expweightcov

A common way to get estimates for time-varying covariance matrices
is the compute the exponentially weighted empirical covariance matrix.

Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving <doi:10.1080/10618600.2017.1322091>. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix <doi:10.1016/j.jeconom.2018.11.007>.

Gregor Kastner

factorstochvol

Bayesian Estimation of (Sparse) Latent Factor Stochastic
Volatility Models

Darjus Hosszejni

Luis Gruber

expweightcov function

<dl><dt>dat</dt>
<dd>Matrix containing the data, with <code>n</code> rows
(points in time) and <code>m</code> columns (component series).</dd>
<dt>alpha</dt>
<dd>Speed of decay.</dd>
<dt>hist</dt>
<dd>How far to go back in time?</dd></dl>

Arguments

Computes the empirical exponentially weighted covariance matrix — expweightcov

<dl>

<dt>dat</dt>
<dd>Matrix containing the data, with <code>n</code> rows
(points in time) and <code>m</code> columns (component series).</dd>


<dt>alpha</dt>
<dd>Speed of decay.</dd>


<dt>hist</dt>
<dd>How far to go back in time?</dd>

</dl>

expweightcov: Computes the empirical exponentially weighted covariance matrix

Description

Usage

Value

Arguments