Numerous dynamic stochastic general equilibrium (DSGE) macroeconomic models now allow for variation in the depreciation rate of capital. The most common approach treats the depreciation rate as an endogenous variable such that the choice to use capital intensively or to spend little on maintenance and repair results in high depreciation [Greenwood, Hercowitz and Huffman (1988); Burnside, Eichenbaum and Rebelo (1996); King and Rebelo (2000) for the former; McGrattan and Schmitz (1999), Collard and Kollintzas (2000) and Licandro and Puch (2000) for the latter].
Procyclical variation in capital utilization amplifies the effect of a technology shock on output. When the depreciation rate is a function of maintenance and repair, the assumption is that each unit of capital is matched with labor input that is geared toward either production or capital maintenance and repair.
In modern security markets information is sold and distributed to investors in a variety of ways. Brokerage (sell-side) analysts distribute reports and newsletters to a large number of clients, while buy-side employees and independent investment research firms offer investment advice to a small number of customers, often providing it only to the proprietary desk that commissions the research. Widely distributed investment advice seems to have little informational content, while the opposite is expected from more expensive personalized research.1 This heterogeneity raises a number of interesting questions: Why do such different allocations of information arise? What are the consequences for asset pricing properties such as informational efficiency and trading volume?
Since the fi rst quarter of 2007 the US equity markets have been hit with a sequence of shocks related to the sub-prime crisis. Despite the fact that prior to this crisis, developed and developing countries seemed to have decoupled from one another, meaning that correlations of output, consumption, inflation, interest rates, and stock market returns had dropped signi cantly from the previous decade, the events after the summer of 2007 made it very clear that systemic risk, in the form of international contagion, was back and with a vengeance.
Measuring the degree of systemic risk shares some of the same challenges the contagion literature has had. The main reason for this is that systemic risk and contagion are usually estimated using reduced-form representations, which complicate the interpretation of coefficients as well as the separation between different theories behind the propagation of shocks. For example, from the computation of simple correlations to linear models, from copulas to GARCH models, from principal components to probit regressions, all these methodologies are agnostic about the di fferent theories of behind the propagation mechanism. Furthermore, in most cases, diff erent theories of why shocks are transmitted internationally are tested in a regression based framework, by introducing interaction terms as some of regressor on the right hand side. Although this approach has taught us a great deal about the crises that prevailed during the 1990s, it has had its limitations with regards to determine the strength of international linkages which generate systemic consequences.
