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Story - acrobat - 10/10/2008 - 12:54 - 0 comments - 0 attachments
This research deals with some statistical modeling problems that are motivated by credit risk analysis. Credit risk modeling has been the subject of considerable research interest in finance and has recently drawn the attention of statistical researchers. In the first chapter, we provide an up-to-date review of credit risk models and demonstrate their close connection to survival analysis.
The first statistical problem considered is the development of adaptive smoothing spline (AdaSS) for heterogeneously smooth function estimation. Two challenging issues that arise in this context are evaluation of reproducing kernel and determination of local penalty, for which we derive an explicit solution based on piecewise type of local adaptation. Our nonparametric AdaSS technique is capable of fitting a diverse set of ‘smooth’ functions including possible jumps, and it plays a key role in subsequent work in the thesis.
The financial crises of the past decade, and the IMF’s initiative to build an early warning system against such crises, have stimulated a wave of research into the empirical determinants of banking crises; e.g. Bordo et al. (2001), Borio and Lowe (2004), Caprio and Klingebiel (1996ab, 2002, 2003), Demirgüc-Kunt and Detragiache (1998, 2002), Demirgüc Kunt et al (2000), Drees and Pazarbasioglu (1995), Flannery (1996), Gavin and Hausman (1996), Glick and Hutchison (2001), Goldstein and Turner (1996), Goldstein et al. (2000), Kaminsky and Reinhardt (1996, 1999), Lindgren et al. (1996, 1999). A common methodological challenge facing empirical research in this area is the identification of crises events. Existing studies rely on the observation of exceptional events or very visible policy interventions, such as forced mergers, bank closures, or bailouts by the government. This can be misleading for a number of reasons.
First, such interventions may occur even in the absence of an acute crisis in the banking sector, e.g., when unresolved structural problems in the banking sector have been lingering for some time. Second, deciding whether a particular intervention is large enough to be called a crisis of the banking system and not just an individual institution involves a subjective judgment. Third, policy interventions typically occur when the crisis has already fully developed. Finally, recent literature on currency crises (Eichengreen, Rose, and Wyplosz, 1995, 1996ab) argues that not every crisis leads to a visible policy intervention of this kind, as central banks and regulators may be able to fend off the crisis successfully with less spectacular means. Focusing on crises that trigger policy interventions thus creates a selection bias in the empirical work.
Financial economists generally agree that in efficient markets, stock prices change to reflect available information - either firm-specific or market-wide. Recent literature has addressed the question of how a firm’s information environment (disclosure policy, analyst following) or its institutional environment (property rights protection, quality of government, legal origin) can affect the relative importance of firm-specific as opposed to market wide factors (Jin and Myers (2006), Piotroski and Roulstone (2003), Chan and Hameed (2006), Morck, Yeung and Yu (2000)). This literature has taken the perspective that if the firm’s environment causes stock prices to aggregate more firm-specific information, market factors should explain a smaller proportion of the variation in stock returns. In other words, the stock return synchronicity or R2 from a standard market model regression should be lower.
This perspective, while intuitive, is at odds with another equally intuitive implication of market efficiency. In efficient markets, stock prices respond only to announcements that are not already anticipated by the market. When the information environment surrounding a firm improves and more firm-specific information is available, market participants are also able to improve their predictions about the occurrence of future firm-specific events. As a result, prevailing stock prices are likely to already “factor in” the likelihood of occurrence of these events. When the events actually happen in the future, the market will not react to such news, since there is little “surprise.” In other words, more informative stock prices today should be associated with less firm-specific variation in stock prices in the future. Therefore, the return synchronicity should be higher.
