96 research outputs found
AR and MA representation of partial autocorrelation functions, with applications
We prove a representation of the partial autocorrelation function (PACF), or
the Verblunsky coefficients, of a stationary process in terms of the AR and MA
coefficients. We apply it to show the asymptotic behaviour of the PACF. We also
propose a new definition of short and long memory in terms of the PACF.Comment: Published in Probability Theory and Related Field
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An extreme value theory approach to calculating minimum capital risk requirements
This paper investigates the frequency of extreme events for three LIFFE futures contracts for
the calculation of minimum capital risk requirements (MCRRs). We propose a semiparametric
approach where the tails are modelled by the Generalized Pareto Distribution and
smaller risks are captured by the empirical distribution function. We compare the capital
requirements form this approach with those calculated from the unconditional density and
from a conditional density - a GARCH(1,1) model. Our primary finding is that both in-sample
and for a hold-out sample, our extreme value approach yields superior results than either of
the other two models which do not explicitly model the tails of the return distribution. Since
the use of these internal models will be permitted under the EC-CAD II, they could be widely
adopted in the near future for determining capital adequacies. Hence, close scrutiny of
competing models is required to avoid a potentially costly misallocation capital resources
while at the same time ensuring the safety of the financial system
Estudio de Aplicación del Análisis Regional de Frecuencias Basado en L-Momentos al Caso de las Precipitaciones Anuales en la Provincia de Camagüey, Cuba
ARFIMA-GARCH modeling of HRV: Clinical application in acute brain injury
In the last decade, several HRV based novel methodologies for describing and assessing heart rate dynamics have been proposed in the literature with the aim of risk assessment. Such methodologies attempt to describe the non-linear and complex characteristics of HRV, and hereby the focus is in two of these characteristics, namely long memory and heteroscedasticity with variance clustering. The ARFIMA-GARCH modeling considered here allows the quantification of long range correlations and time-varying volatility. ARFIMA-GARCH HRV analysis is integrated with multimodal brain monitoring in several acute cerebral phenomena such as intracranial hypertension, decompressive craniectomy and brain death. The results indicate that ARFIMA-GARCH modeling appears to reflect changes in Heart Rate Variability (HRV) dynamics related both with the Acute Brain Injury (ABI) and the medical treatments effects. (c) 2017, Springer International Publishing AG
On Selection of Probability Distributions for Representing Annual Extreme Rainfall Series
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