ldhmm.mllk

This utility computes the MLLK. It is typically invoked by the MLE optimizer. (Zucchini, 3.2)

mllk

Hidden Markov Model (HMM) based on symmetric lambda distribution
framework is implemented for the study of return time-series in the financial
market. Major features in the S&P500 index, such as regime identification,
volatility clustering, and anti-correlation between return and volatility,
can be extracted from HMM cleanly. Univariate symmetric lambda distribution
is essentially a location-scale family of exponential power distribution.
Such distribution is suitable for describing highly leptokurtic time series
obtained from the financial market. It provides a theoretically solid foundation
to explore such data where the normal distribution is not adequate. The HMM
implementation follows closely the book: "Hidden Markov Models for Time Series",
by Zucchini, MacDonald, Langrock (2016).

Stephen Lihn

ldhmm

Hidden Markov Model for Financial Time-Series Based on Lambda
Distribution

ldhmm.mllk function

<dl><dt>object</dt>
<dd>an input ldhmm object to provide static reference, 
such as m, param.nbr, stationary.</dd>
<dt>x</dt>
<dd>numeric, the observations.</dd>
<dt>mllk.print.level</dt>
<dd>numeric, this argument determines the level of printing 
which is done during the minimization process. 
The default value of 0 means that no printing occurs, 
a value of 1 or greater means some tracing information is printed.</dd></dl>

Arguments

Author

Computing the minus log-likelihood (MLLK) — ldhmm.mllk

<dl>

<dt>object</dt>
<dd>an input ldhmm object to provide static reference, 
such as m, param.nbr, stationary.</dd>


<dt>x</dt>
<dd>numeric, the observations.</dd>


<dt>mllk.print.level</dt>
<dd>numeric, this argument determines the level of printing 
which is done during the minimization process. 
The default value of 0 means that no printing occurs, 
a value of 1 or greater means some tracing information is printed.</dd>

</dl>

ldhmm.mllk: Computing the minus log-likelihood (MLLK)

Description

Usage

Value

Arguments

Author