A dynamic network model of the unsecured interbank lending market

We introduce a dynamic network model of interbank lending and estimate the parameters by indirect inference using network statistics of the Dutch interbank market from February 2008 to April 2011. We find that credit-risk uncertainty and peer monitoring are significant factors in explaining the sparse core-periphery structure of the market and the presence of relationship lending. Shocks to credit-risk uncertainty lead to extended periods of low market activity, intensified by reduced peer monitoring. Moreover, changes in the central bank's interest rate corridor have both a direct effect on the market as well as an indirect effect by changing banks’ monitoring efforts

Dynamic Spatial Autoregressive Models with Time-varying Spatial Weighting Matrices

We propose a new spatio-temporal model with time-varying spatial weighting matrices, by allowing for a general parameterization of the spatial matrix. The filtering procedure of the time-varying unknown parameters is performed using the information contained in the score of the conditional distribution of the observables. We provide conditions for the stationarity and ergodicity of the filtered sequence of the spatial matrices as well as for the consistency and asymptotic normality of the maximum likelihood estimator (MLE). An extensive Monte Carlo simulation study to investigate the finite sample properties of the maximum likelihood estimator is also reported. We finally analyze the association between eight European countries' perceived risk, suggesting that the economically strong countries have their perceived risk increased due to their spatial connection with the economically weaker countries, and we investigate the evolution of the spatial connection between the house prices in different areas of the UK, identifying periods when the usually adopted sparse weighting matrix is not sufficient to describe the underlying spatial process

In-sample confidence bands and out-of-sample forecast bands for time-varying parameters in observation-driven models

We study the performances of alternative methods for calculating in-sample confidence and out-of-sample forecast bands for time-varying parameters. The in-sample bands reflect parameter uncertainty, while the out-of-sample bands reflect not only parameter uncertainty, but also innovation uncertainty. The bands are applicable to a wide range of estimation procedures and a large class of observation driven models with differentiable transition functions. A Monte Carlo study is conducted to investigate time-varying parameter models such as generalized autoregressive conditional heteroskedasticity and autoregressive conditional duration models. Our results show convincing differences between the actual coverages provided by the different methods. We illustrate our findings in a volatility analysis for monthly Standard & Poor's 500 index returns