chart.RollingRegression: A wrapper to create charts of relative regression performance...

In guillermozbta/portafolio-master: Econometric tools for performance and risk analysis.

Description

A wrapper to create a chart of relative regression performance through time

Usage

chart.RollingQuantileRegression(Ra, Rb, width = 12, Rf = 0,
  attribute = c("Beta", "Alpha", "R-Squared"), main = NULL, na.pad = TRUE,
  ...)

chart.RollingRegression(Ra, Rb, width = 12, Rf = 0, attribute = c("Beta",
  "Alpha", "R-Squared"), main = NULL, na.pad = TRUE, ...)

charts.RollingRegression(Ra, Rb, width = 12, Rf = 0, main = NULL,
  legend.loc = NULL, event.labels = NULL, ...)

Arguments

Ra	an xts, vector, matrix, data frame, timeSeries or zoo object of asset returns
Rb	return vector of the benchmark asset
Rf	risk free rate, in same period as your returns
width	number of periods to apply rolling function window over
attribute	one of "Beta","Alpha","R-Squared" for which attribute to show
main	set the chart title, same as in plot
na.pad	TRUE/FALSE If TRUE it adds any times that would not otherwise have been in the result with a value of NA. If FALSE those times are dropped.
legend.loc	used to set the position of the legend
event.labels	if not null and event.lines is not null, this will apply a list of text labels to the vertical lines drawn
...	any other passthru parameters to chart.TimeSeries

Details

A group of charts in charts.RollingRegression displays alpha, beta, and R-squared estimates in three aligned charts in a single device.

The attribute parameter is probably the most confusing. In mathematical terms, the different choices yield the following:

Alpha - shows the y-intercept
Beta - shows the slope of the regression line
R-Squared - shows the degree of fit of the regression to the data

chart.RollingQuantileRegression uses rq rather than lm for the regression, and may be more robust to outliers in the data.

Note

Most inputs are the same as "plot" and are principally included so that some sensible defaults could be set.

Author(s)

Peter Carl

See Also

lm
rq

Examples

# First we load the data
data(managers)
chart.RollingRegression(managers[, 1, drop=FALSE],
		managers[, 8, drop=FALSE], Rf = .04/12)
charts.RollingRegression(managers[, 1:6],
		managers[, 8, drop=FALSE], Rf = .04/12,
		colorset = rich6equal, legend.loc="topleft")
dev.new()
chart.RollingQuantileRegression(managers[, 1, drop=FALSE],
		managers[, 8, drop=FALSE], Rf = .04/12)
# not implemented yet
#charts.RollingQuantileRegression(managers[, 1:6],
#		managers[, 8, drop=FALSE], Rf = .04/12,
#		colorset = rich6equal, legend.loc="topleft")

guillermozbta/portafolio-master documentation built on May 11, 2019, 7:20 p.m.