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Cumulative event-clock path

Source: R/clock.R
event_clock_path.Rd

Computes the running (cumulative) event clock \(\widehat A_t = \sum_{s \le t} (\Delta L_s)^2\) — the object behind the "clock plot": how much of the eventual information had arrived by each calendar date.

Usage

event_clock_path(
  x,
  from = NULL,
  to = NULL,
  sample_every = ec_default_params()$sample_every,
  clip = NULL,
  normalize = TRUE
)

# S3 method for class 'event_clock_path'
plot(x, ...)

Arguments

x

An event_prices object (see as_event_prices()), or a data.frame coercible to one.

from

Valuation date/time (default: first observation).

to

One or more horizon dates/times (default: last observation = full sample). Names are used as horizon labels.

sample_every

Integer, use every k-th observation (sparse-sampling robustness; default 1).

clip

Numeric length-2 clipping bounds for q before the log-odds transform; defaults to the bounds stored in x.

normalize

Logical; if TRUE (default) also return A_frac, the clock normalized to \([0, 1]\) over the window.

...

Unused (for the plot method).

Value

A tibble of class event_clock_path with columns time, q, L (clipped log-odds), dL, dA (squared increment), A (cumulative clock), and — with normalize = TRUE — A_frac and cal_frac (fraction of calendar time elapsed). Attributes carry the market id and event date.

Methods (by generic)

  • plot(event_clock_path): Plot method; dispatches to plot_clock().

Examples

data(brexit2016)
ep <- as_event_prices(brexit2016, time = "date", price = "q_leave")
path <- event_clock_path(ep)
tail(path)
#> # A tibble: 6 × 8
#>   time           q      L       dL        dA     A A_frac cal_frac
#>   <date>     <dbl>  <dbl>    <dbl>     <dbl> <dbl>  <dbl>    <dbl>
#> 1 2016-06-18 0.36  -0.575 -0.00434 0.0000188 0.759  0.860    0.958
#> 2 2016-06-19 0.296 -0.866 -0.291   0.0847    0.844  0.956    0.966
#> 3 2016-06-20 0.274 -0.974 -0.108   0.0117    0.856  0.969    0.975
#> 4 2016-06-21 0.252 -1.09  -0.114   0.0129    0.868  0.984    0.983
#> 5 2016-06-22 0.252 -1.09   0       0         0.868  0.984    0.992
#> 6 2016-06-23 0.23  -1.21  -0.120   0.0145    0.883  1        1