Asymmetric long memory GARCH: a reply to Hwang's model

Hwang (2001) proposes the FIFGARCH model to represent long memory asymmetric conditional variance. Although he claims that this model nests many previous models, we show that it does not and that the model is badly specified. We propose and alternative specification

Asymmetric long memory garch: a reply to hwang's model

Hwang (Econom. Lett. 71 (2001) 1) proposes the FIFGARCH model to represent long memory asymmetric conditional variances. However, the model is badly specified and does not nest some fractionally integrated heteroskedastic models previously proposed. We suggest an alternative specification and illustrate the results with simulated data.Publicad

Properties of the sample autocorrelations of non-linear transformations in long memory stochastic volatility models

The autocorrelations of log-squared, squared, and absolute financial returns are often used to infer the dynamic properties of the underlying volatility. This article shows that, in the context of long-memory stochastic volatility models, these autocorrelations are smaller than the autocorrelations of the log volatility and so is the rate of decay for squared and absolute returns. Furthermore, the corresponding sample autocorrelations could have severe negative biases, making the identification of conditional heteroscedasticity and long memory a difficult task. Finally, we show that the power of some popular tests for homoscedasticity is larger when they are applied to absolute returns.Publicad

Finite sample properties of a QML estimator of Stochastic Volatility models with long memory

We analyse the finite sample properties of a QML estimator of LMSV models. We show up its poor performance for realistic parameter values. We discuss an identification problem when the volatility has a unit root. An empirical analysis illustrates our findings.Publicad

PROPERTIES OF THE SAMPLE AUTOCORRELATIONS IN AUTOREGRESSIVE STOCHASTIC VOLATlLITY MODELS

Time series generated by Stochastic Volatility (SV) processes are uncorrelated although not independent. This has consequences on the properties of the sample autocorrelations. In this paper, we analyse the asymptotic and finite sample properties of the correlogram of series generated by SV processes. It is shown that the usual uncorrelatedness tests could be misleading. The properties of the correlogram of the log-squared series, often used as a diagnostic of conditional heteroscedasticity, are also analysed. It is proven that the more persistent and the larger the variance of volatility, the larger the negative bias of fue sample autocorrelations of that series.

Stochastic volatility models and the Taylor effect

It has been often empirically observed that the sample autocorrelations of absolute financial returns are larger than those of squared returns. This property, know as Taylor effect, is analysed in this paper in the Stochastic Volatility (SV) model framework. We show that the stationary autoregressive SV model is able to generate this property for realistic parameter specifications. On the other hand, the Taylor effect is shown not to be a sampling phenomena due to estimation biases of the sample autocorrelations. Therefore, financial models that aims to explain the behaviour of financial returns should take account of this property

Tax Reforms in an Endogenous Growth Model with Pollution

This paper discusses the effects of a green tax reform in an AK growth model without abatement activities and with a negative environmental externality in utility function. There is also a non-optimal level of public spending. The results depend on the financing source of public spending. When there is not public debt, a revenue-neutral green tax reform has not any effect on pollution, growth and welfare. On the contrary, when short-run deficits are financed by debt issuing, a variety of green tax reforms increase welfare. Nevertheless, in this framework, non-green tax reforms are also welfare improving.Environmental externalities, Economic growth, Pollution taxes, Laffer Curve.

Vigor, rendimiento y composición del mosto en viñedos de Tempranillo afectados por clorosis férrica en la D.O. Ribera del Duero

La clorosis férrica o carencia nutricional de hierro, es una de las principales fisiopatías que afectan a la viña. Habitualmente, dicha carencia, se produce en suelos con un alto contenido en caliza activa y elevado pH. Siendo este mineral un constituyente esencial para las plantas, su carencia es capaz de alterar gran número de procesos fisiológicos en los que interviene. El objetivo de este trabajo consiste en evaluar los efectos de la carencia nutricional del hierro sobre la actividad fotosintética, el vigor, las componentes del rendimiento y los parámetros de composición de la uva en las condiciones ecofisiológicas de la Denominación de Origen Ribera del Duero. Por otra parte se pretende analizar si los niveles de clorofila y los parámetros de eficiencia fotosintética a nivel foliar, en época de envero, son útiles para estimar el rendimiento y la calidad de la vendimia en viñedos afectados por clorosis férrica. Para poder llevar a cabo los objetivos citados, se ha realizado un seguimiento en campo de la actividad fotosintética y los niveles de clorofila en un conjunto de 20 subparcelas de viñedo en la DO Ribera del Duero, durante el año 2014. También se obtuvieron los valores de vigor, rendimiento y composición de la uva (contenido en ácido málico, potasio, acidez total, IPT, Brix, ypH). El análisis estadístico de los datos incluyó análisis de varianza y métodos de regresión lineal.Grado en Enologí