The British tripartite financial supervision system in the face of the Northern Rock run

The Northern Rock debacle - Britain's first bank run in 141 years - was the Tripartite regulatory system's first live ammunition test since its establishment in 1997. The aftermath of the crisis lists the destruction of Britain's fifth largest mortgage lender, the tarnishing of the Bank of England's well-established reputation, and the loss of confidence in the reformed regulatory system - a system that had been considered a paragon by policymakers and reformers around the world. As market observers, politicians, investors and bankers criticize not only the mortgage lender for its extreme business model - but also the Tripartite regulatory system for mishandling the crisis - it is important to piece the story together and draw lessons from it. This paper examines the Tripartite's management of the crisis and concludes that the separation between the roles of banking supervision and Lender of Last Resort, coupled by Britain's flawed deposit insurance scheme, account for the British regulatory system's mishandling of the funding shortage that escalated into a bank run.Banks and banking - Great Britain ; Great Britain ; Bank supervision

Smoothed Gradients for Stochastic Variational Inference

Stochastic variational inference (SVI) lets us scale up Bayesian computation to massive data. It uses stochastic optimization to fit a variational distribution, following easy-to-compute noisy natural gradients. As with most traditional stochastic optimization methods, SVI takes precautions to use unbiased stochastic gradients whose expectations are equal to the true gradients. In this paper, we explore the idea of following biased stochastic gradients in SVI. Our method replaces the natural gradient with a similarly constructed vector that uses a fixed-window moving average of some of its previous terms. We will demonstrate the many advantages of this technique. First, its computational cost is the same as for SVI and storage requirements only multiply by a constant factor. Second, it enjoys significant variance reduction over the unbiased estimates, smaller bias than averaged gradients, and leads to smaller mean-squared error against the full gradient. We test our method on latent Dirichlet allocation with three large corpora.Comment: Appears in Neural Information Processing Systems, 201

Hierarchical relational models for document networks

We develop the relational topic model (RTM), a hierarchical model of both network structure and node attributes. We focus on document networks, where the attributes of each document are its words, that is, discrete observations taken from a fixed vocabulary. For each pair of documents, the RTM models their link as a binary random variable that is conditioned on their contents. The model can be used to summarize a network of documents, predict links between them, and predict words within them. We derive efficient inference and estimation algorithms based on variational methods that take advantage of sparsity and scale with the number of links. We evaluate the predictive performance of the RTM for large networks of scientific abstracts, web documents, and geographically tagged news.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS309 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org