logret

logret.default

<code>logret</code> computes the log returns of a time
series, with optional de-meaning and/or standardization.

Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002> and Hosszejni and Kastner (2019) <doi:10.1007/978-3-030-30611-3_8>; the most common use cases are described in Hosszejni and Kastner (2021) <doi:10.18637/jss.v100.i12> and Kastner (2016) <doi:10.18637/jss.v069.i05> and the package examples.

Darjus Hosszejni

stochvol

Efficient Bayesian Inference for Stochastic Volatility (SV)
Models

Gregor Kastner

logret function

<dl><dt>dat</dt>
<dd>The raw data.</dd>
<dt>demean</dt>
<dd>Logical value indicating whether the data should
be de-meaned.</dd>
<dt>standardize</dt>
<dd>Logical value indicating whether the data should
be standardized (in the sense that each component series has an empirical
variance equal to one).</dd>
<dt>...</dt>
<dd>Ignored.</dd></dl>

Arguments

<ul>
<li><code>logret(default)</code>: Log returns of vectors</li>
</ul>

Methods (by class)

Computes the Log Returns of a Time Series — logret

<dl>

<dt>dat</dt>
<dd>The raw data.</dd>


<dt>demean</dt>
<dd>Logical value indicating whether the data should
be de-meaned.</dd>


<dt>standardize</dt>
<dd>Logical value indicating whether the data should
be standardized (in the sense that each component series has an empirical
variance equal to one).</dd>


<dt>...</dt>
<dd>Ignored.</dd>

</dl>

logret: Computes the Log Returns of a Time Series

Description

Usage

Value

Arguments

Methods (by class)