Estimating the Dynamics and Persistence of Financial Networks, with an Application to the Sterling Money Market

Abstract

Summary We propose a novel methodology for dynamic econometric modelling of large financial networks subject to persistence, structural changes and sparsity. We estimate bivariate dynamic Tobit‐type models for each pair of banks, allowing for deterministic or stochastic time‐varying parameters, and then aggregate across all bank pairs. To tackle the high dimensionality of the model, we construct a few lagged variables that efficiently summarize the position of a bank pair in the network. We propose a simple and computationally easy kernel‐based local maximum likelihood estimator of the time‐varying parameters of the model and establish its asymptotic properties. We then apply the model to the time series of daily overnight money market network in the UK 2003–2012. The results show that our model can successfully accommodate the numerous structural breaks arising from changes to the monetary framework and captures well the dynamics of the interbank lending relationships in this period. Copyright © 2015 John Wiley & Sons, Ltd.

Surfing through the GFC: Systemic Risk in Australia

“…This paper examines systemic risk in the Australian financial sector through the interconnectedness of the financial as well as nonfinancial sector firms. Measuring systemic risk through the networks of financial institutions is a growing international empirical literature: for example, Billio et al (2012) and Anufriev and Panchenko (2015) compare snapshots of noncrisis and crisis period networks, while Giraitis et al (2016) and Yilmaz (2014, 2016), among others, estimate time-varying systemic risk via connectivity.…”

Quasi‐Bayesian Estimation of Time‐Varying Volatility in DSGE Models

“…First, at each point t , a Wishart prior distribution for the precision matrix is specified of the form where α 0 t is a degrees of freedom prior parameter, and is a k × k diagonal scale matrix. The kernel‐weighted likelihood function of Giraitis et al (), given the distributional assumption in , takes the following form: where w t j are weights computed using a kernel function and normalized as: …”

“…It is the Bayesian treatment of this paper that facilitates the construction of such an algorithm and, to our best knowledge, this is the first procedure that can accommodate mixtures of time-varying volatilities and time-invariant parameters in a DSGE framework without imposing parametric assumptions on the volatility processes. The novelty is the facilitation of such mixtures: the frequentist method of Giraitis et al (2014Giraitis et al ( , 2016 cannot handle mixtures, while Petrova (2017) only deals with conjugate posterior mixtures where Metropolis steps are not required.…”

A time varying DSGE model with financial frictions

“…In recent work, Galvão, Giraitis, Kapetanios and Petrova (2015a) have provided a new approach that allows time varying estimation of Bayesian models, used for the time varying estimation of the Smets and Wouters (2007) DSGE model in Galvão, Giraitis, Kapetanios and Petrova (2015b). Their approach is an extension and formalisation of rolling window estimation, generalised by combining kernel-generated local likelihoods with appropriately chosen priors to generate a sequence of posterior distributions for the objects of interest over time, following the methodology developed in Giraitis, Kapetanios and Yates (2014) and Giraitis, Kapetanios, Wetherilt and Zikes (2016). Both the kernel and the rolling window approaches, when applied to structural models, assume that, instead of being endowed with perfect knowledge about the economy's data generating process, agents take parameter variation as exogenous when forming their expectations about the future.…”