eventclock

eventclock measures event-clock (information) time ahead of scheduled events — referendums, elections, central-bank decisions — from traded event probabilities such as prediction-market prices. Event-clock time $A_{t,T}$ is the quadratic variation of the log-odds of the traded event probability: how much outcome-relevant information arrived, and when.

The package implements the estimators, closed-form calculators, and datasets of the working paper

Hanke, M., Schadner, W., Stöckl, S., and Weissensteiner, A. (2026). Learning Before Scheduled Events: Prediction Markets, State Prices, and Option Valuation. Working Paper.

Features

• as_event_prices() — standardize any probability series (prediction markets, betting quotes, state prices) with flag-don’t-drop cleaning; converters q_from_price() (discount / overround), q_from_ffutures() (fed funds futures, 25bp grid), and q_from_deal_spread() (merger-arb deal clocks); data screening with ec_validate().
• event_clock() — realized-variation estimator of $A$ with truncation, bipower, and largest-move robustness variants, plus standard errors and confidence intervals (se = TRUE, quarticity or wild bootstrap); event_clock_path() for the cumulative clock over time; event_clock_forecast() for the trailing-window real-time benchmark; ec_signature() for the sampling-frequency (microstructure) diagnostic.
• Assets & finance — event_beta(): realized event exposures $\widehat{\Delta\eta}$ from returns with Newey-West errors and the $\beta = 1$ loading test against an external exposure; ec_relevance(): the sufficient statistic for pricing relevance.
• Formula book — closed-form calculators in $(q, A)$: ec_moments(), ec_exceedance(), ec_revision(), ec_atm_event_call(), ec_sigma_eff(), ec_variance_share(), ec_iv_rule(), ec_target_clock(); exact transition law and simulators with lumpy-information jumps (ec_transition_density(), ec_simulate(), ec_simulate_path()).
• Polymarket connector — pm_search(), pm_markets(), pm_prices(), pm_daily() on the public, keyless Gamma/CLOB APIs.
• Datasets — brexit2016, us2016 (the working paper’s event windows), polymarket2024 (hourly 2024 U.S. election prices), djt2024 (the matching exposed stock), and fomc_meetings (2021-2027 FOMC calendar).
• Plots — plot_q(), plot_clock(), plot_clock_vs_calendar(), plot_signature().

Installation

# install.packages("devtools")
devtools::install_github("sstoeckl/eventclock")

Quick start

library(eventclock)

ep <- as_event_prices(brexit2016,
  time = "date", price = "q_leave",
  market_id = "Brexit: Leave", event_date = as.Date("2016-06-23")
)

# the working paper's headline estimates
event_clock(ep,
  from = as.Date("2016-05-24"),
  to = c(`1W` = as.Date("2016-05-31"), `2W` = as.Date("2016-06-07"),
         `1M` = as.Date("2016-06-23"))
)
#> # A tibble: 15 × 10
#>    market_id     from       to         horizon n_obs n_incr n_gaps max_gap_days
#>    <chr>         <date>     <date>     <chr>   <int>  <int>  <int>        <dbl>
#>  1 Brexit: Leave 2016-05-24 2016-05-31 1W          8      7      0            1
#>  2 Brexit: Leave 2016-05-24 2016-05-31 1W          8      7      0            1
#>  3 Brexit: Leave 2016-05-24 2016-05-31 1W          8      7      0            1
#>  4 Brexit: Leave 2016-05-24 2016-05-31 1W          8      7      0            1
#>  5 Brexit: Leave 2016-05-24 2016-05-31 1W          8      7      0            1
#>  6 Brexit: Leave 2016-05-24 2016-06-07 2W         15     14      0            1
#>  7 Brexit: Leave 2016-05-24 2016-06-07 2W         15     14      0            1
#>  8 Brexit: Leave 2016-05-24 2016-06-07 2W         15     14      0            1
#>  9 Brexit: Leave 2016-05-24 2016-06-07 2W         15     14      0            1
#> 10 Brexit: Leave 2016-05-24 2016-06-07 2W         15     14      0            1
#> 11 Brexit: Leave 2016-05-24 2016-06-23 1M         31     30      0            1
#> 12 Brexit: Leave 2016-05-24 2016-06-23 1M         31     30      0            1
#> 13 Brexit: Leave 2016-05-24 2016-06-23 1M         31     30      0            1
#> 14 Brexit: Leave 2016-05-24 2016-06-23 1M         31     30      0            1
#> 15 Brexit: Leave 2016-05-24 2016-06-23 1M         31     30      0            1
#> # ℹ 2 more variables: method <chr>, A <dbl>

plot_clock(event_clock_path(ep, from = as.Date("2016-05-24")))

Live data from Polymarket:

mkts <- pm_markets("presidential-election-winner-2024")
tok <- mkts$token_id[grepl("Trump", mkts$question) & mkts$outcome == "Yes"]
ep24 <- pm_prices(tok, from = "2024-06-01", to = "2024-11-06")
event_clock(pm_daily(ep24))

(An alternative community client for the same APIs is polymarketR.)

See the vignette for the full tour: vignette("eventclock-brexit", package = "eventclock").

Citation

citation("eventclock")

Please cite the working paper above when you use the event-clock methodology.

License

MIT © Sebastian Stöckl