Socioeconomic Networks with Long-Range Interactions

We study a modified version of a model previously proposed by Jackson and Wolinsky to account for communicating information and allocating goods in socioeconomic networks. In the model, the utility function of each node is given by a weighted sum of contributions from all accessible nodes. The weights, parameterized by the variable $\delta$, decrease with distance. We introduce a growth mechanism where new nodes attach to the existing network preferentially by utility. By increasing $\delta$, the network structure evolves from a power-law to an exponential degree distribution, passing through a regime characterised by shorter average path length, lower degree assortativity and higher central point dominance. In the second part of the paper we compare different network structures in terms of the average utility received by each node. We show that power-law networks provide higher average utility than Poisson random networks. This provides a possible justification for the ubiquitousness of scale-free networks in the real world.Comment: 11 pages, 8 figures, minor correction

Information theoretic description of the e-Mid interbank market: implications for systemic risk

We present an empirical analysis of the European electronic interbank market of overnight lending (e-MID) during the years 1999–2009. The main goal of the paper is to explain the observed changes of the cross-sectional dispersion of lending/borrowing conditions before, during and after the 2007–2008 subprime crisis. Unlike previous contributions, that focused on banks’ dependent and macro information as explanatory variables, we address the role of banks’ behaviour and market microstructure as determinants of the credit spreads

Herding effects in order driven markets: The rise and fall of gurus

We introduce an order driver market model with heterogeneous traders that imitate each other on a dynamic network structure. The communication structure evolves endogenously via a fitness mechanism based on agents performance. We assess under which assumptions imitation, among otherway noise traders, can give rise to the emergence of gurus and their rise and fall in popularity over time. We study the wealth distribution of gurus, followers and non followers and show that traders have an incentive to imitate and to be imitated since herding turns out to be profitable

An Analysis of Settlement Risk Contagion in Alternative Securities Settlement Architecture

This paper compares the so-called gross and net architectures for securities settlement. It studies the settlement risk arising from exogenous operational delays and compares the importance of settlement failures under the two architectures, as a function of the length of the settlement cycle and of different market conditions. Under both architectures, settlement failures are non-monotonically related to the length of settlement cycle. There is no evidence that continuous time settlement provides always higher stability. Gross systems appear to be more stable than net systems

Optimal Trading Strategies in a Limit Order Market with Imperfect Liquidity

We study the optimal execution strategy of selling a security. In a continuous time diffusion framework, a risk-averse trader faces the choice of selling the security promptly or placing a limit order and hence delaying the transaction in order to sell at a more favorable price. We introduce a random delay parameter, which defers limit order execution and characterizes market liquidity. The distribution of expected time-to-fill of limit orders conforms to the empirically observed exponential distribution of trading times, and its variance decreases with liquidity. We obtain a closed-form solution and demonstrate that the presence of the lag factor linearizes the impact of other market parameters on the optimal limit price. Finally, two more stylized facts are rationalized in our model: the equilibrium bid-ask spread decreases with liquidity, but increases with agents risk aversion

Weighted network analysis of high frequency cross-correlation measures

In this paper we implement a Fourier method to estimate high-frequency correlation matrices from small data sets. The Fourier estimates are shown to be considerably less noisy than the standard Pearson correlation measures and thus capable of detecting subtle changes in correlation matrices with just a month of data. The evolution of correlation at different time scales is analyzed from the full correlation matrix and its minimum spanning tree representation. The analysis is performed by implementing measures from the theory of random weighted networks. © 2007 The American Physical Society