ldhmm.decode_stats_history

This utility estimates historical statistics (mean, volatility and kurtosis) according to the state probabilities.
The ldhmm object must have been decoded by running through <code>ldhmm.decoding</code> function.
Note that kurtosis is naively implemented as the linear sum from each state weighted by state probabilities.
It is subject to change to more rigorous formula in future releases.

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.decode_stats_history function

<dl><dt>object</dt>
<dd>a decoded ldhmm object</dd>
<dt>ma.order</dt>
<dd>a positive integer or zero, specifying order of moving average. Default is zero.</dd>
<dt>annualize</dt>
<dd>logical, to annaulize the sd and mean to V (xsqrt(days.pa)x100) and R (xdays.pa). 
Default is <code>FALSE</code>.</dd>
<dt>days.pa</dt>
<dd>a positive integer, specifying number of days per year, default is 252.</dd></dl>

Arguments

Author

Estimating historical statistics (mean, volatility and kurtosis) — ldhmm.decode_stats_history

<dl>

<dt>object</dt>
<dd>a decoded ldhmm object</dd>


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


<dt>annualize</dt>
<dd>logical, to annaulize the sd and mean to V (xsqrt(days.pa)x100) and R (xdays.pa). 
Default is <code>FALSE</code>.</dd>


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

</dl>

ldhmm.decode_stats_history: Estimating historical statistics (mean, volatility and kurtosis)

Description

Usage

Value

Arguments

Author