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Volkswagen "Dieselgate" Multi-Automaker Event Study Dataset

Source: R/data-dieselgate.R
dieselgate.Rd

A small, frozen dataset bundling daily prices for four German automakers and the DAX benchmark index around the 2015 "dieselgate" emissions scandal, ready to drive a complete multi-group event study (prepare_event_study() -> fit_model() -> calculate_statistics()).

Usage

data(dieselgate)

Format

A named list with four elements:

firm

A tibble of daily prices for all four automakers combined, with columns symbol (one of "VOW.DE", "PAH3.DE", "BMW.DE", "MBG.DE"), date (character, "%d.%m.%Y" format), and adjusted (numeric adjusted close). Rows for all firms are stacked (1 440 rows total).

index

A tibble of DAX ("^GDAXI") daily prices with the same symbol / date / adjusted columns, used as the benchmark / reference market for all four events.

request

A four-row tibble giving the event-study request specifications, one row per firm, with the nine columns expected by EventStudyTask: event_id (1L to 4L), firm_symbol, index_symbol, event_date ("18.09.2015"), group ("VW Group" for VOW.DE/PAH3.DE, "Other" for BMW.DE/MBG.DE), event_window_start (-10), event_window_end (10), shift_estimation_window (-11), and estimation_window_length (250). event_id = 1 is VOW.DE for backward compatibility.

meta

A list of provenance metadata: firm_tickers (character vector of all four tickers), groups (named list mapping group labels to tickers), index_ticker, event_date, from, to, source, access_date, and note.

Source

Yahoo Finance daily adjusted prices, retrieved 2026-09-04 via the package's own download_stock_data. This is a small illustrative sample bundled for academic / demonstration use only; see data-raw/dieselgate.R for the reproducible fetch script.

Details

The event is the U.S. Environmental Protection Agency's Notice of Violation issued to Volkswagen on 2015-09-18 (a Friday); the share-price crash lands on the following trading days. The dataset covers two groups: the "VW Group" (VOW.DE, PAH3.DE — directly implicated firms) and "Other" (BMW.DE, MBG.DE — peer automakers). The bundled window layout uses a 250-trading-day estimation window ending 11 days before the event and an event window of [-10, +10] trading days.

Fitting a market model on the "VW Group" events produces a strongly negative cumulative average abnormal return (CAAR approximately -39 roughly -17% and -13% average abnormal returns on the first two trading days after the disclosure, while the "Other" peer automakers show near-zero CAAR (approximately +1%), illustrating the idiosyncratic nature of the shock.

Firms:

  • VOW.DE — Volkswagen AG ordinary shares (Xetra), event_id = 1

  • PAH3.DE — Porsche Automobil Holding SE (Xetra), event_id = 2

  • BMW.DE — BMW AG (Xetra), event_id = 3

  • MBG.DE — Mercedes-Benz Group AG (Xetra), event_id = 4

Groups: "VW Group" (VOW.DE, PAH3.DE) vs "Other" (BMW.DE, MBG.DE). Benchmark: DAX performance index (ticker ^GDAXI). Date range: 2014-06-01 to 2015-11-01.

Examples

# \donttest{
data(dieselgate)

# Build a multi-group task and run the full pipeline
task <- EventStudyTask$new(dieselgate$firm, dieselgate$index,
                           dieselgate$request)
task <- run_event_study(task, ParameterSet$new())

# Single-firm: VW crash (event_id = 1)
task$get_car(1L)
#> # A tibble: 21 × 3
#>    relative_index abnormal_returns     car
#>             <int>            <dbl>   <dbl>
#>  1            -10         0.00280  0.00280
#>  2             -9         0.000281 0.00308
#>  3             -8         0.00930  0.0124 
#>  4             -7         0.0224   0.0348 
#>  5             -6        -0.00573  0.0291 
#>  6             -5         0.00576  0.0349 
#>  7             -4        -0.00497  0.0299 
#>  8             -3         0.00253  0.0324 
#>  9             -2         0.000144 0.0326 
#> 10             -1        -0.000434 0.0321 
#> # ℹ 11 more rows

# Multi-group: CAAR comparison
vw_caar    <- task$aar_caar_tbl[task$aar_caar_tbl$group == "VW Group", ]$CSectT[[1]]
other_caar <- task$aar_caar_tbl[task$aar_caar_tbl$group == "Other", ]$CSectT[[1]]
tail(vw_caar[, c("relative_index", "caar", "caar_t")], 1)
#> # A tibble: 1 × 3
#>   relative_index   caar caar_t
#>            <int>  <dbl>  <dbl>
#> 1             10 -0.386  -12.6
tail(other_caar[, c("relative_index", "caar", "caar_t")], 1)
#> # A tibble: 1 × 3
#>   relative_index   caar caar_t
#>            <int>  <dbl>  <dbl>
#> 1             10 0.0132  0.338
# }