fit_model

print.fHMM_model

This function fits a HMM to <code>fHMM_data</code> via numerical likelihood
maximization.

Fitting (hierarchical) hidden Markov models to financial data
via maximum likelihood estimation. See Oelschläger, L. and Adam, T.
"Detecting bearish and bullish markets in financial time series using
hierarchical hidden Markov models" (2021, Statistical Modelling)
<doi:10.1177/1471082X211034048> for a reference.

Lennart Oelschläger

fHMM

Fitting Hidden Markov Models to Financial Data

Timo Adam

Rouven Michels

fit_model function

<dl><dt>data</dt>
<dd>An object of class <code>fHMM_data</code>.</dd>
<dt>ncluster</dt>
<dd>Set the number of clusters for parallelization.
By default, <code>ncluster = 1</code>.</dd>
<dt>seed</dt>
<dd>Set a seed for the sampling of initial values.
No seed by default.</dd>
<dt>verbose</dt>
<dd>Set to <code>TRUE</code> to print progress messages.</dd>
<dt>init</dt>
<dd>Optionally an object of class <code>parUncon</code> for initialization. This can
for example be the estimate of a previously fitted model <code>model</code>, i.e.
the element <code>model$estimate</code>. The initial values are computed via
<code>replicate(n, jitter(init, amount = 1), simplify = FALSE)</code>,
where <code>n &lt;- data$controls$fit$runs</code>.</dd>
<dt>x</dt>
<dd>An object of class <code>fHMM_model</code>.</dd>
<dt>...</dt>
<dd>Currently not used.</dd></dl>

Arguments

Model fitting — fit_model

<dl>

<dt>data</dt>
<dd>An object of class <code>fHMM_data</code>.</dd>


<dt>ncluster</dt>
<dd>Set the number of clusters for parallelization.
By default, <code>ncluster = 1</code>.</dd>


<dt>seed</dt>
<dd>Set a seed for the sampling of initial values.
No seed by default.</dd>


<dt>verbose</dt>
<dd>Set to <code>TRUE</code> to print progress messages.</dd>


<dt>init</dt>
<dd>Optionally an object of class <code>parUncon</code> for initialization. This can
for example be the estimate of a previously fitted model <code>model</code>, i.e.
the element <code>model$estimate</code>. The initial values are computed via
<code>replicate(n, jitter(init, amount = 1), simplify = FALSE)</code>,
where <code>n &lt;- data$controls$fit$runs</code>.</dd>


<dt>x</dt>
<dd>An object of class <code>fHMM_model</code>.</dd>


<dt>...</dt>
<dd>Currently not used.</dd>

</dl>

fit_model: Model fitting

Description

Usage

Value

Arguments

Details