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Type: Journal article
Title: Volatility in high-frequency intensive care mortality time series: application of univariate and multivariate GARCH models
Author: Moran, J.
Solomon, P.
Citation: Open Journal of Applied Sciences, 2017; 7(08):385-411
Publisher: Scientific Research Publishing
Issue Date: 2017
ISSN: 2165-3917
Statement of
John L. Moran, Patricia J. Solomon
Abstract: Mortality time series display time-varying volatility. The utility of statistical estimators from the financial time-series paradigm, which account for this characteristic, has not been addressed for high-frequency mortality series. Using daily mean-mortality series of an exemplar intensive care unit (ICU) from the Australian and New Zealand Intensive Care Society adult patient database, joint estimation of a mean and conditional variance (volatility) model for a stationary series was undertaken via univariate autoregressive moving average (ARMA, lags (p, q)), GARCH (Generalised Autoregressive Conditional Heteroscedasticity, lags (p, q)). The temporal dynamics of the conditional variance and correlations of multiple provider series, from rural/ regional, metropolitan, tertiary and private ICUs, were estimated utilising multivariate GARCH models. For the stationary first differenced series, an asymmetric power GARCH model (lags (1, 1)) with t distribution (degrees-offreedom, 11.6) and ARMA (7,0) for the mean-model, was the best-fitting. The four multivariate component series demonstrated varying trend mortality decline and persistent autocorrelation. Within each MGARCH series no model specification dominated. The conditional correlations were surprisingly low (<0.1) between tertiary series and substantial (0.4 - 0.6) between rural-regional and private series. The conditional-variances of both the univariate and multivariate series demonstrated a slow rate of time decline from periods of early volatility and volatility spikes.
Keywords: Time series; mortality; Intensive Care Unit; ARIMA; GARCH; multivariate GARCH; volatility
Rights: Copyright, by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).
RMID: 0030075356
DOI: 10.4236/ojapps.2017.78030
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Appears in Collections:Mathematical Sciences publications

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