residualizeReturns

#' function to Residualize the returns via regressions

Linear cross-section factor model fitting with least-squares
and robust fitting the 'lmrobdetMM()' function from 'RobStatTM'; related
volatility, Value at Risk and Expected Shortfall risk and performance
attribution (factor-contributed vs idiosyncratic returns);
tabular displays of risk and performance reports;
factor model Monte Carlo. The package authors would like to thank Chicago
Research on Security Prices,LLC for the cross-section of about 300
CRSP stocks data (in the data.table object 'stocksCRSP', and S&P GLOBAL MARKET
INTELLIGENCE for contributing 14 factor scores (a.k.a "alpha factors".and
"factor exposures") fundamental data on the 300 companies in the data.table
object 'factorSPGMI'.  The 'stocksCRSP' and 'factorsSPGMI' data are not covered by
the GPL-2 license, are not provided as open source of any kind, and they are
not to be redistributed in any form.

Mido Shammaa

facmodCS

Cross-Section Factor Models

Doug Martin

Kirk Li

Avinash Acharya

Lingjie Yi

residualizeReturns function

<dl><dt>specObj</dt>
<dd>specObj is a ffmSpec object,</dd>
<dt>benchmark</dt>
<dd>we might need market returns</dd>
<dt>rfRate</dt>
<dd>risk free rate</dd>
<dt>isBenchExcess</dt>
<dd>toggle to select whether to calculate excess returns</dd></dl>

Arguments

residualizeReturns — residualizeReturns

<dl>

<dt>specObj</dt>
<dd>specObj is a ffmSpec object,</dd>


<dt>benchmark</dt>
<dd>we might need market returns</dd>


<dt>rfRate</dt>
<dd>risk free rate</dd>


<dt>isBenchExcess</dt>
<dd>toggle to select whether to calculate excess returns</dd>

</dl>

residualizeReturns: residualizeReturns

Description

Usage

Value

Arguments

Details

See Also