ldhmm.w2n

This utility transforms the working parameter array back to
the vectors and matrix of the contrained parameters. (Zucchini, 3.3.1)

Parameter

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.w2n function

<dl><dt>object</dt>
<dd>an ldhmm object that can supply m, param.nbr and stationary.</dd>
<dt>par.vector</dt>
<dd>numeric, linear working parameter array. See <code>ldhmm.n2w</code>.</dd>
<dt>mu.scale</dt>
<dd>numeric, it should mirror what is provided to <code>ldhmm.n2w</code>.
Default is 1.</dd></dl>

Arguments

Author

Transforming working parameter array to natural parameters — ldhmm.w2n

<dl>

<dt>object</dt>
<dd>an ldhmm object that can supply m, param.nbr and stationary.</dd>


<dt>par.vector</dt>
<dd>numeric, linear working parameter array. See <code>ldhmm.n2w</code>.</dd>


<dt>mu.scale</dt>
<dd>numeric, it should mirror what is provided to <code>ldhmm.n2w</code>.
Default is 1.</dd>

</dl>

ldhmm.w2n: Transforming working parameter array to natural parameters

Description

Usage

Value

Arguments

Author