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    <title>EconStor Collection: CFS Working Paper Series, Universität Frankfurt a. M.</title>
    <link>http://hdl.handle.net/10419/122</link>
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      <title>Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility</title>
      <link>http://hdl.handle.net/10419/72654</link>
      <description>Title: Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility
&lt;br/&gt;
&lt;br/&gt;Authors: Andersen, Torben G.; Bollerslev, Tim; Francis X. Diebold,
&lt;br/&gt;
&lt;br/&gt;Abstract: A rapidly growing literature has documented important improvements in volatility measurement and forecasting performance through the use of realized volatilities constructed from high-frequency returns coupled with relatively simple reduced-form time series modeling procedures. Building on recent theoretical results from Barndorff-Nielsen and Shephard (2003c,d) for related bi-power variation measures involving the sum of high-frequency absolute returns, the present paper provides a practical framework for non-parametrically measuring the jump component in realized volatility measurements. Exploiting these ideas for a decade of high-frequency five-minute returns for the DM/$ exchange rate, the S&amp;P500 market index, and the 30-year U.S. Treasury bond yield, we find the jump component of the price process to be distinctly less persistent than the continuous sample path component. Explicitly including the jump measure as an additional explanatory variable in an easy-to-implement reduced form model for realized volatility results in highly significant jump coefficient estimates at the daily, weekly and quarterly forecast horizons. As such, our results hold promise for improved financial asset allocation, risk management, and derivatives pricing, by separate modeling, forecasting and pricing of the continuous and jump components of total return variability.</description>
      <pubDate>Tue, 29 Oct 2002 22:58:59 GMT</pubDate>
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      <title>Do Changes in Sovereign Credit Ratings Contribute to Financial Contagion in Emerging Market Crises?</title>
      <link>http://hdl.handle.net/10419/72653</link>
      <description>Title: Do Changes in Sovereign Credit Ratings Contribute to Financial Contagion in Emerging Market Crises?
&lt;br/&gt;
&lt;br/&gt;Authors: Kraeussl, Roman
&lt;br/&gt;
&lt;br/&gt;Abstract: Credit rating changes for long-term foreign currency debt may act as a wake-up call with up-grades and downgrades in one country affecting other financial markets within and across national borders. Such a potential (contagious) rating effect is likely to be stronger in emerg-ing market economies, where institutional investors problems of asymmetric information are more present. This empirical study complements earlier research by explicitly examining cross-security and cross-country contagious rating effects of credit rating agencies sovereign risk assessments. In particular, the specific impact of sovereign rating changes during the fi-nancial turmoil in emerging markets in the latter half of the 1990s has been examined. The results indicate that sovereign rating changes in a ground-zero country have a (statistically) significant impact on the financial markets of other emerging market economies although the spillover effects tend to be regional.</description>
      <pubDate>Tue, 29 Oct 2002 22:58:59 GMT</pubDate>
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      <title>Learning to Forecast and Cyclical Behavior of Output and Inflation</title>
      <link>http://hdl.handle.net/10419/72652</link>
      <description>Title: Learning to Forecast and Cyclical Behavior of Output and Inflation
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&lt;br/&gt;Authors: Adam, Klaus
&lt;br/&gt;
&lt;br/&gt;Abstract: This paper considers a sticky price model with a cash-in-advance constraint where agents forecast inflation rates with the help of econometric models. Agents use least squares learning to estimate two competing models of which one is consistent with rational expectations once learning is complete. When past performance governs the choice of forecast model, agents may prefer to use the inconsistent forecast model, which generates an equilibrium where forecasts are inefficient. While average output and inflation result the same as under rational expectations, higher moments differ substantially: output and inflation show persistence, inflation responds sluggishly to nominal disturbances, and the dynamic correlations of output and inflation match U.S. data surprisingly well.</description>
      <pubDate>Tue, 29 Oct 2002 22:58:59 GMT</pubDate>
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    <item>
      <title>On the Relation between Robust and Bayesian Decision Making</title>
      <link>http://hdl.handle.net/10419/72651</link>
      <description>Title: On the Relation between Robust and Bayesian Decision Making
&lt;br/&gt;
&lt;br/&gt;Authors: Adam, Klaus
&lt;br/&gt;
&lt;br/&gt;Abstract: This paper compares Bayesian decision theory with robust decision theory where the decision maker optimizes with respect to the worst state realization. For a class of robust decision problems there exists a sequence of Bayesian decision problems whose solution converges towards the robust solution. It is shown that the limiting Bayesian problem displays infinite risk aversion and that decisions are insensitive (robust) to the precise assignment of prior probabilities. This holds independent from whether the preference for robustness is global or restricted to local perturbations around some reference model.</description>
      <pubDate>Tue, 29 Oct 2002 22:58:59 GMT</pubDate>
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