Anderson-Sleath yield curves: latest and historical

The Bank of England publishes daily fitted yield curves at all maturities using the Anderson and Sleath (2001) smoothing methodology. Five curves are produced: nominal gilt, real (index-linked) gilt, implied inflation, overnight index swap (OIS), and the commercial bank liability curve (BLC). Each is available in spot and instantaneous-forward form, and in two segments: the standard curve (half-year maturity steps out to 25 or 40 years) and the separately fitted short end (monthly steps from one month to five years), selected with segment = "short".

The default behaviour of boe_curve() returns the latest published month, matching what most analysts need when they reach for “today’s curve”. Pass from, to, or frequency = "monthly" and the function switches to the BoE historical archive, which extends back as far as 1979 for nominal gilts.

Latest published month

latest <- boe_curve(curve = "nominal", measure = "spot")
#> ℹ Downloading yield curve archive from Bank of England
#> ✔ Downloading yield curve archive from Bank of England [260ms]
#> 
range(latest$date)
#> [1] "2026-05-01" "2026-05-28"
range(latest$maturity_years)
#> [1]  0.5 40.0

boe_curve() returns a long-format boe_tbl with one row per (date, maturity) pair. Provenance is attached as an attribute:

attr(latest, "boe_query")$source
#> [1] "latest"
attr(latest, "boe_query")$series_codes
#> [1] "AS_NOMINAL_SPOT"

Historical: 10-year nominal spot rate since 2000

For time-series work, boe_curve_panel() reshapes the long format into a wide panel with one column per pillar maturity. End-of-month frequency is plenty for multi-decade work and keeps the download small.

panel <- boe_curve_panel(
  curve     = "nominal",
  measure   = "spot",
  frequency = "monthly",
  from      = "2000-01-01",
  maturities = c(2, 5, 10, 20)
)
#> ℹ Downloading nominal monthly yield-curve archive from Bank of England
#> ✔ Downloading nominal monthly yield-curve archive from Bank of England [37ms]
#> 
head(panel)
#> # BoE [boe_curve_panel]: 1 series [AS_NOMINAL_SPOT] · 6 obs · 2000-01-31 to 2026-04-30 · freq=monthly
#>         date       m2       m5      m10      m20
#> 1 2000-01-31 6.474909 6.286147 5.613887 4.478985
#> 2 2000-02-29 6.302563 6.010255 5.334982 4.363222
#> 3 2000-03-31 6.273430 5.857503 5.147789 4.349529
#> 4 2000-04-30 6.069206 5.704744 5.097727 4.262276
#> 5 2000-05-31 6.136496 5.695847 5.051332 4.304463
#> 6 2000-06-30 5.942865 5.594983 5.073053 4.379719

if (requireNamespace("ggplot2", quietly = TRUE)) {
  ggplot2::ggplot(panel, ggplot2::aes(date, m10)) +
    ggplot2::geom_line(colour = "#1f77b4") +
    ggplot2::labs(
      title    = "UK 10-year nominal spot rate",
      subtitle = "End of month, Anderson-Sleath fitted",
      x = NULL, y = "Per cent"
    ) +
    ggplot2::theme_minimal()
}

10-year UK nominal spot rate, 2000 to present

5y5y forward implied inflation

The 5y5y forward inflation rate (the average implied inflation rate over the second five-year horizon, five years from now) is a textbook medium-term inflation expectations measure. It comes straight off the implied-inflation forward curve.

inflation_fwd <- boe_curve_panel(
  curve     = "inflation",
  measure   = "forward",
  frequency = "monthly",
  from      = "2010-01-01",
  maturities = c(5, 10)
)
#> ℹ Downloading inflation monthly yield-curve archive from Bank of England
#> ✔ Downloading inflation monthly yield-curve archive from Bank of England [87ms]
#> 

inflation_fwd$five_y_five_y <- (inflation_fwd$m10 * 10 -
                                inflation_fwd$m5  *  5) / 5
head(inflation_fwd[, c("date", "m5", "m10", "five_y_five_y")])
#> # BoE: 6 obs
#>         date       m5      m10 five_y_five_y
#> 1 2010-01-31 3.299640 3.863640      4.427640
#> 2 2010-02-28 3.401710 4.095848      4.789986
#> 3 2010-03-31 3.415668 4.318069      5.220470
#> 4 2010-04-30 3.458722 4.233360      5.007998
#> 5 2010-05-31 2.957608 4.008686      5.059764
#> 6 2010-06-30 2.779321 3.854157      4.928993

if (requireNamespace("ggplot2", quietly = TRUE)) {
  ggplot2::ggplot(inflation_fwd, ggplot2::aes(date, five_y_five_y)) +
    ggplot2::geom_line(colour = "#d62728") +
    ggplot2::geom_hline(yintercept = 2.0, linetype = "dashed",
                        colour = "grey40") +
    ggplot2::annotate("text", x = max(inflation_fwd$date),
                      y = 2.0, label = "2% target", hjust = 1, vjust = -0.5,
                      colour = "grey40", size = 3) +
    ggplot2::labs(
      title    = "UK 5y5y forward implied inflation",
      subtitle = "End of month, derived from the BoE implied-inflation forward curve",
      x = NULL, y = "Per cent per annum"
    ) +
    ggplot2::theme_minimal()
}

UK 5y5y forward implied inflation, 2010 to present

OIS curve evolution across the rate cycle

The OIS curve gives a market-implied path for Bank Rate. Comparing OIS spot pillars at MPC decision dates shows how expectations shifted through the 2022 to 2024 hiking cycle.

ois <- boe_curve_panel(
  curve     = "ois",
  measure   = "spot",
  frequency = "monthly",
  from      = "2020-01-01",
  maturities = c(0.5, 1, 2, 5)
)
#> ℹ Downloading ois monthly yield-curve archive from Bank of England
#> ✔ Downloading ois monthly yield-curve archive from Bank of England [57ms]
#> 

mpc <- boe_mpc_decisions(from = "2020-01-01")
#> ℹ Downloading from Bank of England
#> ✔ Downloading from Bank of England [566ms]
#> 
mpc <- data.frame(date = mpc$date, bank_rate = mpc$new_rate_pct)

merged <- merge(ois, mpc, by = "date", all.x = TRUE)
merged$bank_rate <- as.numeric(merged$bank_rate)

# carry the bank rate forward between MPC dates
for (i in seq_along(merged$bank_rate)) {
  if (i > 1 && is.na(merged$bank_rate[i])) {
    merged$bank_rate[i] <- merged$bank_rate[i - 1]
  }
}
tail(merged)
#>          date     m0.5       m1       m2       m5 bank_rate
#> 70 2025-11-28 3.670990 3.533751 3.468765 3.589609        NA
#> 71 2025-12-31 3.613941 3.485618 3.454123 3.621165        NA
#> 72 2026-01-30 3.603969 3.491676 3.492607 3.727255        NA
#> 73 2026-02-27 3.461611 3.348672 3.309542 3.485201        NA
#> 74 2026-03-31 3.950747 4.102948 4.162256 4.106304        NA
#> 75 2026-04-30 3.968888 4.190262 4.258125 4.245096        NA

if (requireNamespace("ggplot2", quietly = TRUE)) {
  long <- data.frame(
    date  = rep(merged$date, 5),
    pillar = rep(c("Bank Rate", "6m OIS", "1y OIS", "2y OIS", "5y OIS"),
                 each = nrow(merged)),
    rate  = c(merged$bank_rate, merged$m0.5, merged$m1, merged$m2, merged$m5)
  )
  long$pillar <- factor(long$pillar,
                        levels = c("Bank Rate", "6m OIS", "1y OIS",
                                   "2y OIS", "5y OIS"))

  ggplot2::ggplot(long, ggplot2::aes(date, rate, colour = pillar)) +
    ggplot2::geom_line() +
    ggplot2::labs(
      title    = "UK OIS spot pillars and Bank Rate, 2020 to present",
      subtitle = "Tightening cycle visible across all pillars",
      x = NULL, y = "Per cent", colour = NULL
    ) +
    ggplot2::theme_minimal() +
    ggplot2::theme(legend.position = "bottom")
}
#> Warning: Removed 75 rows containing missing values or values outside the scale range
#> (`geom_line()`).

UK OIS spot pillars and Bank Rate, 2020 to present

The short end of the curve

The standard curves step in half-years from 0.5 years out. For near-term policy and money-market work the Bank fits a separate short end at monthly maturities, from one month to five years. Pass segment = "short" to boe_curve() or boe_curve_panel() to reach it.

short <- boe_curve(curve = "nominal", measure = "spot", segment = "short")
#> ℹ Using cached yield curve archive
#> ✔ Using cached yield curve archive [6ms]
#> 
range(short$maturity_years)   # monthly grid, ~1/12 to 5 years
#> [1] 0.4166667 4.9999998

The short end of the OIS forward curve is the cleanest market-implied path for Bank Rate: the instantaneous forward rate at each horizon, at monthly resolution. Here it is on the most recent published date.

ois_short  <- boe_curve(curve = "ois", measure = "forward", segment = "short")
#> ℹ Using cached yield curve archive
#> ✔ Using cached yield curve archive [6ms]
#> 
latest_day <- ois_short[ois_short$date == max(ois_short$date), ]
head(latest_day)
#> # BoE [boe_curve]: 1 series [AS_OIS_FORWARD_SHORT] · 6 obs · 2026-05-01 to 2026-05-28 · freq=daily
#>            date maturity_years rate_pct
#> 1021 2026-05-28     0.08333333 3.733148
#> 1022 2026-05-28     0.16666667 3.788507
#> 1023 2026-05-28     0.25000000 3.849676
#> 1024 2026-05-28     0.33333333 3.912664
#> 1025 2026-05-28     0.41666667 3.973075
#> 1026 2026-05-28     0.50000000 4.025891

if (requireNamespace("ggplot2", quietly = TRUE)) {
  ggplot2::ggplot(latest_day, ggplot2::aes(maturity_years, rate_pct)) +
    ggplot2::geom_line(colour = "#1f77b4") +
    ggplot2::geom_point(size = 0.8, colour = "#1f77b4") +
    ggplot2::labs(
      title    = "UK OIS instantaneous forward curve, short end",
      subtitle = paste("Market-implied Bank Rate path on",
                       format(max(ois_short$date), "%d %B %Y")),
      x = "Horizon (years)", y = "Per cent"
    ) +
    ggplot2::theme_minimal()
}

UK OIS short-end forward curve on the latest published date

Short-end history goes back as far as the Bank published it: to 1979 for nominal gilts, and from 2016 for OIS. Where a period has no short-end sheet (early OIS, for instance), those dates are simply absent from the result rather than causing an error.

When to reach for the archive

Question	Argument set
Today’s curve	none (default)
Last few years, daily	`from = "2020-01-01"`
Multi-decade panel for econometrics	`frequency = "monthly", from = "1990-01-01"`
Near-term policy-rate path, monthly detail	`segment = "short"`
Commercial bank liability curve	`curve = "blc"` (always uses archive)

Archive zips cache for 30 days by default; the latest-month zip caches for 24 hours. Override with cache_ttl_h if you need to force a fresh pull.

References

Anderson, N. and Sleath, J. (2001). New estimates of the UK real and nominal yield curves. Bank of England Working Paper No. 126. https://www.bankofengland.co.uk/working-paper/2001/new-estimates-of-the-uk-real-and-nominal-yield-curves

Charles Coverdale