سازمان صنعتی تصفیه و استقرار پس از معامله

The introduction to this special issue reviews the literature on the industrial organization of securities market clearing and settlement, covering institutional, theoretical, and empirical contributions, including both papers in this special issue and previous studies. Clearing and settlement is an important but under-researched network industry. Recent theoretical research has characterized the network externalities in clearing and settlement and explored the economic efficiency of various alternative industrial structures. Initial empirical research has identified substantial economies of both scale and scope and important interactions with trading platforms. More research is needed to elaborate these theoretical insights and improve our understanding of the economics of this major industry.

A surprisingly large amount of economic resources are absorbed by costs of trading and settling on securities markets. The accounts of the major investment banks and asset managers and of the various providers of clearing and settlement services suggest that the industry spends more than €10bn dollars per year on processing financial trades and ensuring that the resulting obligations are carried out as agreed.1 Brokerage margins and illiquidity in fragmented markets add a great deal more to the total costs of conducting securities trades. Despite the economic importance of this industry it remains under-researched. A number of studies have in recent years addressed risk management and systemic risks of post-trade processing and the associated large-value payment systems.2 There is also of course the large body of work on market micro-structure, examining the relationship between trading arrangements, information revelation, and the determination of prices and trading volumes in financial markets. But there has until very recently been almost no academic literature on the industrial organization of trading and post-trade activities. The growing interest in the industrial organization of trading and post-trade activities has been driven in large part by the rapid rise of these activities in the agenda for both policy makers and industry leaders. Financial authorities are trying to promote the internationalization of financial markets. This has become a central issue in the European Union. The creation of a single pan-European financial market was one of the major economic goals of the creation of the single European currency, but to date this objective is frustrated, amongst other reasons, by the absence of any pan-European infrastructures for trading and post-trade processing.

We develop a model of the change in industry concentration that integrates the elements of market mechanism and chance. The model is stochastically simulated and connected to real world data by searching the parameterization that minimizes deviations from simulated paths and historical industry data. Thus, we overcome the problem of loss of generality in simulations andturn evolutionary modeling into a tool for the empirical analysis of the industrial concentration process. This offers a new methodological alternative to the list of research methods for the study of market evolution described by Geroski and Mata (2001). For the particular industry studied here (the U.S. household laundry equipment industry) the estimates of the structural evolutionary model fit the empirical concentration data better than an estimated version of Gibrat’s model. Both the structural evolutionary model and the Gibrat model build on elements of chance as determinants of industry development. Hence, when it comes to assessing the likely future course of an industry the range of possible trajectories of concentration implied by a model (and its estimated parameters) becomes particularly important. The analysis presented here shows that the structural evolutionary model leads to a narrower range of possible trajectories of concentration measures than is the case with the parameterized version of Gibrat’s model. Hence, both industry analysts wishing to assess future profits (possibly rising with concentration) and competition regulators wishing to prevent developments disadvantageous to consumers may gain from such a structural evolutionary analysis. It should be noted that specific applications of the described approach would not be limited to industries with the kind of historical development (a clear tendency toward higher concentration) shown by the household laundry equipment industry. In fact, industries that are at present less concentrated are particularly interesting candidates for this type of forward looking investigation. However, the analysis of such industries is likely to require a modeling of the processes of innovation and market entry. Our exemplary analysis does not include such complications.