Vertical integration may arise as a response to problems caused by contractual incompleteness (Grossman and Hart (1986), Bolton and Whinston (1993) and Rey and Tirole (2004). and as ways to acquire valuable private information about the production process (Arrow (1975)), to avoid rationing (Green (1986), Bolton and Whinston (1993)) and to weaken rivals (Bolton and Whinston (1993), Rey and Tirole (2004), Chemla (2003)). Uncertainty in demand, lack of market flexibility and (implied) risk aversion may also entail vertical integration (Hendrikse and Peters (1989), Carlton (1979), Perry (1989), Emons (1996)).
With the increasing availability of high frequency financial data, the development of realized variance (RV) has made tremendous progress both on the theoretical front and in empirical applications, (see for instance Andersen and Bollerslev, 1998; Andersen, Bollerslev, Diebold, and Labys, 2001, 2003; Barndorff Nielsen and Shephard, 2004a). The focus of this literature is the construction of a flexible model-free measure of integrated variance for asset returns.
Bankruptcy prediction has been routinely studied by academics, practitioners, and regulators. The well-known prediction models include discriminant analysis model (Altman, 1968), Merton model (Merton, 1974; Vassalou and Xing, 2004), logit model (Ohlson, 1980), and probit model (Zmijewski, 1984), to name only a few. The common principal of these approaches is that the models are developed using single-period data of firms. Shumway (2001) pointed out that such prediction processes are static in nature, since they ignore the changing characteristics of firms through time. In order to avoid possible loss of prediction power due to using static models, Shumway (2001) and Chava and Jarrow (2004) proposed a discrete-time hazard model (DHM) using multiple-period data for bankruptcy prediction. Their novel model applies the idea of survival analysis (Cox and Oakes, 1984), and has the advantage of using all available information of firms to build up a prediction system so that each firm’s bankruptcy risk at each time point can be determined. Thus DHM is a dynamic forecasting model.
The performance of bankruptcy prediction models was mainly assessed in the literature by performing prediction-oriented tests; see for example the above references. Recently, Hillegeist et al. (2004) proposed a different approach for doing it. They compared the information content about credit risk provided by out-of-sample values of probability of bankruptcy (PB) based on Merton model, Z-Score based on discriminant analysis model, and O-Score based on logit model. Their results show that PB based on Merton model provides significantly more information than Z-Score and O-Score. In contrast, Agarwal and Taffler (2008) pointed out that there is little difference between PB based on Merton model and Z-Score, in terms of predictive ability and information content. The results in Hillegeist et al. (2004) and Agarwal and Taffler (2008) were developed by comparing “static” bankruptcy prediction models.
