Credit rating dynamics and Markov mixture models

Despite mounting evidence to the contrary, credit migration matrices, used in many credit risk and pricing applications, are typically assumed to be generated by a simple Markov process. Based on empirical evidence, we propose a parsimonious model that is a mixture of (two) Markov chains, where the mixing is on the speed of movement among credit ratings. We estimate this model using credit rating histories and show that the mixture model statistically dominates the simple Markov model and that the differences between two models can be economically meaningful. The non-Markov property of our model implies that the future distribution of a firm’s ratings depends not only on its current rating but also on its past rating history. Indeed we find that two firms with identical current credit ratings can have substantially different transition probability vectors. We also find that conditioning on the state of the business cycle or industry group does not remove the heterogeneity with respect to the rate of movement. We go on to compare the performance of mixture and Markov chain using out-of-sample predictions.     

Arrow's impossibility theorem

Summary It is the purpose of this article to give an exact formulation of Arrow's famous impossibility theorem and its proof. Although the results presented here are not new in the sense that the theorem is refuted or questionned, it may be of some interest especially for the reader who wants to get a more or less complete view of the argumentation leading up to the theorem. Special attention is paid to the logical structure of the argument in the conviction that a lot of misunderstanding exists about what Arrow and subsequent writers tried to say. By way of introduction to the central theme some remarks are made concerning the relation of the Arrow problem to welfare economics.The author would like to thank Mr. W. R. de Jong of the Department of Philosophy of the Free University for his valuable advice in the field of logic and in particular Keimpe Reitsma, student of economics at the Free University, for the extensive discussions which formed an indispensable element in realizing this article. Thanks are due also to two referees for their useful suggestions.During the preparation of this article I was Assistant Professor at the Faculty of Economics, Free University, Amsterdam, The Netherlands. At present I am with the Ministry for Social Affairs, Directorate for General Economic Affairs.     

Deposit Insurance and Risk Management of the U.S. Banking System: What is the Loss Distribution Faced by the FDIC?

We examine the question of deposit insurance through the lens of risk management by constructing the loss distribution faced by the Federal Deposit Insurance Corporation (FDIC). We take a novel approach by arguing that the risk management problem faced by the FDIC is similar to that of a bank managing a loan portfolio, only in the FDIC’s case the risk arises from the potential for loss of the individual banks in its portfolio. We explicitly estimate the cumulative loss distribution of FDIC insured banks using two variations of the Merton model and find that reserves are sufficient to cover roughly 99.85% of the loss distribution, corresponding to about a BBB+ rating. However, under different stress scenarios (higher correlations, fat-tailed bank returns, increased loss severity) that level can be much lower: approximately 96% corresponding to about a B+ rating.JEL classification: G210, G280.Any views expressed represent those of the author only and not necessarily those of the Federal Reserve Bank of New York or the Federal Reserve System.     

Impact of farm management diversity on the environmental and economic performance of the wheat–maize cropping system in the North China Plain

To assess the attainable greenhouse gas (GHG) mitigation potential from crop production in China's most important grain production region, the North China Plain (NCP), single farm environmental and economic performances of 65 winter wheat–summer maize (WW–SM) producing households were determined. The results revealed a huge heterogeneity among farms, with up to five times higher environmental impact of worst compared to best performing farms. Astonishingly no trade-off between productivity and sustainability could be identified in the region, with high-yield farms emitting no different amounts of GHGs per hectare compared to low-yield farms. Building on cluster analysis, with farms grouped according to their economic and environmental performance into ‘poor’, ‘fair' and ‘good’ producers, the regional GHG mitigation potential was estimated. Under the scenario assumption that all grain in the NCP is produced under ‘good’ production conditions, 21% and 7% of GHG could be mitigated in wheat and maize production, respectively. The study shows that in the NCP, exemplary for China's rapidly developing agricultural sector, the crop management skills of a substantial share of farmers could obviously not keep pace with the massive input intensification. Among others, farmer–farmer trainings are recommended to close the gap in crop production performance among producers.     

A decision model for regional industrial recruitment and development

Although the practice of industrial recruitment is widespread among regions, its objectives and constraints are neither well established nor carefully evaluated in determining the most desirable industries. As a result, many regions frequently resort to a blind scramble for new industries. This paper suggests a decision model for a regional economy to determine the priority in industrial recruitment. The problem is formulated in terms of a mathematical programming model in which the objectives and constraints are explicitly stated. Regional input-output, table provides additional constraints in the model. The input-output table is also used in testing the economic feasibility of optimal solutions.     

Forecasting economic and financial variables with global VARs

This paper considers the problem of forecasting economic and financial variables across a large number of countries in the global economy. To this end a global vector autoregressive (GVAR) model, previously estimated by Dees, di Mauro, Pesaran, and Smith (2007) and Dees, Holly, Pesaran, and Smith (2007) over the period 1979Q1–2003Q4, is used to generate out-of-sample forecasts one and four quarters ahead for real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1–2005Q4. Forecasts are obtained for 134 variables from 26 regions, which are made up of 33 countries and cover about 90% of the world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. Building on the forecast combination literature, the effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modelling problem, and the heterogeneity of the economies considered–industrialised, emerging, and less developed countries–as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed, the double-averaged GVAR forecasts perform better than the benchmark competitors, especially for output, inflation and real equity prices.     

An Examination of Takeovers, Job Loss and the Wage Decline within UK Industry

This paper investigates the effects of takeovers on workers’ employment prospects and wages in the UK for the years 1987–1995. We address directly the idea that takeovers involve a ‘breach of trust’ with employees. Our results provide no support for the breach of trust hypothesis and rather suggest shareholders and workers in the post‐acquisition joint entity are locked in a form of ‘equal misery’ following the execution of the takeover. There already an exist a wide range of event studies documenting the effect of takeovers on shareholders and a smaller number of studies discussing the impact of takeovers on employees. The contribution of the present study is to relate the separate effects of acquisition on these two groups to each other. By doing so we seek to test directly the proposition that takeovers reallocate rents from workers to target shareholders, via the bid‐premia paid on acquisition.     

Stress testing banks

How much capital and liquidity does a bank need to support its risk taking activities? During the recent (and still ongoing) financial crisis, answers to this question using standard approaches, e.g., regulatory capital ratios, were no longer credible, and thus broad-based supervisory stress testing became the new tool. Bank balance sheets are notoriously opaque and susceptible to asset substitution (easy swapping of high risk for low risk assets), so stress tests, tailored to the situation at hand, can provide clarity by openly disclosing details of the results and approaches taken, allowing trust to be regained. With that trust re-established, the cost-benefit of stress testing disclosures may tip away from bank-specific towards more aggregated information. This paper lays out a framework for the stress testing of banks: why it is useful and why it has become such a popular tool for the regulatory community in the course of the recent financial crisis; how stress testing is done (design and execution); and finally, with stress testing results in hand, how one should handle their disclosure, and whether it should be different in crisis vs. “normal” times.