returns: Constructor of an object of S3 class 'returns'.
In estudy2: An Implementation of Parametric and Nonparametric Event Study
Description
Usage
Arguments
Details
Value
References
See Also
Examples
View source: R/apply_market_model.R
Description
Constructs an object of S3 class returns.
Usage
1
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8
Arguments
rates
an object of class either zoo or data.frame
giving observed rates of returns of security.
regressor
an object of the same class as rates representing
rates of returns of the market model, if needed.
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 constructor is a generic function, dispatched for classes zoo
data.frame. Parameters rates and regressor should be
objects of the same class (zoo or data.frame). There are three
market model implemented. mean_adj stands for mean-adjusted-returns
model, which is the average of returns during the estimation period.
mrkt_adj represents market-adjusted-returns model: the securities'
rates of returns are simply market index rates of returns (in terms of
parameters - regressor). Finally, sim stands for single-index
market model For this model only Ordinary Least Squares
estimation_method is currently implemented. All models are described
in Brown and Warner (1985).
Value
An object of S3 class returns, which contains following
fields:
observed: an object of zoo class containing observed rates of
returns.
predicted: an object of zoo class containing predicted by a
market model rates of returns.
lower95CI: a lower bound of the 95% Confidence Interval for
predicted rates of returns.
upper95CI: an upper bound of the 95% Confidence Interval for
predicted rates of returns.
abnormal: an object of zoo class containing abnormal returns.
regressor: an object of zoo class containing rates of
regressor (typically market index).
market_model: a code name of the market model.
full_name_market_model: a full name of the market model.
estimation_method: a code name of the estimation method (applied only
for SIM).
full_name_estimation_method: a full name of the estimation method
(applied only for SIM).
coefficients: coefficients α and β for SIM market
model (applied only for SIM).
estimation_start: a start date of the estimation period.
estimation_end: an end date of the estimation period.
estimation_length: a length of the estimation period.
References
Brown S.J., Warner J.B. Using Daily Stock Returns, The Case
of Event Studies. Journal of Financial Economics, 14:3-31, 1985.
See Also
Examples
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library("zoo")
## 1. Mean-adjusted-returns model
## Not run:
library("magrittr")
single_return <- get_prices_from_tickers("AMZN",
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") %>%
returns(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)
single_return <- returns(rates[, "AMZN"],
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")
single_return <- get_prices_from_tickers("AMZN",
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") %>%
returns(regressor = rates_indx,
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)
single_return <- returns(rates = rates[, "AMZN", drop = FALSE],
regressor = rates_indx,
market_model = "mrkt_adj",
estimation_method = "ols",
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")
single_return <- get_prices_from_tickers("AMZN",
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") %>%
returns(regressor = rates_indx,
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)
single_return <- returns(rates = rates[, "AMZN", drop = FALSE],
regressor = rates_indx,
market_model = "sim",
estimation_method = "ols",
estimation_start = as.Date("2019-04-01"),
estimation_end = as.Date("2020-03-13"))
estudy2 documentation built on Nov. 15, 2021, 5:09 p.m.
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Description Usage Arguments Details Value References See Also Examples
View source: R/apply_market_model.R
Description
Constructs an object of S3 class returns.
Usage
1 2 3 4 5 6 7 8 |
Arguments
rates |
an object of class either |
regressor |
an object of the same class as |
market_model |
a character indicating the market model among
|
estimation_method |
a character specifying an estimation method for
|
estimation_start |
an object of |
estimation_end |
an object of |
Details
The constructor is a generic function, dispatched for classes zoo
data.frame. Parameters rates and regressor should be
objects of the same class (zoo or data.frame). There are three
market model implemented. mean_adj stands for mean-adjusted-returns
model, which is the average of returns during the estimation period.
mrkt_adj represents market-adjusted-returns model: the securities'
rates of returns are simply market index rates of returns (in terms of
parameters - regressor). Finally, sim stands for single-index
market model For this model only Ordinary Least Squares
estimation_method is currently implemented. All models are described
in Brown and Warner (1985).
Value
An object of S3 class returns, which contains following
fields:
observed: an object of
zooclass containing observed rates of returns.predicted: an object of
zooclass containing predicted by a market model rates of returns.lower95CI: a lower bound of the 95% Confidence Interval for predicted rates of returns.
upper95CI: an upper bound of the 95% Confidence Interval for predicted rates of returns.
abnormal: an object of
zooclass containing abnormal returns.regressor: an object of
zooclass containing rates of regressor (typically market index).market_model: a code name of the market model.
full_name_market_model: a full name of the market model.
estimation_method: a code name of the estimation method (applied only for SIM).
full_name_estimation_method: a full name of the estimation method (applied only for SIM).
coefficients: coefficients α and β for SIM market model (applied only for SIM).
estimation_start: a start date of the estimation period.
estimation_end: an end date of the estimation period.
estimation_length: a length of the estimation period.
References
Brown S.J., Warner J.B. Using Daily Stock Returns, The Case of Event Studies. Journal of Financial Economics, 14:3-31, 1985.
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | library("zoo")
## 1. Mean-adjusted-returns model
## Not run:
library("magrittr")
single_return <- get_prices_from_tickers("AMZN",
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") %>%
returns(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)
single_return <- returns(rates[, "AMZN"],
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")
single_return <- get_prices_from_tickers("AMZN",
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") %>%
returns(regressor = rates_indx,
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)
single_return <- returns(rates = rates[, "AMZN", drop = FALSE],
regressor = rates_indx,
market_model = "mrkt_adj",
estimation_method = "ols",
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")
single_return <- get_prices_from_tickers("AMZN",
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") %>%
returns(regressor = rates_indx,
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)
single_return <- returns(rates = rates[, "AMZN", drop = FALSE],
regressor = rates_indx,
market_model = "sim",
estimation_method = "ols",
estimation_start = as.Date("2019-04-01"),
estimation_end = as.Date("2020-03-13"))
|
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