A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market

We propose a dynamic network model where two mechanisms control the probability of a link between two nodes: (i) the existence or absence of this link in the past, and (ii) node-specific latent variables (dynamic fitnesses) describing the propensity of each node to create links. Assuming a Markov dynamics for both mechanisms, we propose an Expectation-Maximization algorithm for model estimation and inference of the latent variables. The estimated parameters and fitnesses can be used to forecast the presence of a link in the future. We apply our methodology to the e-MID interbank network for which the two linkage mechanisms are associated with two different trading behaviors in the process of network formation, namely preferential trading and trading driven by node-specific characteristics. The empirical results allow to recognise preferential lending in the interbank market and indicate how a method that does not account for time-varying network topologies tends to overestimate preferential linkage.Comment: 19 pages, 6 figure

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

"Fiscal Deficit, Capital Formation, and Crowding Out in India: Evidence from an Asymmetric VAR Model"

This paper analyzes the real (direct) and financial crowding out in India between 1970–71 and 2002–03. Using an asymmetric vector autoregressive (VAR) model, the paper finds no real crowding out between public and private investment; rather, complementarity is observed between the two. The dynamics of financial crowding out is captured through the dual transmission mechanism via the real rate of interest—that is, whether private capital formation is interest-rate sensitive and, in turn, whether the rise in the real rate of interest is induced by a fiscal deficit. The study found empirical evidence for the former but not the latter, supporting the conclusion that there is no financial crowding out in India. The differential impacts of public infrastructure and noninfrastruture innovations on the private corporate sector are carried out separately to analyze the nonhomogeneity aspects of public investment. The results of the Impulse Response Function reinforced that no other macrovariables, including cost and quantity of credit and the output gap, have been as significant as public investment—in particular, public infrastructure investment—in determining private corporate investment in the medium and long terms, which has crucial policy implications.