ldhmm.decoding

This utility computes the state probabilities, uses local and global decoding to calculate the states.
The results are saved to the returned <code>ldhmm</code> object.

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.decoding function

<dl><dt>object</dt>
<dd>an ldhmm object</dd>
<dt>x</dt>
<dd>numeric, the observations.</dd>
<dt>do.global</dt>
<dd>logical, if <code>TRUE</code> (default), perform Viterbi decoding.</dd>
<dt>do.stats</dt>
<dd>logical, if <code>TRUE</code> (default), calculate stats.</dd></dl>

Arguments

Author

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

<dl>

<dt>object</dt>
<dd>an ldhmm object</dd>


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


<dt>do.global</dt>
<dd>logical, if <code>TRUE</code> (default), perform Viterbi decoding.</dd>


<dt>do.stats</dt>
<dd>logical, if <code>TRUE</code> (default), calculate stats.</dd>

</dl>

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

Description

Usage

Value

Arguments

Author