Internal Liquidity Management - ILM

Data - ECB

Last observation: 2026-W34 (N = 43) · 30 août 2026 (N = 4) · juil. 2026 (N = 49)

First observation: 31 déc. 1998 (N = 3) · 1998-W53 (N = 32) · mars 1999 (N = 26)

Last data update: 01 sept. 2026, 02:21

Last compile: 02 sept. 2026, 23:15

Structure

FREQ

REF_AREA

BS_REP_SECTOR

BS_ITEM

COUNT_AREA

CURRENCY_TRANS

OBS_STATUS

OBS_CONF

COLLECTION

DECIMALS

UNIT

UNIT_MULT

Info

• Liquidity. html

Main Refinancing operation

France, Germany, Italy

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, 2100,  1), "-01-01")),
               labels = date_format("%Y"))

Eurosystem

Monthly

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, 2100,  2), "-01-01")),
               labels = date_format("%Y"))

Weekly

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, 2100,  2), "-01-01")),
               labels = date_format("%Y"))

Total Assets

France, Germany, Italy

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, 2100,  1), "-01-01")),
               labels = date_format("%Y"))

Liabilities related to monetary policy operations denominated in €

ILM.W.U2.C.L020000.U2.EUR

France, Germany, Italy

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, 2100,  1), "-01-01")),
               labels = date_format("%Y"))

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

Base money

Linear

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, 2100,  2), "-01-01")),
               labels = date_format("%Y"))

Log

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, 2100,  2), "-01-01")),
               labels = date_format("%Y"))

Banknotes in circulation - Differences

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, 2100,  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€

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, 2100,  2), "-01-01")),
               labels = date_format("%Y"))

Years of GDP

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, 2100,  2), "-01-01")),
               labels = date_format("%Y"))

Stacked

Bn€

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) |>
  arrange(date) |>
  mutate(TITLE = ifelse(BS_ITEM == "A050A00", "Other liquidity providing operations", TITLE),
         TITLE = gsub(" - Eurosystem", "", TITLE)) |>
  ggplot() + geom_area(aes(x = date, y = OBS_VALUE, fill = TITLE), alpha = 0.5) +
  ylab("Liquidity-providing factors") + xlab("") + theme_minimal() +
  theme(legend.position = c(0.3, 0.75),
        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, 2100,  2), "-01-01")),
               labels = date_format("%Y"))

Years of GDP

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_area(aes(x = date, y = OBS_VALUE, fill = TITLE), alpha = 0.5) +
  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, 2100,  2), "-01-01")),
               labels = date_format("%Y"))

Liquidity-absorbing factors

Individual

Bn€

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, 2100,  2), "-01-01")),
               labels = date_format("%Y"))

Years of GDP

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, 2100,  2), "-01-01")),
               labels = date_format("%Y"))

Stacked

Bn€

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, 2100,  2), "-01-01")),
               labels = date_format("%Y"))

Years of GDP

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, 2100,  2), "-01-01")),
               labels = date_format("%Y"))

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