<?xml version="1.0" encoding="UTF-8" standalone="no" ?>
<rss version="2.0">
  <channel>
    <title>Neele, J.</title>
    <link>http://repub.eur.nl/res/aut/6421/</link>
    <description>List of Publications</description>
    <language>en</language>
    <image>
      <url>http://repub.eur.nl/static-eur/img/logo.png</url>
      <title>RePub, Erasmus University Rotterdam</title>
      <link>http://repub.eur.nl</link>
    </image>
    <item>
      <title>Modeling asymmetric volatility in weekly Dutch temperature data (Article)</title>
      <link>http://repub.eur.nl/res/pub/11153/</link>
      <pubDate>2001-03-13T00:00:00Z</pubDate>
      <description>In addition to clear-cut seasonality in mean and variance, weekly Dutch temperature data appear to have a strong asymmetry in the impact of unexpectedly high or low temperatures on conditional volatility. Furthermore, this asymmetry also shows fairly pronounced seasonal variation. To describe these features, we propose a univariate seasonal time series model with asymmetric conditionally heteroskedastic errors. We fit this (and other, nested) model(s) to 25 years of weekly data. We evaluate its forecasting performance for 5 years of hold-out data and find that the imposed asymmetry leads to better out-of-sample forecasts of temperature volatility.</description>
    </item> <item>
      <title>Modeling asymmetric volatility in weekly Dutch temperature data (Research Paper)</title>
      <link>http://repub.eur.nl/res/pub/1533/</link>
      <pubDate>1998-09-21T00:00:00Z</pubDate>
      <description>In addition to clear-cut seasonality in mean and variance, weekly Dutch temperature data appear to have a strong asymmetry in the impact of unexpectedly high or low temperatures on conditional volatility. Furthermore, this asymmetry also shows fairly pronounced seasonal variation. To describe these features, we propose a univariate seasonal time series model with asymmetric conditionally heteroskedastic errors. We fit this (and other, nested) model(s) to 25 years of weekly data. We evaluate its
forecasting performance for 5 years of hold-out data and find that the imposed asymmetry leads to better out-of-sample forecasts of temperature
volatility.</description>
    </item> <item>
      <title>Forecasting volatility with switching persistence GARCH models (Research Paper)</title>
      <link>http://repub.eur.nl/res/pub/1553/</link>
      <pubDate>1998-06-16T00:00:00Z</pubDate>
      <description>In this paper we examine the forecasting performance of five nonlinear GARCH(1,1) models. Four of these have recently been proposed in literature, while the fifth model is a new one. All five models allow for switching
persistence of shocks, depending on the value and/or sign of recent returns.
We consider the models for weekly data on 5 major stock markets. Our results indicate that all models improve upon the linear GARCH(1,1) model and that our new model sometimes yields favorable forecasting results.</description>
    </item>
  </channel>
</rss>