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    <title>EconStor Community: Institut für Wirtschaftspolitik und Quantitative Wirtschaftsforschung (IWQW)</title>
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        <rdf:li resource="http://hdl.handle.net/10419/36698" />
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  <item rdf:about="http://hdl.handle.net/10419/36698">
    <title>Using wavelets for time series forecasting: Does it pay off?</title>
    <link>http://hdl.handle.net/10419/36698</link>
    <description>Titel: Using wavelets for time series forecasting: Does it pay off?
&lt;br/&gt;
&lt;br/&gt;Autoren: Schlüter, Stephan; Deuschle, Carola
&lt;br/&gt;
&lt;br/&gt;Zusammenfassung: By means of wavelet transform a time series can be decomposed into a time dependent sum of frequency components. As a result we are able to capture seasonalities with time-varying period and intensity, which nourishes the belief that incorporating the wavelet transform in existing forecasting methods can improve their quality. The article aims to verify this by comparing the power of classical and wavelet based techniques on the basis of four time series, each of them having individual characteristics. We find that wavelets do improve the forecasting quality. Depending on the data's characteristics and on the forecasting horizon we either favour a denoising step plus an ARIMA forecast or an multiscale wavelet decomposition plus an ARIMA forecast for each of the frequency components.</description>
  </item>
  <item rdf:about="http://hdl.handle.net/10419/36698">
    <title>Using wavelets for time series forecasting: Does it pay off?</title>
    <link>http://hdl.handle.net/10419/36698</link>
    <description>Titel: Using wavelets for time series forecasting: Does it pay off?
&lt;br/&gt;
&lt;br/&gt;Autoren: Schlüter, Stephan; Deuschle, Carola
&lt;br/&gt;
&lt;br/&gt;Zusammenfassung: By means of wavelet transform a time series can be decomposed into a time dependent sum of frequency components. As a result we are able to capture seasonalities with time-varying period and intensity, which nourishes the belief that incorporating the wavelet transform in existing forecasting methods can improve their quality. The article aims to verify this by comparing the power of classical and wavelet based techniques on the basis of four time series, each of them having individual characteristics. We find that wavelets do improve the forecasting quality. Depending on the data's characteristics and on the forecasting horizon we either favour a denoising step plus an ARIMA forecast or an multiscale wavelet decomposition plus an ARIMA forecast for each of the frequency components.</description>
  </item>
  <item rdf:about="http://hdl.handle.net/10419/30185">
    <title>Volatility models with innovations from new maximum entropy densities at work</title>
    <link>http://hdl.handle.net/10419/30185</link>
    <description>Titel: Volatility models with innovations from new maximum entropy densities at work
&lt;br/&gt;
&lt;br/&gt;Autoren: Fischer, Matthias; Gao, Yang; Herrmann, Klaus
&lt;br/&gt;
&lt;br/&gt;Zusammenfassung: Generalized autoregressive conditional heteroskedasticity (GARCH) processes have become very popular as models for financial return data because they are able to capture volatility clustering as well as leptokurtic unconditional distributions which result from the assumption of conditionally normal error distributions. In contrast, Bollerslev (1987) and several follow-ups provided evidence that starting with leptokurtic and possibly skewed (conditional) error distributions will achieve better results. Parallel to these exible but to some extend arbitrary chosen parametric distributions, recent years saw a rise in suggestions for maximum entropy distributions (e.g. Rockinger and Jondeau, 2002, Park and Bera, 2009 or Fischer and Herrmann, 2010). Within this contribution we provide a comprehensive comparison between both different ME densities and their parametric competitors within different generalized GARCH models such as APARCH and GJR-GARCH.</description>
  </item>
  <item rdf:about="http://hdl.handle.net/10419/30184">
    <title>Pricing an European gas storage facility using a continuous-time spot price model with GARCH diffusion</title>
    <link>http://hdl.handle.net/10419/30184</link>
    <description>Titel: Pricing an European gas storage facility using a continuous-time spot price model with GARCH diffusion
&lt;br/&gt;
&lt;br/&gt;Autoren: Schlüter, Stephan; Davison, Matt
&lt;br/&gt;
&lt;br/&gt;Zusammenfassung: In this article we present both a theoretical framework and a solved example for pricing an European gas storage facility and computing the optimal strategy for its operation. As a representative price index we choose the Dutch TTF day-ahead gas price. We present statistical evidence that the volatility of this index is time-varying, so we introduce a new continuous-time model by incorporating GARCH diffusion into an Ornstein-Uhlenbeck process. Based on this price process we use dynamic programming methods to derive partial differential equations for pricing a storage facility. As an example we apply our methodology to a storage site located in Epe at the German-Dutch border. In this context we investigate the effects of multiple contract types, and perform a sensitivity analysis for all model parameters. We obtain a value surface displaying the properties of a financial straddle. Both volatility and mean reversion influence the facility value - but only around the long-run mean of the gas price. The terminal condition, which includes information about the contract provisions, is of importance if it contains e.g. penalty terms for low inventory levels. Otherwise its influence is diminishing for increasing lease periods.</description>
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