Estimating the Dynamics and Persistence of Financial Networks, with an Application to the Sterling Money Market
金融网络动态与持久性的估计:英镑货币市场应用AI 解读 ↓
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金融网络动态与持久性的估计:英镑货币市场应用AI 解读 ↓
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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.
分组时变网络向量自回归模型的估计
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分组时变网络向量自回归模型的估计
This paper introduces a flexible time-varying network vector autoregressive model framework for large-scale time series. A latent group structure is imposed on the heterogeneous and node-specific time-varying momentum and network spillover effects so that the number of unknown time-varying coefficients to be estimated can be reduced considerably. A classic agglomerative clustering algorithm with nonparametrically estimated distance matrix is combined with a ratio criterion…
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网络时间序列中的欧氏镜像与动态演化
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