Anderson-Sleath yield curves: latest and historical
In boe: Download Data from the 'Bank of England' Statistical Database

library(boe)
not_on_cran <- identical(Sys.getenv("NOT_CRAN"), "true")
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  fig.width  = 7,
  fig.height = 4.5,
  eval = not_on_cran
)
op <- options(boe.cache_dir = tempfile("boe_vignette_"))

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")
range(latest$date)
range(latest$maturity_years)

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")$source
attr(latest, "boe_query")$series_codes

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)
)
head(panel)

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()
}

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)
)

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")])

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()
}

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)
)

mpc <- boe_mpc_decisions(from = "2020-01-01")
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)

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")
}

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")
range(short$maturity_years)   # monthly grid, ~1/12 to 5 years

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")
latest_day <- ois_short[ois_short$date == max(ois_short$date), ]
head(latest_day)

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()
}

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.