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A rigorous asymptotic analysis concerning the phenomenon of non-uniqueness of quasi-equilibrium turbulent boundary layers in the large Reynolds number limit has recently been carried out in [2]. The approach contains the classical asymptotic theory of wall-bounded turbulent shear flows, cf. [3], as a limiting case. Compared to the latter, the novel theory allows for a moderately large but still asymptotically small velocity defect with respect to the external inviscid flow. Therefore, it applies to attached flow only which, however, exhibits some properties known from separating turbulent boundary layers. Here a first comparison of the theoretical results with numerical and experimental data is presented. As a special aspect, the impact of the equilibrium conditions on the associated external potential flow field is elucidated.

This chapter studied stylized facts and the basic mechanisms of exchange-rate caused financial and real crises. As we have shown it is likely to be the connection of weak balance sheets (of households, firms, financial intermediaries, governments and countries) and large exchange rate shocks that lead to positive feedback mechanisms and thus to credit contraction, declining asset prices and economic activity, real crisis and large output loss. This in particular appears to be a basic mechanism if there exists in the country large debt denominated in foreign currency. Moreover, as we have shown, credit rationing and state dependent default premia may entail destabilizing mechanisms, possibly leading to low level equilibria. The insight of how financial and real risk can be enlarged by large currency shocks and to what extent an international portfolio might be able to hedge this risk is studied further in Chap. 13.

A rigorous asymptotic analysis concerning the phenomenon of non-uniqueness of quasi-equilibrium turbulent boundary layers in the large Reynolds number limit has recently been carried out in [2]. The approach contains the classical asymptotic theory of wall-bounded turbulent shear flows, cf. [3], as a limiting case. Compared to the latter, the novel theory allows for a moderately large but still asymptotically small velocity defect with respect to the external inviscid flow. Therefore, it applies to attached flow only which, however, exhibits some properties known from separating turbulent boundary layers. Here a first comparison of the theoretical results with numerical and experimental data is presented. As a special aspect, the impact of the equilibrium conditions on the associated external potential flow field is elucidated.

This chapter has employed perfect and imperfect capital market theory and discussed the relation of creditmarket borrowing, credit risk, asset prices and economic activity. We also have shown how in a simple model of the firm the micro-macro link may work. In the next chapter we want to pursue the question of how to empirically test for credit risk of economic agents and its impact on economic activity.

In studying the consumption based dynamic asset pricing theory we have first presumed that there is an exogenously given dividend stream which is equal to the consumption stream of the agent whose utility function could take on different forms. For the case of simple utility functions we have also derived the Euler equation as the essential equation to study dynamic asset pricing. Appendix 2 derives the Euler equation from dynamic programming. As we also have shown, using preferences such as log or power utility, the equity premium and the Sharpe ratio cannot match the equity premium and the Sharpe ratio of actual time series data. For those preferences a too high parameter of risk aversion and/or a strong covariance of consumption growth with asset returns are required which one does not find in the data. The question thus remains whether models that more explicitly take into account production activities or rely on other types of preferences may be able to provide a better match of theory and the data. Asset pricing for production economies is taken up next. Other preferences are considered in Chap. 15.

Our review of empirical approaches to study the interaction of stock prices and output -or in some cases stock prices, other financial variables and output—should be viewed as an introduction to modelling asset markets and economic activity. In Chap. 6 macro factors impacting stock prices are studied and a macro model that takes account of the interaction of macro variables and asset prices is introduced and empirical results reported. In Chap. 7 we explore the effects of new technology on asset prices and returns. Thereafter standard asset pricing models, in particular the capital asset pricing and intertemporal capital asset pricing models, are considered in detail and some estimation results are reported as well.

In studying the consumption based dynamic asset pricing theory we have first presumed that there is an exogenously given dividend stream which is equal to the consumption stream of the agent whose utility function could take on different forms. For the case of simple utility functions we have also derived the Euler equation as the essential equation to study dynamic asset pricing. Appendix 2 derives the Euler equation from dynamic programming. As we also have shown, using preferences such as log or power utility, the equity premium and the Sharpe ratio cannot match the equity premium and the Sharpe ratio of actual time series data. For those preferences a too high parameter of risk aversion and/or a strong covariance of consumption growth with asset returns are required which one does not find in the data. The question thus remains whether models that more explicitly take into account production activities or rely on other types of preferences may be able to provide a better match of theory and the data. Asset pricing for production economies is taken up next. Other preferences are considered in Chap. 15.

Our review of empirical approaches to study the interaction of stock prices and output -or in some cases stock prices, other financial variables and output—should be viewed as an introduction to modelling asset markets and economic activity. In Chap. 6 macro factors impacting stock prices are studied and a macro model that takes account of the interaction of macro variables and asset prices is introduced and empirical results reported. In Chap. 7 we explore the effects of new technology on asset prices and returns. Thereafter standard asset pricing models, in particular the capital asset pricing and intertemporal capital asset pricing models, are considered in detail and some estimation results are reported as well.

In studying the consumption based dynamic asset pricing theory we have first presumed that there is an exogenously given dividend stream which is equal to the consumption stream of the agent whose utility function could take on different forms. For the case of simple utility functions we have also derived the Euler equation as the essential equation to study dynamic asset pricing. Appendix 2 derives the Euler equation from dynamic programming. As we also have shown, using preferences such as log or power utility, the equity premium and the Sharpe ratio cannot match the equity premium and the Sharpe ratio of actual time series data. For those preferences a too high parameter of risk aversion and/or a strong covariance of consumption growth with asset returns are required which one does not find in the data. The question thus remains whether models that more explicitly take into account production activities or rely on other types of preferences may be able to provide a better match of theory and the data. Asset pricing for production economies is taken up next. Other preferences are considered in Chap. 15.

In studying the consumption based dynamic asset pricing theory we have first presumed that there is an exogenously given dividend stream which is equal to the consumption stream of the agent whose utility function could take on different forms. For the case of simple utility functions we have also derived the Euler equation as the essential equation to study dynamic asset pricing. Appendix 2 derives the Euler equation from dynamic programming. As we also have shown, using preferences such as log or power utility, the equity premium and the Sharpe ratio cannot match the equity premium and the Sharpe ratio of actual time series data. For those preferences a too high parameter of risk aversion and/or a strong covariance of consumption growth with asset returns are required which one does not find in the data. The question thus remains whether models that more explicitly take into account production activities or rely on other types of preferences may be able to provide a better match of theory and the data. Asset pricing for production economies is taken up next. Other preferences are considered in Chap. 15.