ldhmm.forecast_state

This utility computes the state forecast, given the sequence of observations in the past.

forecast

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.forecast_state function

<dl><dt>object</dt>
<dd>an ldhmm object</dd>
<dt>x</dt>
<dd>numeric, the observations.</dd>
<dt>h</dt>
<dd>integer, time steps to forecast.</dd></dl>

Arguments

Author

Computing the state forecast — ldhmm.forecast_state

<dl>

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


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


<dt>h</dt>
<dd>integer, time steps to forecast.</dd>

</dl>

ldhmm.forecast_state: Computing the state forecast

Description

Usage

Value

Arguments

Author