esr_loss

Simultaneous modeling of the quantile and the expected shortfall of a response variable given
a set of covariates, see Dimitriadis and Bayer (2019) <doi:10.1214/19-EJS1560>.

Sebastian Bayer

esreg

Joint Quantile and Expected Shortfall Regression

Timo Dimitriadis

esr_loss function

<dl><dt>r</dt>
<dd>Vector of returns</dd>
<dt>q</dt>
<dd>Vector of quantiles</dd>
<dt>e</dt>
<dd>Vector of expected shortfalls</dd>
<dt>alpha</dt>
<dd>Probability level</dd>
<dt>g1</dt>
<dd>1, 2, see G1_fun</dd>
<dt>g2</dt>
<dd>1, 2, 3, 4, 5, see G2_curly_fun, G2_fun</dd>
<dt>return_mean</dt>
<dd>If TRUE returns the average tick loss, else the individual values</dd></dl>

Arguments

Joint Loss Function — esr_loss

<dl>

<dt>r</dt>
<dd>Vector of returns</dd>


<dt>q</dt>
<dd>Vector of quantiles</dd>


<dt>e</dt>
<dd>Vector of expected shortfalls</dd>


<dt>alpha</dt>
<dd>Probability level</dd>


<dt>g1</dt>
<dd>1, 2, see G1_fun</dd>


<dt>g2</dt>
<dd>1, 2, 3, 4, 5, see G2_curly_fun, G2_fun</dd>


<dt>return_mean</dt>
<dd>If TRUE returns the average tick loss, else the individual values</dd>

</dl>

esr_loss: Joint Loss Function

Description

Usage

Arguments

References