Internal Liquidity Management

Data - ECB

Author

Info

source	dataset	.html	.qmd	.RData
ecb	ILM	[2024-06-19]	https://fgee	olf.com/data

Data on monetary policy

source	dataset	Title	Download	Compile
ecb	ILM	Internal Liquidity Management	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_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	SUP	Supervisory Banking Statistics	2024-06-30	[2024-06-19]
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	[2024-06-19]
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_COMPILE

LAST_COMPILE
2024-07-01

Last

Code

ILM %>%
  group_by(TIME_PERIOD, FREQ) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  head(5) %>%
  print_table_conditional()

TIME_PERIOD	FREQ	Nobs
2024-W25	W	35
2024-W24	W	35
2024-W23	W	35
2024-W22	W	35
2024-W21	W	35

Info

Liquidity. html

FREQ

Code

ILM %>%
  left_join(FREQ,  by = "FREQ") %>%
  group_by(FREQ, Freq) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

FREQ	Freq	Nobs
M	Monthly	104058
W	Weekly	45351
D	Daily	20585

REF_AREA

Code

ILM %>%
  left_join(REF_AREA,  by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

BS_REP_SECTOR

Code

ILM %>%
  left_join(BS_REP_SECTOR,  by = "BS_REP_SECTOR") %>%
  group_by(BS_REP_SECTOR, Bs_rep_sector) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

BS_ITEM

Code

ILM %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  group_by(BS_ITEM, Bs_item) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

KEY

Code

ILM %>%
  group_by(KEY, TITLE) %>%
  summarise(Nobs = n()) %>%
  mutate(KEY = paste0('<a  target=_blank href=https://data.ecb.europa.eu/data/datasets/ILM/', KEY, ' >', KEY, '</a>')) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Daily

List of datasets

Code

ILM %>%
  filter(FREQ == "D") %>%
  group_by(KEY, TITLE) %>%
  summarise(Nobs = n()) %>%
  mutate(KEY = paste0("[", KEY, "](https://data.ecb.europa.eu/data/datasets/ILM/", KEY, ')')) %>%
  print_table_conditional()

KEY	TITLE	Nobs
[ILM.D.U2.C.A050500.U2.EUR]	https://data.ecb.europa.eu/data/datasets/ILM/ILM.D.U2.C.	050500
[ILM.D.U2.C.BMK1.U2.EUR](ht	ps://data.ecb.europa.eu/data/datasets/ILM/ILM.D.U2.C.BMK	.U2.EU
[ILM.D.U2.C.FAAF1.Z5.Z01](h	tps://data.ecb.europa.eu/data/datasets/ILM/ILM.D.U2.C.FA	F1.Z5.
[ILM.D.U2.C.FAAF2.Z5.Z01](h	tps://data.ecb.europa.eu/data/datasets/ILM/ILM.D.U2.C.FA	F2.Z5.
[ILM.D.U2.C.L020100.U2.EUR]	https://data.ecb.europa.eu/data/datasets/ILM/ILM.D.U2.C.	020100
[ILM.D.U2.C.L020200.U2.EUR]	https://data.ecb.europa.eu/data/datasets/ILM/ILM.D.U2.C.	020200

Main Refinancing operation

France, Germany, Italy

Code

ILM %>%
  filter(KEY %in% c("ILM.M.FR.N.A050100.U2.EUR",
                    "ILM.M.DE.N.A050100.U2.EUR",
                    "ILM.M.IT.N.A050100.U2.EUR")) %>%
  month_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
  ylab("") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 10000, 2),
                labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 1), "-01-01")),
               labels = date_format("%Y"))

Eurosystem

Monthly

Code

ILM %>%
  filter(KEY %in% c("ILM.M.U2.C.A050100.U2.EUR")) %>%
  month_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
  ylab("") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 10000, 20),
                labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

Weekly

Code

ILM %>%
  filter(KEY %in% c("ILM.W.U2.C.A050100.U2.EUR")) %>%
  arrange(desc(TIME_PERIOD)) %>%
  week_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE)) +
  ylab("Main refinancing operation - Eurosystem") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 10000, 20),
                labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

Total Assets

France, Germany, Italy

Code

ILM %>%
  filter(KEY %in% c("ILM.M.DE.N.T000000.Z5.Z01",
                    "ILM.M.FR.N.T000000.Z5.Z01",
                    "ILM.M.IT.N.T000000.Z5.Z01")) %>%
  month_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
  ylab("") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 10000, 200),
                labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 1), "-01-01")),
               labels = date_format("%Y"))

France, Germany, Italy

Code

ILM %>%
  filter(KEY %in% c("ILM.M.FR.N.L020000.U2.EUR",
                    "ILM.M.DE.N.L020000.U2.EUR",
                    "ILM.M.IT.N.L020000.U2.EUR")) %>%
  month_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
  ylab("") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 10000, 200),
                labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 1), "-01-01")),
               labels = date_format("%Y"))

Liabilities to euro area credit institutions related to monetary policy operations denominated in euro

Base money

Linear

Code

ILM %>%
  filter(KEY %in% c("ILM.M.U2.C.LT00001.Z5.EUR",
                    "ILM.M.U2.C.L020100.U2.EUR")) %>%
  month_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
  ylab("") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 10000, 500),
                labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

Log

Code

ILM %>%
  filter(KEY %in% c("ILM.M.U2.C.LT00001.Z5.EUR",
                    "ILM.M.U2.C.L020100.U2.EUR")) %>%
  month_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
  ylab("Required and Excess reserves") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = 10^(seq(0, 10, 1)),
                labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

Banknotes in circulation - Differences

Code

ILM %>%
  filter(KEY %in% c("ILM.M.4F.E.L010000.Z5.EUR",
                    "ILM.M.U2.C.L010000.Z5.EUR")) %>%
  month_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
  ylab("Liquidity-providing factors") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.45, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 10000, 200),
                labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

Liquidity

https://data.ecb.europa.eu/publications/ecbeurosystem-policy-and-exchange-rates/3030613

Liquidity-providing factors

Individual

Bn€

Code

ILM %>%
  filter(KEY %in% c("ILM.M.U2.C.AN00001.Z5.Z0Z",
                    "ILM.M.U2.C.A050100.U2.EUR",
                    "ILM.M.U2.C.A050200.U2.EUR",
                    "ILM.M.U2.C.A050500.U2.EUR",
                    "ILM.M.U2.C.A050A00.U2.EUR")) %>%
  month_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
  ylab("Liquidity-providing factors
") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.45, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 10000, 500),
                labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

Years of GDP

Code

ILM %>%
  filter(KEY %in% c("ILM.M.U2.C.AN00001.Z5.Z0Z",
                    "ILM.M.U2.C.A050100.U2.EUR",
                    "ILM.M.U2.C.A050200.U2.EUR",
                    "ILM.M.U2.C.A050500.U2.EUR",
                    "ILM.M.U2.C.A050A00.U2.EUR")) %>%
  month_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  left_join(B1GQ %>% mutate(date = date + months(3)), by = "date") %>%
  mutate(B1GQ_i = spline(x = date, y = B1GQ, xout = date)$y) %>%
  mutate(OBS_VALUE = OBS_VALUE/B1GQ_i) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
  ylab("Liquidity-providing factors (years of GDP)") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.4, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-5, 100, 5)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

Stacked

Bn€

Code

ILM %>%
  filter(KEY %in% c("ILM.M.U2.C.AN00001.Z5.Z0Z",
                    "ILM.M.U2.C.A050100.U2.EUR",
                    "ILM.M.U2.C.A050200.U2.EUR",
                    "ILM.M.U2.C.A050500.U2.EUR",
                    "ILM.M.U2.C.A050A00.U2.EUR")) %>%
  month_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  ggplot + geom_col(aes(x = date, y = OBS_VALUE, fill = TITLE)) +
  ylab("Liquidity-providing factors") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.45, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 10000, 500),
                labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

Years of GDP

Code

ILM %>%
  filter(KEY %in% c("ILM.M.U2.C.AN00001.Z5.Z0Z",
                    "ILM.M.U2.C.A050100.U2.EUR",
                    "ILM.M.U2.C.A050200.U2.EUR",
                    "ILM.M.U2.C.A050500.U2.EUR",
                    "ILM.M.U2.C.A050A00.U2.EUR")) %>%
  month_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  left_join(B1GQ %>% mutate(date = date + months(3)), by = "date") %>%
  mutate(B1GQ_i = spline(x = date, y = B1GQ, xout = date)$y) %>%
  mutate(OBS_VALUE = OBS_VALUE/B1GQ_i) %>%
  ggplot + geom_col(aes(x = date, y = OBS_VALUE, fill = TITLE)) +
  ylab("Liquidity-providing factors (years of GDP)") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.4, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-5, 100, 5)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

Liquidity-absorbing factors

Individual

Bn€

Code

ILM %>%
  filter(KEY %in% c("ILM.M.U2.C.L020200.U2.EUR",
                    "ILM.M.U2.C.L020300.U2.EUR",
                    "ILM.M.U2.C.L010000.Z5.EUR",
                    "ILM.M.U2.C.L050100.U2.EUR",
                    "ILM.M.U2.C.AN00002.Z5.Z0Z")) %>%
  month_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
  ylab("Liquidity-absorbing factors") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.45, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 10000, 500),
                labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

Years of GDP

Code

ILM %>%
  filter(KEY %in% c("ILM.M.U2.C.L020200.U2.EUR",
                    "ILM.M.U2.C.L020300.U2.EUR",
                    "ILM.M.U2.C.L010000.Z5.EUR",
                    "ILM.M.U2.C.L050100.U2.EUR",
                    "ILM.M.U2.C.AN00002.Z5.Z0Z")) %>%
  month_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  left_join(B1GQ %>% mutate(date = date + months(3)), by = "date") %>%
  mutate(B1GQ_i = spline(x = date, y = B1GQ, xout = date)$y) %>%
  mutate(OBS_VALUE = OBS_VALUE/B1GQ_i) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE)) +
  ylab("Liquidity-providing factors (years of GDP)") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.4, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-5, 100, 5)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

Stacked

Bn€

Code

ILM %>%
  filter(KEY %in% c("ILM.M.U2.C.L020200.U2.EUR",
                    "ILM.M.U2.C.L020300.U2.EUR",
                    "ILM.M.U2.C.L010000.Z5.EUR",
                    "ILM.M.U2.C.L050100.U2.EUR",
                    "ILM.M.U2.C.AN00002.Z5.Z0Z")) %>%
  month_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  ggplot + geom_col(aes(x = date, y = OBS_VALUE, fill = TITLE)) +
  ylab("Liquidity-absorbing factors") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.45, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 10000, 500),
                labels = dollar_format(acc = 1, pre = "", su = " Bn€")) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

Years of GDP

Code

ILM %>%
  filter(KEY %in% c("ILM.M.U2.C.L020200.U2.EUR",
                    "ILM.M.U2.C.L020300.U2.EUR",
                    "ILM.M.U2.C.L010000.Z5.EUR",
                    "ILM.M.U2.C.L050100.U2.EUR",
                    "ILM.M.U2.C.AN00002.Z5.Z0Z")) %>%
  month_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  left_join(B1GQ %>% mutate(date = date + months(3)), by = "date") %>%
  mutate(B1GQ_i = spline(x = date, y = B1GQ, xout = date)$y) %>%
  mutate(OBS_VALUE = OBS_VALUE/B1GQ_i) %>%
  ggplot + geom_col(aes(x = date, y = OBS_VALUE, fill = TITLE)) +
  ylab("Liquidity-providing factors (years of GDP)") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.4, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-5, 100, 5)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

Info

Data on monetary policy

LAST_COMPILE

Last

Info

FREQ

REF_AREA

BS_REP_SECTOR

BS_ITEM

KEY

Daily

List of datasets

Main Refinancing operation

France, Germany, Italy

Eurosystem

Monthly

Weekly

Total Assets

France, Germany, Italy

Liabilities related to monetary policy operations denominated in €

France, Germany, Italy

Base money

Linear

Log

Banknotes in circulation - Differences

Liquidity

Liquidity-providing factors

Individual

Bn€

Years of GDP

Stacked

Bn€

Years of GDP

Liquidity-absorbing factors

Individual

Bn€

Years of GDP

Stacked

Bn€

Years of GDP