Non-Parametric Intraday Event Study Test

Implements the non-parametric intraday event study methodology of Rinaudo & Saha (2014). For each event time, cumulative abnormal returns (CARs) on the event day are compared to the empirical distribution of CARs from estimation-period days. Significance is assessed without distributional assumptions.

Usage

nonparametric_intraday_test(
  estimation_window,
  event_window,
  event_times,
  p = 0.05,
  init_window = 5L,
  upper = FALSE
)

Arguments

estimation_window

Data frame with columns day, time, and abnormalReturn containing intraday abnormal returns for multiple estimation days.

event_window

Data frame with columns time and abnormalReturn for the single event day.

event_times

Character vector of intraday times at which events occurred (e.g., c("10:00", "14:30")).

p

Numeric; significance level in (0, 1). Default 0.05.

init_window

Integer; number of initial observations to accumulate before testing significance. Default 5.

upper

Logical; if TRUE test for positive abnormal returns (upper tail), otherwise test for negative abnormal returns (lower tail). Default FALSE.

Value

A named list of tibbles, one per event time. Each tibble contains:

id

Observation index within the significant window.

CAR

Cumulative abnormal return on the event day.

CI

Empirical confidence interval boundary from estimation days.

fitCI

Polynomial-smoothed CI (degree-4 polynomial fit).

When an event time is not significant, returns a single-row tibble with all values equal to zero.

References

Rinaudo, J.B. & Saha, A. (2014). Non-parametric intraday event studies.

Examples

if (FALSE) { # \dontrun{
results <- nonparametric_intraday_test(
  estimation_window = est_data,
  event_window = event_data,
  event_times = c("10:00", "14:30"),
  p = 0.05,
  init_window = 5
)
} # }