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    <title>EconStor Community: Department of Economics, Rutgers University</title>
    <link>http://hdl.handle.net/10419/281</link>
    <description>Department of Economics, Rutgers University</description>
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      <title>The elusive scale economies of the largest banks and their implications for global competitiveness</title>
      <link>http://hdl.handle.net/10419/59504</link>
      <description>Title: The elusive scale economies of the largest banks and their implications for global competitiveness
&lt;br/&gt;
&lt;br/&gt;Authors: Hughes, Joseph P.
&lt;br/&gt;
&lt;br/&gt;Abstract: In the wake of the financial crisis that began in 2007, policy makers have focused again on the largest financial firms to consider the association of their size with systemic risk. An equally important question examines whether their size benefits the economy. In particular, is the size of our largest financial institutions the result of technological cost advantages that improve the efficiency of their capital allocation and liquidity and enhance their international competitiveness? Or is it the result, not of technological cost advantages, but of safety-net subsidies that confer too-big-to-fail cost advantages and foster moral hazard in investment decisions. This paper reviews the evidence of large scale economies that increase with size and considers the credibility of this evidence by examining details of how scale economies are measured and why evidence of scale economies eludes many investigations. A method of estimating scale economies developed by Hughes, Lang, Mester, and Moon (1996) distinguishes the underlying scale effects on cost from the effects on costs of size-related changes in risk-taking, which can obscure technological cost advantages, such as those due to better diversification. It reviews evidence that technology, not too-big-to-fail subsidies, accounts for the cost advantage of the largest financial institutions. Finally, it considers the implications of scale economies for scaling back the operations of the largest financial institutions and for the global competitiveness of smaller institutions.</description>
      <pubDate>Fri, 29 Oct 2010 22:58:59 GMT</pubDate>
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    <item>
      <title>Comparison of Bayesian model selection criteria and conditional Kolmogorov test as applied to spot asset pricing models</title>
      <link>http://hdl.handle.net/10419/59503</link>
      <description>Title: Comparison of Bayesian model selection criteria and conditional Kolmogorov test as applied to spot asset pricing models
&lt;br/&gt;
&lt;br/&gt;Authors: Shen, Xiangjin; Tsurumi, Hiroki
&lt;br/&gt;
&lt;br/&gt;Abstract: We compare Bayesian and sample theory model specification criteria. For the Bayesian criteria we use the deviance information criterion and the cumulative density of the mean squared errors of forecast. For the sample theory criterion we use the conditional Kolmogorov test. We use Markov chain Monte Carlo methods to obtain the Bayesian criteria and bootstrap sampling to obtain the conditional Kolmogorov test. Two non-nested models we consider are the CIR and Vasicek models for spot asset prices. Monte Carlo experiments show that the DIC performs better than the cumulative density of the mean squared errors of forecast and the CKT. According to the DIC and the mean squared errors of forecast, the CIR model explains the daily data on uncollateralized Japanese call rate from January 1 1990 to April 18 1996; but according to the CKT, neither the CIR nor Vasicek models explains the daily data.</description>
      <pubDate>Fri, 29 Oct 2010 22:58:59 GMT</pubDate>
    </item>
    <item>
      <title>Real-time datasets really do make a difference: Definitional change, data release, and forecasting</title>
      <link>http://hdl.handle.net/10419/59502</link>
      <description>Title: Real-time datasets really do make a difference: Definitional change, data release, and forecasting
&lt;br/&gt;
&lt;br/&gt;Authors: Fernandez, Andres; Swanson, Norman
&lt;br/&gt;
&lt;br/&gt;Abstract: In this paper, we empirically assess the extent to which early release inefficiency and definitional change affect prediction precision. In particular, we carry out a series of ex-ante prediction experiments in order to examine: the marginal predictive content of the revision process, the trade-offs associated with predicting different releases of a variable, the importance of particular forms of definitional change which we call 'definitional breaks', and the rationality of early releases of economic variables. An important feature of our rationality tests is that they are based solely on the examination of ex-ante predictions, rather than being based on in-sample regression analysis, as are many tests in the extant literature. Our findings point to the importance of making real-time datasets available to forecasters, as the revision process has marginal predictive content, and because predictive accuracy increases when multiple releases of data are used when specifying and estimating prediction models. We also present new evidence that early releases of money are rational, whereas prices and output are irrational. Moreover, we find that regardless of which release of our price variable one specifies as the 'target' variable to be predicted, using only 'first release' data in model estimation and prediction construction yields mean square forecast error (MSFE) 'best' predictions. On the other hand, models estimated and implemented using 'latest available release' data are MSFE-best for predicting all releases of money. We argue that these contradictory finding are due to the relevance of definitional breaks in the data generating processes of the variables that we examine. In an empirical analysis, we examine the real-time predictive content of money for income, and we find that vector autoregressions with money do not perform significantly worse than autoregressions, when predicting output during the last 20 years.</description>
      <pubDate>Fri, 29 Oct 2010 22:58:59 GMT</pubDate>
    </item>
    <item>
      <title>Predictive inference for integrated volatility</title>
      <link>http://hdl.handle.net/10419/59500</link>
      <description>Title: Predictive inference for integrated volatility
&lt;br/&gt;
&lt;br/&gt;Authors: Corradi, Valentina; Distaso, Walter; Swanson, Norman R.
&lt;br/&gt;
&lt;br/&gt;Abstract: In recent years, numerous volatility-based derivative products have been engineered. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this paper, we propose nonparametric kernel estimators of the aforementioned quantities. The kernel functions used in our analysis are based on different realized volatility measures, which are constructed using the ex post variation of asset prices. A set of sufficient conditions under which the estimators are asymptotically equivalent to their unfeasible counterparts, based on the unobservable volatility process, is provided. Asymptotic normality is also established. The efficacy of the estimators is examined via Monte Carlo experimentation, and an empirical illustration based upon data from the New York Stock Exchange is provided.</description>
      <pubDate>Fri, 29 Oct 2010 22:58:59 GMT</pubDate>
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