ldhmm.ts_abs_acf

This utility computes the ACF of the absolute value of a time series as a proxy 
of the auto-correlation of the volatility. It allows to drop the largest N outliers
so that they would not skew the ACF calculation.

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.ts_abs_acf function

<dl><dt>x</dt>
<dd>numeric, the observations.</dd>
<dt>drop</dt>
<dd>a positive integer, specifying number of outliers to be dropped.</dd>
<dt>lag.max</dt>
<dd>a positive integer, specifying number of lags to be computed.</dd></dl>

Arguments

Author

Computing ACF of the absolute value of a time series — ldhmm.ts_abs_acf

<dl>

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


<dt>drop</dt>
<dd>a positive integer, specifying number of outliers to be dropped.</dd>


<dt>lag.max</dt>
<dd>a positive integer, specifying number of lags to be computed.</dd>

</dl>

ldhmm.ts_abs_acf: Computing ACF of the absolute value of a time series

Description

Usage

Value

Arguments

Author