ldhmm.forecast_volatility

This utility computes the volatility forecast based on the given future observations for next one period.

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_volatility function

<dl><dt>object</dt>
<dd>an ldhmm object</dd>
<dt>x</dt>
<dd>numeric, the observations.</dd>
<dt>xf</dt>
<dd>numeric, the future observations to be forecasted.</dd>
<dt>ma.order</dt>
<dd>a positive integer or zero, specifying order of moving average. Default is zero.</dd>
<dt>days.pa</dt>
<dd>a positive integer specifying trading days per year, default is 252.</dd></dl>

Arguments

Author

Computing the volatility forecast for next one period — ldhmm.forecast_volatility

<dl>

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


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


<dt>xf</dt>
<dd>numeric, the future observations to be forecasted.</dd>


<dt>ma.order</dt>
<dd>a positive integer or zero, specifying order of moving average. Default is zero.</dd>


<dt>days.pa</dt>
<dd>a positive integer specifying trading days per year, default is 252.</dd>

</dl>

ldhmm.forecast_volatility: Computing the volatility forecast for next one period

Description

Usage

Value

Arguments

Author