ecld

Construct an <code>ecld-class</code> by providing the required parameters.
The default is the standard symmetric cusp distribution (lambda=3).

Constructor

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

ecld function

<dl><dt>lambda</dt>
<dd>numeric, the lambda parameter. Must be positive. Default: 3.</dd>
<dt>sigma</dt>
<dd>numeric, the scale parameter. Must be positive. Default: 1.</dd>
<dt>mu</dt>
<dd>numeric, the location parameter. Default: 0.</dd>
<dt>verbose</dt>
<dd>logical, display timing information, for debugging purpose, default is <code>FALSE</code>.</dd></dl>

Arguments

Author

Constructor of ecld class — ecld

<dl>

<dt>lambda</dt>
<dd>numeric, the lambda parameter. Must be positive. Default: 3.</dd>


<dt>sigma</dt>
<dd>numeric, the scale parameter. Must be positive. Default: 1.</dd>


<dt>mu</dt>
<dd>numeric, the location parameter. Default: 0.</dd>


<dt>verbose</dt>
<dd>logical, display timing information, for debugging purpose, default is <code>FALSE</code>.</dd>

</dl>

ecld: Constructor of ecld class

Description

Usage

Value

Arguments

Author

Examples