ldhmm.sma

This utility calculates simple moving average, with option to backfill for NA.

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.sma function

<dl><dt>x</dt>
<dd>numeric, the time series.</dd>
<dt>order</dt>
<dd>a positive integer to specify order of moving average.</dd>
<dt>na.backfill</dt>
<dd>logical, specify whether to backfill for NA. Default is <code>TRUE</code>.</dd></dl>

Arguments

Author

Simple moving average of a time series — ldhmm.sma

<dl>

<dt>x</dt>
<dd>numeric, the time series.</dd>


<dt>order</dt>
<dd>a positive integer to specify order of moving average.</dd>


<dt>na.backfill</dt>
<dd>logical, specify whether to backfill for NA. Default is <code>TRUE</code>.</dd>

</dl>

ldhmm.sma: Simple moving average of a time series

Description

Usage

Value

Arguments

Author

Examples