Supervisory Banking Statistics

Data - ECB

Author

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Info

source	dataset	.html	.RData
ecb	SUP	2024-12-28	2024-12-29

Data on monetary policy

source	dataset	Title	Download	Compile
ecb	SUP	Supervisory Banking Statistics	2024-12-29	[2024-12-28]
bdf	FM	Marché financier, taux	2024-12-28	[2024-12-28]
bdf	MIR	Taux d'intérêt - Zone euro	2024-07-01	[2024-07-26]
bdf	MIR1	Taux d'intérêt - France	2024-12-09	[2024-11-29]
bis	CBPOL	Policy Rates, Daily	2024-12-29	[2024-12-19]
ecb	BSI	Balance Sheet Items	2024-11-19	[2024-12-29]
ecb	BSI_PUB	Balance Sheet Items - Published series	2024-12-29	[2024-12-29]
ecb	FM	Financial market data	2024-12-29	[2024-12-29]
ecb	ILM	Internal Liquidity Management	2024-12-29	[2024-12-29]
ecb	ILM_PUB	Internal Liquidity Management - Published series	2024-09-10	[2024-12-29]
ecb	MIR	MFI Interest Rate Statistics	2024-12-29	[2024-06-19]
ecb	RAI	Risk Assessment Indicators	2024-12-29	[2024-12-29]
ecb	YC	Financial market data - yield curve	2024-11-19	[2024-12-28]
ecb	YC_PUB	Financial market data - yield curve - Published series	2024-12-29	[2024-12-28]
ecb	liq_daily	Daily Liquidity	2024-09-11	[2024-12-29]
eurostat	ei_mfir_m	Interest rates - monthly data	2024-12-28	[2024-12-28]
eurostat	irt_st_m	Money market interest rates - monthly data	2024-12-29	[2024-12-28]
fred	r	Interest Rates	2024-12-29	[2024-12-29]
oecd	MEI	Main Economic Indicators	2024-06-30	[2024-04-16]
oecd	MEI_FIN	Monthly Monetary and Financial Statistics (MEI)	2024-12-22	[2024-09-15]

LAST_COMPILE

LAST_COMPILE
2024-12-29

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	1768
2024-Q3	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 .}

Show entries

Search:

TITLE	Nobs
	17 18872
Loans and advances (excluding cash balances at central banks and other demand deposits), SIs	18872
Credit risk exposure , SIs	17072
Risk weights, SIs	14866
Deposits, SIs	12416
Government exposures, SIs	11896
Number of supervised institutions by band, SIs	9648
Loans and advances subject to impairment review, SIs	7868
Coverage ratio, SIs	7695
Government exposures, LSIs	7144
Operational risk exposure, SIs	6208

Showing 1 to 10 of 326 entries

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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	306353
PCT	Percentage	101917
LAF	NA	28127
Z	Not applicable	13993

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	304317
_Z	Not applicable	104997
N_	Non-performing exposures	16355
ST2	Assets with significant increase in credit risk since initial recognition but not credit-impaired (Stage 2)	8624
P_	Performing exposures	4236
NFM	Non-performing exposures with forbearance measures	3842
PFM	Performing exposures with forbearance measures	3835
ST1	Assets without significant increase in credit risk since initial recognition (Stage 1)	1504
ST3	Credit-impaired assets (Stage 3)	1504
PCI	Purchased or originated credit-impaired financial assets	1176

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()

Show entries

Search:

CB_ITEM	Cb_item	Nobs
		38 25282
E0010	Amount of exposures - total	25282
L1150	Deposits (balance sheet)	16600
A1140	Loans and advances (excluding cash balances at central banks and other demand deposits)	13462
E0035	Amount of exposures - loans and advances (excluding cash balances at central banks and other demand deposits)	10810
AQ100	Loans and advances subject to impairment review	7868
I3645	Coverage ratio - total exposures	6242
A1200	Debt securities	5635
I7005	Non-performing loans ratio (excluding cash balances at central banks and other demand deposits)	5104
E0036		5100
I7500	Stage 2 loans and advances (% of loans and advances subject to impairment review)	5024

Showing 1 to 10 of 249 entries

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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	346497
LSI	Less significant institutions	103893

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	261130
AMC	Classification by business model - asset manager & custodian	11193
CWH	Classification by business model - corporate/wholesale lenders	11193
DEV	Classification by business model - development/promotional lenders	11193
DIV	Classification by business model - diversified lenders	11193
NC	Classification by business model - others/ not classified	11193
RCCL	Classification by business model - retail lenders and consumer credit lenders	11193
UNI	Classification by business model - universal and investment banks	11193
GSIB	Classification by size/business model - G-SIBs	8225
SL30	Classification by size - banks with total assets less than 30 billion of EUR	8225
SM20	Classification by size - banks with total assets more than 200 billion of EUR	8225
ST10	Classification by size - banks with total assets between 30 billion and 100 billion of EUR	8225
ST20	Classification by size - banks with total assets between 100 billion and 200 billion of EUR	8225
LORI	Classification by risk - banks with low risk	7913
MHRI	Classification by risk - banks with medium, high risk and non-rated	7913
SML	Classification by business model - small market lenders	7913
DOM	Classification by geographical diversification - banks with significant domestic exposures	7897
EEA	Classification by geographical diversification - banks with largest non-domestic exposures in non-SSM EEA	7897
NEEA	Classification by geographical diversification - banks with largest non-domestic exposures in non-EEA Europe	7897
ROW	Classification by geographical diversification - banks with largest non-domestic exposures in RoW	7897
SSM	Classification by geographical diversification - banks with largest non-domestic exposures in the SSM	7897
CSCB	Classification by business model-central savings and cooperative banks	3280
EML	Classification by business model-emerging markets lenders	3280

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()

Show entries

Search:

COUNT_AREA	Count_area	Nobs
		112 408305
W0	World (all entities, including reference area, including IO)	408305
_Z	Not applicable	13993
AT	Austria	1448
BE	Belgium	1448
DE	Germany	1448
ES	Spain	1448
FR	France	1448
IE	Ireland	1448
IT	Italy	1448
LU	Luxembourg	1448

Showing 1 to 10 of 33 entries

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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	431350
H	Half-yearly	19040

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)	214751
AT	Austria	11607
BE	Belgium	11607
CY	Cyprus	11607
DE	Germany	11607
EE	Estonia	11607
ES	Spain	11607
FI	Finland	11607
FR	France	11607
GR	Greece	11607
IE	Ireland	11607
IT	Italy	11607
LT	Lithuania	11607
LU	Luxembourg	11607
LV	Latvia	11607
MT	Malta	11607
NL	Netherlands	11607
PT	Portugal	11607
SI	Slovenia	11607
SK	Slovakia	11607
BG	Bulgaria	7553
HR	Croatia	7553

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	353146
S13	General government	25887
S11	Non financial corporations	21135
S14	Households	19711
S12R	Other financial corporations	10637
S122Z	Deposit-taking corporations except the central bank and excluding electronic money institutions principally engaged in financial intermediation	6840
S121	Central bank	6809
S1V	Non-financial corporations, households and NPISH	6225

TIME_FORMAT

Code

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

TIME_FORMAT	Nobs
P3M	431350
P6M	19040

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())