  filter(grepl("Net interest margin", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE", "ES", "NL", "BE", "AT", "PT"),
         SBS_DI_1 == "SII") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2018-01-01")) %>%
  arrange(desc(date)) %>%
  left_join(SBS_DI_1,  by = "SBS_DI_1") %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  mutate(color = ifelse(REF_AREA == "FR", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Net Interest Margin") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(8) + 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, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

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

Common equity Tier 1 ratio
Tier 1 ratio

https://www.bankingsupervision.europa.eu/press/pr/date/2023/html/ssm.pr230111_{4cb4953fd6.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())