ldhmm.read_sample_object

This utility is used to read sample ldhmm object so that the user doesn't need to go through
lengthy optimization process to obtain a trained HMM for advanced features.

data

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.read_sample_object function

<dl><dt>symbol</dt>
<dd>Character for the symbol of the time series. Default is <code>spx-daily-m10</code></dd>
<dt>extdata_dir</dt>
<dd>optionally specify user's own extdata folder</dd></dl>

Arguments

Author

Read sample ldhmm object — ldhmm.read_sample_object

<dl>

<dt>symbol</dt>
<dd>Character for the symbol of the time series. Default is <code>spx-daily-m10</code></dd>


<dt>extdata_dir</dt>
<dd>optionally specify user's own extdata folder</dd>

</dl>

ldhmm.read_sample_object: Read sample ldhmm object

Description

Usage

Value

Arguments

Author

Examples