eventstudy.Single.__init__

Single.__init__(model_func, model_data: dict, event_window: tuple = (-10, 10), estimation_size: int = 300, buffer_size: int = 30, keep_model: bool = False, description: str = None, event_date: numpy.datetime64 = None)

Low-level (complex) way of runing an event study. Prefer the simpler use of model methods.

Parameters

• model_func – Function computing the modelisation of returns.

• model_data (dict) – Dictionary containing all parameters needed by model_func.

• event_window (tuple, optional) – Event window specification (T2,T3), by default (-10, +10). A tuple of two integers, representing the start and the end of the event window. Classically, the event-window starts before the event and ends after the event. For example, event_window = (-2,+20) means that the event-period starts 2 periods before the event and ends 20 periods after.

• estimation_size (int, optional) – Size of the estimation for the modelisation of returns [T0,T1], by default 300

• buffer_size (int, optional) – Size of the buffer window [T1,T2], by default 30

• keep_model (bool, optional) – If true the model used to compute the event study will be stored in memory. It will be accessible through the class attributes eventstudy.Single.model, by default False

See also

market_model(), FamaFrench_3factor(), constant_mean()

Example

Run an event study based on : .. the market_model function defined in the models submodule, .. given values for security and market returns, .. and default parameters

>>> from eventstudy import Single, models
>>> event = Single(
...     models.market_model,
...     {'security_returns':[0.032,-0.043,...], 'market_returns':[0.012,-0.04,...]}
... )