apply_market_model: Apply a market model and return a list of 'returns' objects.
In irudnyts/estudy2: An Implementation of Parametric and Nonparametric Event Study

apply_market_model

R Documentation

Apply a market model and return a list of `returns` objects.

Description

The function applies a given market model to securities' rates of returns and returns a list of returns objects for each security, which can be passed directly to a whole battery of tests.

Usage

apply_market_model(
  rates,
  regressors,
  same_regressor_for_all = TRUE,
  market_model = c("mean_adj", "mrkt_adj", "sim"),
  estimation_method = c("ols"),
  estimation_start,
  estimation_end
)

Arguments

`rates`	an object of `list`, `data.frame`, `zoo` containing rates of returns of securities.
`regressors`	an object of the same class as `rates` containing regressors. The argument can be omitted, if market model is `mean_adj`. `regressors` must have the same number of components as `rates` except cases when the same regressor is used for all securities.
`same_regressor_for_all`	logical. Should the same regressor be used for each security? The default value is TRUE.
`market_model`	a character indicating the market model among `mean_adj`, `mrkt_adj`, and `sim`.
`estimation_method`	a character specifying an estimation method for `sim` model.
`estimation_start`	an object of `Date` class giving the first date of the estimation period.
`estimation_end`	an object of `Date` class giving the last date of the estimation period.

Details

The generic function is dispatched for such classes as list, data.frame, and zoo. If same_regressor_for_all is TRUE, and regressors has the length greater than one, the first element of regressors will be applied for each security in rates.

Value

A list of returns objects.

References

Brown S.J., Warner J.B. Using Daily Stock Returns, The Case of Event Studies. Journal of Financial Economics, 14:3-31, 1985.

Examples

## 1. Mean-adjusted-returns model
## Not run: 
library("magrittr")
tickers <- c("AMZN", "ZM", "UBER", "NFLX", "SHOP", "FB", "UPWK")
securities_returns <- get_prices_from_tickers(tickers,
                                              start = as.Date("2019-04-01"),
                                              end = as.Date("2020-04-01"),
                                              quote = "Close",
                                              retclass = "zoo") %>%
    get_rates_from_prices(quote = "Close",
                          multi_day = TRUE,
                          compounding = "continuous") %>%
    apply_market_model(market_model = "mean_adj",
                       estimation_start = as.Date("2019-04-01"),
                       estimation_end = as.Date("2020-03-13"))

## End(Not run)
## The result of the code above is equivalent to:
data(rates)
securities_returns <- apply_market_model(
    rates,
    market_model = "mean_adj",
    estimation_start = as.Date("2019-04-01"),
    estimation_end = as.Date("2020-03-13")
)

## 2. Market-adjusted-returns model
## Not run: 
library("magrittr")
rates_indx <- get_prices_from_tickers("^GSPC",
                                      start = as.Date("2019-04-01"),
                                      end = as.Date("2020-04-01"),
                                      quote = "Close",
                                      retclass = "zoo") %>%
    get_rates_from_prices(quote = "Close",
                          multi_day = TRUE,
                          compounding = "continuous")
tickers <- c("AMZN", "ZM", "UBER", "NFLX", "SHOP", "FB", "UPWK")
securities_returns <- get_prices_from_tickers(tickers,
                                              start = as.Date("2019-04-01"),
                                              end = as.Date("2020-04-01"),
                                              quote = "Close",
                                              retclass = "zoo") %>%
    get_rates_from_prices(quote = "Close",
                          multi_day = TRUE,
                          compounding = "continuous") %>%
    apply_market_model(regressor = rates_indx,
                       same_regressor_for_all = TRUE,
                       market_model = "mrkt_adj",
                       estimation_start = as.Date("2019-04-01"),
                       estimation_end = as.Date("2020-03-13"))

## End(Not run)
## The result of the code above is equivalent to:
data(rates, rates_indx)
securities_returns <- apply_market_model(
    rates = rates,
    regressor = rates_indx,
    same_regressor_for_all = TRUE,
    market_model = "mrkt_adj",
    estimation_start = as.Date("2019-04-01"),
    estimation_end = as.Date("2020-03-13")
)

## 3. Single-index market model
## Not run: 
library("magrittr")
rates_indx <- get_prices_from_tickers("^GSPC",
                                      start = as.Date("2019-04-01"),
                                      end = as.Date("2020-04-01"),
                                      quote = "Close",
                                      retclass = "zoo") %>%
    get_rates_from_prices(quote = "Close",
                          multi_day = TRUE,
                          compounding = "continuous")
tickers <- c("AMZN", "ZM", "UBER", "NFLX", "SHOP", "FB", "UPWK")
securities_returns <- get_prices_from_tickers(tickers,
                                              start = as.Date("2019-04-01"),
                                              end = as.Date("2020-04-01"),
                                              quote = "Close",
                                              retclass = "zoo") %>%
    get_rates_from_prices(quote = "Close",
                          multi_day = TRUE,
                          compounding = "continuous") %>%
    apply_market_model(regressor = rates_indx,
                       same_regressor_for_all = TRUE,
                       market_model = "sim",
                       estimation_method = "ols",
                       estimation_start = as.Date("2019-04-01"),
                       estimation_end = as.Date("2020-03-13"))

## End(Not run)
## The result of the code above is equivalent to:
data(rates, rates_indx)
securities_returns <- apply_market_model(
    rates = rates,
    regressor = rates_indx,
    same_regressor_for_all = TRUE,
    market_model = "sim",
    estimation_method = "ols",
    estimation_start = as.Date("2019-04-01"),
    estimation_end = as.Date("2020-03-13")
)

irudnyts/estudy2 documentation built on Nov. 18, 2024, 2:48 a.m.