Volume and Volatility Event Studies

Introduction

Traditional event studies focus on price-based abnormal returns. However, corporate events often affect trading volume and return volatility even when the net price effect is ambiguous. For example, an earnings announcement may trigger heavy trading and volatility spikes without moving the price much in either direction. Volume and volatility event studies capture these information-content effects that price-based methods can miss.

The EventStudy package provides two dedicated models:

Model	Class	Abnormal measure
Volume	`VolumeModel`	Deviation of (log-)volume from estimation-window mean
Volatility	`VolatilityModel`	Ratio of squared returns to estimation-window variance, minus 1

Both models plug into the standard pipeline (prepare_event_study() → fit_model() → calculate_statistics()) and are compatible with all existing test statistics.

library(EventStudy)
library(dplyr)
library(ggplot2)

Data Requirements

Volume Event Study

The VolumeModel requires a firm_volume column in the firm data, in addition to the standard symbol, date, and adjusted columns. The volume column should contain raw trading volume (number of shares traded):

firm_tbl <- tibble(
  symbol   = rep("FIRM_A", 300),
  date     = format(seq(as.Date("2014-01-01"), by = "day", length.out = 300),
                    "%d.%m.%Y"),
  adjusted = 100 * cumprod(1 + rnorm(300, 0.0003, 0.015)),
  firm_volume = pmax(0, rnorm(300, mean = 1e6, sd = 2e5))
)

The firm_volume column is preserved through the pipeline automatically. The standard EventStudyTask passes through any extra columns present in the firm data.

Volatility Event Study

The VolatilityModel does not require extra columns—it uses the firm returns already computed by prepare_event_study(). Abnormal volatility is defined as:

AV_{i,t} = \frac{R_{i,t}^2}{\hat{\sigma}_i^2} - 1

where \hat{\sigma}_i^2 is the variance of firm returns in the estimation window. Positive values indicate higher-than-expected volatility.

Volume Event Study

Setup

set.seed(42)

# Firm data with volume
n <- 300
firm_tbl <- tibble(
  symbol      = "FIRM_A",
  date        = format(seq(as.Date("2014-06-01"), by = "day",
                           length.out = n), "%d.%m.%Y"),
  adjusted    = 100 * cumprod(1 + rnorm(n, 0.0003, 0.015)),
  firm_volume = pmax(0, rnorm(n, mean = 1e6, sd = 2e5))
)

# Index data
index_tbl <- tibble(
  symbol   = "INDEX_1",
  date     = firm_tbl$date,
  adjusted = 1000 * cumprod(1 + rnorm(n, 0.0002, 0.012))
)

# Request table
request_tbl <- tibble(
  event_id                = 1L,
  firm_symbol             = "FIRM_A",
  index_symbol            = "INDEX_1",
  event_date              = firm_tbl$date[200],
  group                   = "Earnings",
  event_window_start      = -10L,
  event_window_end        = 10L,
  shift_estimation_window = -11L,
  estimation_window_length = 150L
)

Running the Volume Study

The key difference from a standard event study is passing VolumeModel$new() as the return model. The study_type field on the ParameterSet can be set to "volume" for appropriate axis labels in plots.

task <- EventStudyTask$new(firm_tbl, index_tbl, request_tbl)

params <- ParameterSet$new(
  return_calculation      = SimpleReturn$new(),
  return_model            = VolumeModel$new(log_transform = TRUE),
  single_event_statistics = SingleEventStatisticsSet$new(),
  multi_event_statistics  = MultiEventStatisticsSet$new()
)
params$study_type <- "volume"

task <- task |>
  prepare_event_study(params) |>
  fit_model(params) |>
  calculate_statistics(params)

Log Transform

By default, VolumeModel log-transforms volume (log(volume + 1)) before computing abnormal volume. This is standard practice because raw volume is typically right-skewed. To disable the transform:

vm <- VolumeModel$new(log_transform = FALSE)

Interpreting Results

Abnormal volume is the difference between observed (log-)volume and the estimation-window mean:

AVolume_{i,t} = \log(V_{i,t} + 1) - \overline{\log(V_i + 1)}^{\text{est}}

Positive values indicate higher-than-normal trading activity. The test statistics (AR T-test, CAR T-test, etc.) are applied to these abnormal volume measures in the same way as for abnormal returns.

# Single-event results
task$get_ar(event_id = 1)
task$get_car(event_id = 1)

# Tidy output
tidy.EventStudyTask(task, type = "ar")

Visualization

plot_event_study(task, type = "ar", event_id = 1) +
  labs(title = "Abnormal Volume Around Earnings Announcement",
       y = "Abnormal Log-Volume")

Volatility Event Study

Setup

The volatility study uses the same data structure as a standard event study—no extra columns are needed.

set.seed(123)

n <- 300
firm_tbl <- tibble(
  symbol   = "FIRM_A",
  date     = format(seq(as.Date("2014-06-01"), by = "day",
                        length.out = n), "%d.%m.%Y"),
  adjusted = 100 * cumprod(1 + rnorm(n, 0.0003, 0.015))
)

index_tbl <- tibble(
  symbol   = "INDEX_1",
  date     = firm_tbl$date,
  adjusted = 1000 * cumprod(1 + rnorm(n, 0.0002, 0.012))
)

request_tbl <- tibble(
  event_id                = 1L,
  firm_symbol             = "FIRM_A",
  index_symbol            = "INDEX_1",
  event_date              = firm_tbl$date[200],
  group                   = "Earnings",
  event_window_start      = -10L,
  event_window_end        = 10L,
  shift_estimation_window = -11L,
  estimation_window_length = 150L
)

Running the Volatility Study

task <- EventStudyTask$new(firm_tbl, index_tbl, request_tbl)

params <- ParameterSet$new(
  return_calculation      = SimpleReturn$new(),
  return_model            = VolatilityModel$new(),
  single_event_statistics = SingleEventStatisticsSet$new(),
  multi_event_statistics  = MultiEventStatisticsSet$new()
)
params$study_type <- "volatility"

task <- task |>
  prepare_event_study(params) |>
  fit_model(params) |>
  calculate_statistics(params)

Interpreting Results

Abnormal volatility is a variance ratio minus 1:

AV_{i,t} = \frac{R_{i,t}^2}{\hat{\sigma}_i^2} - 1

AV = 0: volatility matches estimation-window expectations.
AV > 0: volatility is higher than expected (e.g., information arrival).
AV < 0: volatility is lower than expected (e.g., quiet trading).

Values around event dates are typically large and positive when events carry new information, regardless of the direction of the price move.

task$get_ar(event_id = 1)
task$get_car(event_id = 1)

tidy.EventStudyTask(task, type = "ar")

Visualization

plot_event_study(task, type = "ar", event_id = 1) +
  labs(title = "Abnormal Volatility Around Earnings Announcement",
       y = "Abnormal Volatility (Variance Ratio - 1)")

Combining Price, Volume, and Volatility

A comprehensive event study often examines all three dimensions. You can run separate studies on the same data and compare results:

set.seed(42)

n <- 300
firm_tbl <- tibble(
  symbol      = "FIRM_A",
  date        = format(seq(as.Date("2014-06-01"), by = "day",
                           length.out = n), "%d.%m.%Y"),
  adjusted    = 100 * cumprod(1 + rnorm(n, 0.0003, 0.015)),
  firm_volume = pmax(0, rnorm(n, mean = 1e6, sd = 2e5))
)

index_tbl <- tibble(
  symbol   = "INDEX_1",
  date     = firm_tbl$date,
  adjusted = 1000 * cumprod(1 + rnorm(n, 0.0002, 0.012))
)

request_tbl <- tibble(
  event_id = 1L, firm_symbol = "FIRM_A", index_symbol = "INDEX_1",
  event_date = firm_tbl$date[200], group = "Earnings",
  event_window_start = -10L, event_window_end = 10L,
  shift_estimation_window = -11L, estimation_window_length = 150L
)

# --- Price study ---
task_price <- EventStudyTask$new(firm_tbl, index_tbl, request_tbl)
params_price <- ParameterSet$new(return_model = MarketModel$new())
task_price <- run_event_study(task_price, params_price)

# --- Volume study ---
task_vol <- EventStudyTask$new(firm_tbl, index_tbl, request_tbl)
params_vol <- ParameterSet$new(return_model = VolumeModel$new())
params_vol$study_type <- "volume"
task_vol <- task_vol |>
  prepare_event_study(params_vol) |>
  fit_model(params_vol) |>
  calculate_statistics(params_vol)

# --- Volatility study ---
task_volat <- EventStudyTask$new(firm_tbl, index_tbl, request_tbl)
params_volat <- ParameterSet$new(return_model = VolatilityModel$new())
params_volat$study_type <- "volatility"
task_volat <- task_volat |>
  prepare_event_study(params_volat) |>
  fit_model(params_volat) |>
  calculate_statistics(params_volat)

# Extract tidy ARs from each study
ar_price  <- tidy.EventStudyTask(task_price, type = "ar") %>%
  mutate(study = "Price (AR)")
ar_vol    <- tidy.EventStudyTask(task_vol, type = "ar") %>%
  mutate(study = "Volume (Abnormal Log-Volume)")
ar_volat  <- tidy.EventStudyTask(task_volat, type = "ar") %>%
  mutate(study = "Volatility (Variance Ratio - 1)")

combined <- bind_rows(ar_price, ar_vol, ar_volat)

ggplot(combined, aes(x = relative_index, y = abnormal_returns)) +
  geom_hline(yintercept = 0, linetype = "dashed", colour = "grey40") +
  geom_vline(xintercept = 0, linetype = "dotted", colour = "red", alpha = 0.6) +
  geom_col(fill = "steelblue", alpha = 0.7) +
  facet_wrap(~ study, ncol = 1, scales = "free_y") +
  labs(
    title = "Price, Volume, and Volatility Event Study",
    x     = "Relative Time to Event",
    y     = "Abnormal Measure"
  ) +
  theme_minimal()

This three-panel view reveals whether the event primarily affects prices, trading activity, volatility, or some combination.

Diagnostics

Model diagnostics work the same way as for price-based studies:

# Volume model diagnostics
model_diagnostics(task_vol)
plot_diagnostics(task_vol, event_id = 1)

# Volatility model diagnostics
model_diagnostics(task_volat)
plot_diagnostics(task_volat, event_id = 1)

The diagnostics include estimation-window residual normality (Shapiro-Wilk), autocorrelation (Durbin-Watson, Ljung-Box), and model fit statistics.

Export

Results can be exported in the same formats as any other study:

export_results(task_vol, "volume_results.csv")
export_results(task_vol, "volume_results.xlsx")
export_results(task_volat, "volatility_results.tex", which = c("model", "ar"))

References

Campbell, J. Y. & Hentschel, L. (1992). No news is good news: An asymmetric model of changing volatility in stock returns. Journal of Financial Economics, 31(3), 281–318.
Beaver, W. H. (1968). The information content of annual earnings announcements. Journal of Accounting Research, 6, 67–92.
MacKinlay, A. C. (1997). Event Studies in Economics and Finance. Journal of Economic Literature, 35(1), 13–39.

Simon Mueller

2026-09-06

Introduction

Data Requirements

Volume Event Study

Volatility Event Study

Volume Event Study

Setup

Running the Volume Study

Log Transform

Interpreting Results

Visualization

Volatility Event Study

Setup

Running the Volatility Study

Interpreting Results

Visualization

Combining Price, Volume, and Volatility

Diagnostics

Export

References