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Working Paper
Spatial Dependence and Data-Driven Networks of International Banks
This paper computes data-driven correlation networks based on the stock returns of international banks and conducts a comprehensive analysis of their topological properties. We first apply spatial-dependence methods to filter the effects of strong common factors and a thresholding procedure to select the significant bilateral correlations. The analysis of topological characteristics of the resulting correlation networks shows many common features that have been documented in the recent literature but were obtained with private information on banks? exposures. Our analysis validates these ...
Report
Prestigious stock exchanges: a network analysis of international financial centers
In this paper, we use methods from social network analysis to assess the relative importance of financial centers around the world. Using data from virtually the entire universe of global equity activity, we present two sets of complete rankings for up to forty-five separate locations for the 1990-2006 period. The first phase of our analysis evaluates international stock exchanges based on their ability to attract global initial public offerings (IPOs). The second phase compares the capacity of these exchanges to provide an efficient trading platform for cross-listed companies. U.S. exchanges ...
Working Paper
Dealer Networks
Dealers in over-the-counter securities form networks to mitigate search frictions. The audit trail for municipal bonds shows the dealer network has a core-periphery structure. Central dealers are more efficient at matching buyers and sellers than peripheral dealers, which shortens intermediation chains and speeds up trading. Investors face a tradeoff between execution speed and cost. Central dealers provide immediacy by pre-arranging fewer trades and holding larger inventory. However, trading costs increase strongly with dealer centrality. Investors with strong liquidity need trade with ...
Working Paper
The dynamic factor network model with an application to global credit risk
We introduce a dynamic network model with probabilistic link functions that depend on stochastically time-varying parameters. We adopt the widely used blockmodel framework and allow the high-dimensional vector of link probabilities to be a function of a low-dimensional set of dynamic factors. The resulting dynamic factor network model is straightforward and transparent by nature. However, parameter estimation, signal extraction of the dynamic factors, and the econometric analysis generally are intricate tasks for which simulation-based methods are needed. We provide feasible and practical ...