Financial market data

Data - ECB

Author

François Geerolf 

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Info

source dataset Title .html .rData
ecb FM Financial market data 2026-06-03 2026-06-04

LAST_COMPILE

LAST_COMPILE
2026-06-04

Last

TIME_PERIOD FREQ Nobs
2026-Q1 Q 23
2026-06-04 D 6
2026-05 M 34
2025-06-11 B 6
2025 A 24

Last Day

TITLE TIME_PERIOD OBS_VALUE
Deposit facility - date of changes (raw data) - Change in percentage points compared to previous rate 2026-06-04 -0.25
Deposit facility - date of changes (raw data) - Level 2026-06-04 2.00
Marginal lending facility - date of changes (raw data) - Change in percentage points compared to previous rate 2026-06-04 -0.25
Marginal lending facility - date of changes (raw data) - Level 2026-06-04 2.40
Main refinancing operations - fixed rate tenders (fixed rate) (date of changes) - Level 2026-06-04 2.15
Main refinancing operations - Minimum bid rate/fixed rate (date of changes) - Level 2026-06-04 2.15

PROVIDER_FM_ID

Code 
FM %>%
  left_join(PROVIDER_FM_ID,  by = "PROVIDER_FM_ID") %>%
  group_by(PROVIDER_FM_ID, PROVIDER_FM_ID_desc) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

FREQ

Code 
FM %>%
  left_join(FREQ,  by = "FREQ") %>%
  group_by(FREQ, Freq) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) print_table(.) else .}
FREQ Freq Nobs
A Annual 930
B Daily - businessweek 6306
D Daily 60098
M Monthly 21703
Q Quarterly 3765

REF_AREA

Code 
FM %>%
  left_join(REF_AREA,  by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area) %>%
  summarise(Nobs = n()) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

CURRENCY

Code 
FM %>%
  left_join(CURRENCY,  by = "CURRENCY") %>%
  group_by(CURRENCY, Currency) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
CURRENCY Currency Nobs
EUR Euro 75248
GBP UK pound sterling 6741
USD US dollar 6312
JPY Japanese yen 3630
DKK Danish krone 452
SEK Swedish krona 419

PROVIDER_FM

Code 
FM %>%
  left_join(PROVIDER_FM,  by = "PROVIDER_FM") %>%
  group_by(PROVIDER_FM, Provider_fm) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
PROVIDER_FM Provider_fm Nobs
4F ECB 76765
DS DataStream 10011
RT Reuters 6026

INSTRUMENT_FM

Code 
FM %>%
  left_join(INSTRUMENT_FM,  by = "INSTRUMENT_FM") %>%
  group_by(INSTRUMENT_FM, Instrument_fm) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
INSTRUMENT_FM Instrument_fm Nobs
KR Key interest rate 60685
MM Money Market 12180
EI Equity/index 9140
BB Benchmark bond 7948
CY Commodity 1135
BZ Zero-coupon yield bond 887
SP Spread 827

DATA_TYPE_FM

Code 
FM %>%
  left_join(DATA_TYPE_FM,  by = "DATA_TYPE_FM") %>%
  group_by(DATA_TYPE_FM, Data_type_fm) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
DATA_TYPE_FM Data_type_fm Nobs
LEV Level 40269
CHG Change in percentage points compared to previous rate 20215
HSTA Historical close, average of observations through period 15865
SPR Spread 5920
YLDA Yield, average of observations through period 4563
YLD Yield 3385
YLDE Yield, end of period 887
ASKA Ask price or primary activity, average of observations through period 871
SPRE Spread, end of period 827

COLLECTION

Code 
FM %>%
  left_join(COLLECTION,  by = "COLLECTION") %>%
  group_by(COLLECTION, Collection) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) print_table(.) else .}
COLLECTION Collection Nobs
A Average of observations through period 21320
E End of period 71482

UNIT

Code 
FM %>%
  left_join(UNIT,  by = "UNIT") %>%
  group_by(UNIT, Unit) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) print_table(.) else .}
UNIT Unit Nobs
PC Percent 20215
PCPA Percent per annum 63283
POINTS Points 9140
USD US dollar 164

TITLE

Code 
FM %>%
  group_by(TITLE) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Real Rates

USA, Euro Area

1999

Code 
plot <- FM %>%
  filter(PROVIDER_FM_ID %in% c("R_US10YT_RR", "R_U2_10Y")) %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Zone euro", "États-Unis")) %>%
  filter(date >= as.Date("1999-01-01")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Taux d'intérêt réels à 10 ans") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Ref_area)) + 
  scale_color_manual(values = c("#B22234", "#003399")) +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1999-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin =-Inf, ymax = +Inf), 
            fill = '#B22234', alpha = 0.1)  +
  geom_rect(data = cepr_recessions %>%
              filter(Peak > as.Date("1999-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = '#003399', alpha = 0.1)  +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.26, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 1),
                     labels = percent_format(accuracy = 1)) +
  labs(caption = "Source: BCE, Données sur les marchés financiers")
save(plot, file = "FM_files/figure-html/real-rates-USA-EUR-1999-1.RData")
plot

USA, Japan, Euro Area

All

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("R_US10YT_RR", "R_JP10YT_RR", "R_U2_10Y")) %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(REF_AREA == "U2", color2, color)) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Real rates, 10-year") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1900, 2100, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 1),
                     labels = percent_format(accuracy = 1))

2010-

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("R_US10YT_RR", "R_JP10YT_RR", "R_U2_10Y")) %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
  filter(date >= as.Date("2010-01-01")) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(REF_AREA == "U2", color2, color)) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Real rates, 10-year") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 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))

2015-

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("R_US10YT_RR", "R_JP10YT_RR", "R_U2_10Y")) %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(REF_AREA == "U2", color2, color)) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Real rates, 10-year") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 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))

2017-

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("R_US10YT_RR", "R_JP10YT_RR", "R_U2_10Y")) %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(REF_AREA == "U2", color2, color)) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Real rates, 10-year") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 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))

ECB Key Interest rates

EONIA

All

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("DFR", "MLFR", "MRR_RT", "EONIA"),
         FREQ == "M") %>%
  month_to_date %>%
  left_join(PROVIDER_FM_ID,  by = "PROVIDER_FM_ID") %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = PROVIDER_FM_ID_desc)) + 
  theme_minimal() + xlab("") + ylab("Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2025, 2) %>% 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(0.45, 0.92),
        legend.title = element_blank())

Deposit Facility, Marginal Lending Facility

All

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("DFR", "MLFR", "MRR_RT", "MRR_FR", "MRR_MBR", "MRR"),
         DATA_TYPE_FM == "LEV",
         FREQ == "D") %>%
  day_to_date %>%
  left_join(PROVIDER_FM_ID,  by = "PROVIDER_FM_ID") %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = PROVIDER_FM_ID_desc)) + 
  theme_minimal() + xlab("") + ylab("Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2025, 2) %>% 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(0.45, 0.92),
        legend.title = element_blank())

Deposit Facility, Marginal Lending Facility

All

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("DFR", "MLFR", "MRR_RT"),
         DATA_TYPE_FM == "LEV",
         FREQ == "D") %>%
  day_to_date %>%
  mutate(Provider_fm_id = factor(PROVIDER_FM_ID,
                                 levels = c("MLFR", "MRR_RT", "DFR"),
                                 labels = c("ECB Marginal lending facility",
                                            "ECB Main refinancing operations",
                                            "ECB Deposit facility"))) %>%
  left_join(PROVIDER_FM_ID,  by = "PROVIDER_FM_ID") %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = Provider_fm_id)) + 
  theme_minimal() + xlab("") + ylab("Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2025, 2) %>% 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(0.65, 0.92),
        legend.title = element_blank()) +
  geom_hline(yintercept = 3.75/100, linetype = "dashed")

2010

Code 
dates_ecb <- FM %>%
  filter(PROVIDER_FM_ID == "DFR") %>%
  day_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE-lag(OBS_VALUE)) %>%
  filter(OBS_VALUE != 0) %>%
  pull(date)
FM %>%
  filter(PROVIDER_FM_ID %in% c("DFR", "MLFR", "MRR_RT"),
         DATA_TYPE_FM == "LEV",
         FREQ == "D") %>%
  day_to_date %>%
  left_join(PROVIDER_FM_ID,  by = "PROVIDER_FM_ID") %>%
  mutate(Provider_fm_id = factor(PROVIDER_FM_ID,
                                 levels = c("MLFR", "MRR_RT", "DFR"),
                                 labels = c("ECB Marginal lending facility",
                                            "ECB Main refinancing operations - Minimum bid rate/fixed rate",
                                            "ECB Deposit facility"))) %>%
  filter(date >= as.Date("2010-01-01"))%>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = Provider_fm_id)) + 
  theme_minimal() + xlab("") + ylab("Interest Rates (%)") +
  scale_x_date(breaks = dates_ecb,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.45, 0.92),
        legend.title = element_blank())

2016-

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("DFR", "MLFR", "MRR_RT"),
         DATA_TYPE_FM == "LEV",
         FREQ == "D") %>%
  day_to_date %>%
  left_join(PROVIDER_FM_ID,  by = "PROVIDER_FM_ID") %>%
  filter(date >= as.Date("2016-01-01"))%>%
  mutate(Provider_fm_id = factor(PROVIDER_FM_ID,
                                 levels = c("MLFR", "MRR_RT", "DFR"),
                                 labels = c("ECB Marginal lending facility",
                                            "ECB Main refinancing operations - Minimum bid rate/fixed rate",
                                            "ECB Deposit facility"))) %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = Provider_fm_id)) + 
  theme_minimal() + xlab("") + ylab("Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2025, 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(0.45, 0.92),
        legend.title = element_blank())

2022-

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("DFR", "MLFR", "MRR_RT"),
         DATA_TYPE_FM == "LEV",
         FREQ == "D") %>%
  day_to_date %>%
  filter(date >= as.Date("2022-01-01")) %>%
  add_row(date = as.Date("2025-06-11"), PROVIDER_FM_ID = "DFR", OBS_VALUE = 2) %>%
  add_row(date = as.Date("2025-06-11"), PROVIDER_FM_ID = "MRR_RT", OBS_VALUE = 2.15) %>%
  add_row(date = as.Date("2025-06-11"), PROVIDER_FM_ID = "MLFR", OBS_VALUE = 2.4) %>%
  add_row(date = as.Date("2025-06-10"), PROVIDER_FM_ID = "DFR", OBS_VALUE = 2.25) %>%
  add_row(date = as.Date("2025-06-10"), PROVIDER_FM_ID = "MRR_RT", OBS_VALUE = 2.4) %>%
  add_row(date = as.Date("2025-06-10"), PROVIDER_FM_ID = "MLFR", OBS_VALUE = 2.65) %>%
  arrange(desc(date)) %>%
  #add_row(date = as.Date("2024-09-17"), PROVIDER_FM_ID = "DFR", OBS_VALUE = 3.5) %>%
  #add_row(date = as.Date("2024-09-17"), PROVIDER_FM_ID = "MRR_RT", OBS_VALUE = 3.65) %>%
  #add_row(date = as.Date("2024-09-17"), PROVIDER_FM_ID = "MLFR", OBS_VALUE = 3.9) %>%
  left_join(PROVIDER_FM_ID,  by = "PROVIDER_FM_ID") %>%
  mutate(Provider_fm_id = factor(PROVIDER_FM_ID,
                                 levels = c("MLFR", "MRR_RT", "DFR"),
                                 labels = c("ECB Marginal lending facility",
                                            "ECB Main refinancing operations - Minimum bid rate/fixed rate",
                                            "ECB Deposit facility"))) %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = Provider_fm_id)) + 
  theme_minimal() + xlab("") + ylab("Interest Rates (%)") +
  scale_x_date(breaks = c(dates_ecb),
               labels = date_format("%d %B %Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.65, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_label_repel(data = . %>% filter(date == max(date)),
                   aes(x = date, y = OBS_VALUE / 100, label = percent(OBS_VALUE / 100, acc = 0.01), color = Provider_fm_id))

2022 - Dates of change

  • Schedules for the meetings of the Governing Council. html

  • Key ECB interest rates: Dates of change. html

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("DFR", "MLFR", "MRR_RT"),
         DATA_TYPE_FM == "LEV",
         FREQ == "D") %>%
  day_to_date %>%
  left_join(PROVIDER_FM_ID,  by = "PROVIDER_FM_ID") %>%
  mutate(Provider_fm_id = factor(PROVIDER_FM_ID,
                                 levels = c("MLFR", "MRR_RT", "DFR"),
                                 labels = c("ECB Marginal lending facility",
                                            "ECB Main refinancing operations",
                                            "ECB Deposit facility"))) %>%
  filter(date >= as.Date("2022-07-01")) %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = Provider_fm_id)) + 
  theme_minimal() + xlab("") + ylab("Interest Rates (%)") +
  scale_x_date(breaks = c(dates_ecb, Sys.Date()),
               labels = date_format("%d %B %Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Around September 2022

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("DFR", "MLFR", "MRR_RT"),
         DATA_TYPE_FM == "LEV",
         FREQ == "D") %>%
  day_to_date %>%
  left_join(PROVIDER_FM_ID,  by = "PROVIDER_FM_ID") %>%
  filter(date >= as.Date("2022-09-05"),
         date <= as.Date("2022-09-20")) %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = PROVIDER_FM_ID_desc)) + 
  theme_minimal() + xlab("") + ylab("Interest Rates (%)") +
  scale_x_date(breaks = seq(from = as.Date("2022-01-01"), as.Date("2026-01-01"), by = "1 day"),
               labels = date_format("%d %b %Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 0.25),
                     labels = percent_format(accuracy = .01)) +
  theme(legend.position = c(0.4, 0.92),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Japan

1970-

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("JP10YT_RR", "R_JP10YT_RR")) %>%
  month_to_date %>%
  left_join(PROVIDER_FM_ID,  by = "PROVIDER_FM_ID") %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = PROVIDER_FM_ID_desc)) + 
  theme_minimal() + xlab("") + ylab("Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-50, 50, 2),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.9),
        legend.title = element_blank())

2000-

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("JP10YT_RR", "R_JP10YT_RR")) %>%
  month_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  left_join(PROVIDER_FM_ID,  by = "PROVIDER_FM_ID") %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = PROVIDER_FM_ID_desc)) + 
  theme_minimal() + xlab("") + ylab("Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-50, 50, 1),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.75, 0.9),
        legend.title = element_blank())

United States

1970-

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("US10YT_RR", "R_US10YT_RR")) %>%
  month_to_date %>%
  left_join(PROVIDER_FM_ID,  by = "PROVIDER_FM_ID") %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = PROVIDER_FM_ID_desc)) + 
  theme_minimal() + xlab("") + ylab("Interest Rates (%)") +
  scale_x_date(breaks = seq(1900, 2100, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-50, 50, 2),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.75, 0.9),
        legend.title = element_blank())

2000-

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("US10YT_RR", "R_US10YT_RR")) %>%
  month_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  left_join(PROVIDER_FM_ID,  by = "PROVIDER_FM_ID") %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = PROVIDER_FM_ID_desc)) + 
  theme_minimal() + xlab("") + ylab("Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-50, 50, 1),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.75, 0.9),
        legend.title = element_blank())

Euro area

1970-

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("U2_10Y", "R_U2_10Y")) %>%
  month_to_date %>%
  left_join(PROVIDER_FM_ID,  by = "PROVIDER_FM_ID") %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = PROVIDER_FM_ID_desc)) + 
  theme_minimal() + xlab("") + ylab("Interest Rates (%)") +
  scale_x_date(breaks = seq(1900, 2100, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-50, 50, 2),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.75, 0.9),
        legend.title = element_blank())

2000-

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("U2_10Y", "R_U2_10Y")) %>%
  month_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  left_join(PROVIDER_FM_ID,  by = "PROVIDER_FM_ID") %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = PROVIDER_FM_ID_desc)) + 
  theme_minimal() + xlab("") + ylab("Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-50, 50, 1),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.35, 0.2),
        legend.title = element_blank())

2015-

Code 
FM %>%
  filter(PROVIDER_FM_ID %in% c("U2_10Y", "R_U2_10Y")) %>%
  month_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(PROVIDER_FM_ID,  by = "PROVIDER_FM_ID") %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = PROVIDER_FM_ID_desc)) + 
  theme_minimal() + xlab("") + ylab("Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-50, 50, 1),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.35, 0.2),
        legend.title = element_blank())

More…

Data on monetary policy

source dataset Title .html .rData
bdf FM NA NA NA
ecb FM Financial market data 2026-06-03 2026-06-04
bdf MIR NA NA NA
bdf MIR1 NA NA NA
bis CBPOL Policy Rates, Daily 2026-04-08 2026-06-04
ecb BSI Balance Sheet Items 2026-06-03 2026-05-22
ecb BSI_PUB NA NA NA
ecb ILM Internal Liquidity Management 2026-06-03 2026-06-04
ecb ILM_PUB NA NA NA
ecb MIR MFI Interest Rate Statistics 2026-06-03 2026-06-04
ecb RAI NA NA NA
ecb SUP NA NA NA
ecb YC NA NA NA
ecb YC_PUB NA NA NA
ecb liq_daily Daily Liquidity 2026-06-03 2025-06-06
eurostat ei_mfir_m Interest rates - monthly data 2026-06-03 2026-04-26
eurostat irt_st_m Money market interest rates - monthly data 2026-06-03 2026-04-26
fred r Interest Rates 2026-06-04 2026-05-29
oecd MEI Main Economic Indicators 2024-04-16 2025-07-24
oecd MEI_FIN Monthly Monetary and Financial Statistics (MEI) 2024-09-15 2025-07-24

Data on interest rates

source dataset Title .html .rData
bdf FM NA NA NA
ecb FM Financial market data 2026-06-03 2026-06-04
bdf MIR NA NA NA
bdf MIR1 NA NA NA
bis CBPOL_D Policy Rates, Daily 2026-04-07 2025-08-20
bis CBPOL_M Policy Rates, Monthly 2026-06-04 2024-04-19
ecb MIR MFI Interest Rate Statistics 2026-06-03 2026-06-04
eurostat ei_mfir_m Interest rates - monthly data 2026-06-03 2026-04-26
eurostat irt_lt_mcby_d EMU convergence criterion series - daily data 2026-06-03 2025-07-24
eurostat irt_st_m Money market interest rates - monthly data 2026-06-03 2026-04-26
fred r Interest Rates 2026-06-04 2026-05-29
oecd MEI Main Economic Indicators 2024-04-16 2025-07-24
oecd MEI_FIN Monthly Monetary and Financial Statistics (MEI) 2024-09-15 2025-07-24
wdi FR.INR.DPST Deposit interest rate (%) 2022-09-27 2026-06-04
wdi FR.INR.LEND Lending interest rate (%) 2026-03-24 2026-06-04
wdi FR.INR.RINR NA NA NA