DCC-GARCH Model

Event study model using Dynamic Conditional Correlation GARCH for time-varying beta estimation. Requires the rmgarch package. The bivariate DCC-GARCH model captures both time-varying volatility and time-varying correlation between firm and market returns, yielding a time-varying beta: \(\beta_t = Cov(R_{firm}, R_{market})_t / Var(R_{market})_t\).

Super class

ModelBase -> DCCGARCHModel

Public fields

model_name

Name of the model.

garch_order

GARCH order for each univariate model. Default c(1,1).

dcc_order

DCC order. Default c(1,1).

Methods

Public methods

• DCCGARCHModel$new()

• DCCGARCHModel$fit()

• DCCGARCHModel$abnormal_returns()

• DCCGARCHModel$clone()

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DCCGARCHModel$new()

Create a new DCCGARCHModel.

Usage

DCCGARCHModel$new(garch_order = c(1, 1), dcc_order = c(1, 1))

Arguments

garch_order

GARCH(p,q) order for univariate models.

dcc_order

DCC(a,b) order.

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DCCGARCHModel$fit()

Fit the DCC-GARCH model on the estimation window.

Usage

DCCGARCHModel$fit(data_tbl)

Arguments

data_tbl

Data frame or tibble with firm_returns, index_returns, estimation_window, event_window columns.

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DCCGARCHModel$abnormal_returns()

Calculate abnormal returns using the last conditional beta.

Usage

DCCGARCHModel$abnormal_returns(data_tbl)

Arguments

data_tbl

Data frame or tibble.

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DCCGARCHModel$clone()

The objects of this class are cloneable with this method.

Usage

DCCGARCHModel$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.