cleanOutliers

Outliers are "cleaned" by shrinking or rejecting data whose
distance from the median (med) is larger in absolute value than a specified
value k multiplied by the median absolute deviation from the median (mad).
Outlier shrinkage results in the data value being set equal to the nearest
of med-k*mad and med+k*mad. Rejected data is assigned an NA. Shrinkage is
the default.

A collection of functions and data sets that support teaching
a quantitative finance MS level course on Portfolio Construction and Risk
Analysis, and the writing of a textbook for such a course.  The package is
unique in providing several real-world data sets that may be used for problem
assignments and student projects.  The data sets include cross-sections of
stock data from the Center for Research on Security Prices, LLC (CRSP),
corresponding factor exposures data from S&P Global, and several SP500 data
sets.

Doug Martin

PCRA

Companion to Portfolio Construction and Risk Analysis

Alexios Galanos

Kirk Li

Jon Spinney

Thomas Philips

cleanOutliers function

<dl><dt>x</dt>
<dd>A numeric vector</dd>
<dt>k</dt>
<dd>A numeric value, which multiplies the mad. Smaller values of k
result in greater fractions of data which is either shrunk of rejected,
and larger values of k result in smaller fractions of the data that are
shrunk or rejected.</dd>
<dt>shrink</dt>
<dd>A logical variable whose default is TRUE.</dd></dl>

Arguments

Clean Returns Outliers — cleanOutliers

<dl>

<dt>x</dt>
<dd>A numeric vector</dd>


<dt>k</dt>
<dd>A numeric value, which multiplies the mad. Smaller values of k
result in greater fractions of data which is either shrunk of rejected,
and larger values of k result in smaller fractions of the data that are
shrunk or rejected.</dd>


<dt>shrink</dt>
<dd>A logical variable whose default is TRUE.</dd>

</dl>

cleanOutliers: Clean Returns Outliers

Description

Usage

Value

Arguments

Examples