Author: Charles Coverdale
Date: 2026-05-30

# Anderson-Sleath yield curves: latest and historical

#### Charles Coverdale

#### 30 May 2026

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.

## 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