Internal Liquidity Management

Data - ECB

Info

source dataset .html .qmd .RData
ecb ILM [2024-09-18] https://fgee olf.com/data

Data on monetary policy

source dataset Title Download Compile
ecb ILM Internal Liquidity Management 2024-10-08 [2024-09-18]
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_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 [2024-09-19]
ecb SUP Supervisory Banking Statistics 2024-10-08 [2024-09-19]
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 [2024-09-18]
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
ILM %>%
  group_by(TIME_PERIOD, FREQ) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  head(5) %>%
  print_table_conditional()
TIME_PERIOD FREQ Nobs
2024-W39 W 35
2024-W38 W 35
2024-W37 W 35
2024-W36 W 35
2024-W35 W 35

Info

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 108492
W Weekly 45841
D Daily 20813

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"))