Empirical Implementation of a 2-Factor Structural Model for Loss-Given-Default

In this study we develop a theoretical model for ultimate loss-given default in the Merton (1974) structural credit risk model framework, deriving compound option formulae to model differential seniority of instruments, and incorporating an optimal foreclosure threshold. We consider an extension that allows for an independent recovery rate process, representing undiversifiable recovery risk, having a stochastic drift. The comparative statics of this model are analyzed and compared and in the empirical exercise, we calibrate the models to observed LGDs on bonds and loans having both trading prices at default and at resolution of default, utilizing an extensive sample of losses on defaulted firms (Moody’s Ultimate Recovery Database™), 800 defaults in the period 1987-2008 that are largely representative of the U.S. large corporate loss experience, for which we have the complete capital structures and can track the recoveries on all instruments from the time of default to the time of resolution. We find that parameter estimates vary significantly across recovery segments, that the estimated volatilities of recovery rates and of their drifts are increasing in seniority (bank loans versus bonds). We also find that the component of total recovery volatility attributable to the LGD-side (as opposed to the PD-side) systematic factor is greater for higher ranked instruments and that more senior instruments have lower default risk, higher recovery rate return and volatility, as well as greater correlation between PD and LGD. Analyzing the implications of our model for the quantification of downturn LGD, we find the ratio of the later to ELGD (the “LGD markup”) to be declining in expected LGD, but uniformly higher for lower ranked instruments or for higher PD-LGD correlation. Finally, we validate the model in an out-of-sample bootstrap exercise, comparing it to a high-dimensional regression model and to a non-parametric benchmark based upon the same data, where we find our model to compare favorably. We conclude that our model is worthy of consideration to risk managers, as well as supervisors concerned with advanced IRB under the Basel II capital accord.LGD; credit risk; default; structural model

Empirical Analysis and Trading Strategies for Defaulted Debt Securities with Models for Risk and Investment Management

This study empirically analyzes the historical performance of defaulted debt from Moody’s Ultimate Recovery Database (1987-2010). Motivated by a stylized structural model of credit risk with systematic recovery risk, we argue and find evidence that returns on defaulted debt co-vary with determinants of the market risk premium, firm specific and structural factors. Defaulted debt returns in our sample are observed to be increasing in collateral quality or debt cushion of the issue. Returns are also increasing for issuers having superior ratings at origination, more leverage at default, higher cumulative abnormal returns on equity prior to default, or greater market implied loss severity at default. Considering systematic factors, returns on defaulted debt are positively related to equity market indices and industry default rates. On the other hand, defaulted debt returns decrease with short-term interest rates. In a rolling out-of-time and out-of-sample resampling experiment we show that our leading model exhibits superior performance. We also document the economic significance of these results through excess abnormal returns, implementing a hypothetical trading strategy, of around 5-6% (2-3%) assuming zero (1bp per month) round-trip transaction costs. These results are of practical relevance to investors and risk managers in this segment of the fixed income market.Distressed Debt; Recoveries; Default; Credit Risk

End-to-end Recovery of Human Shape and Pose

We describe Human Mesh Recovery (HMR), an end-to-end framework for reconstructing a full 3D mesh of a human body from a single RGB image. In contrast to most current methods that compute 2D or 3D joint locations, we produce a richer and more useful mesh representation that is parameterized by shape and 3D joint angles. The main objective is to minimize the reprojection loss of keypoints, which allow our model to be trained using images in-the-wild that only have ground truth 2D annotations. However, the reprojection loss alone leaves the model highly under constrained. In this work we address this problem by introducing an adversary trained to tell whether a human body parameter is real or not using a large database of 3D human meshes. We show that HMR can be trained with and without using any paired 2D-to-3D supervision. We do not rely on intermediate 2D keypoint detections and infer 3D pose and shape parameters directly from image pixels. Our model runs in real-time given a bounding box containing the person. We demonstrate our approach on various images in-the-wild and out-perform previous optimization based methods that output 3D meshes and show competitive results on tasks such as 3D joint location estimation and part segmentation.Comment: CVPR 2018, Project page with code: https://akanazawa.github.io/hmr

The Brexit-Trump syndrome: it’s the economics, stupid

For decades, investment has been falling, living standards have declined, and inequality has risen. What the Brexit and Trump campaigns shared was that they exploited the resulting disaffection by blaming those problems on external forces, including globalisation. Yet these problems were not the inevitable results of globalisation, but of domestic policy choices, influenced by flawed economic theories. Michael Jacobs and Mariana Mazzucato explain why and how we need to rethink contemporary capitalism

Antitrust Error

Fueled by economics, antitrust has evolved into a highly sophisticated body of law. Its malleable doctrine enables courts to tailor optimal standards to a wide variety of economic phenomena. Indeed, economic theory has been so revolutionary that modern U.S. competition law bears little resemblance to that which prevailed fifty years ago. Yet, for all the contributions of economics, its explanatory powers are subject to important limitations. Profound questions remain at the borders of contemporary antitrust enforcement, but answers remain elusive. It is because of the epistemological limitations of economic analysis that antitrust remains unusually vulnerable to error. The fear of mistakenly ascribing anticompetitive labels to innocuous conduct is now pervasive. The Supreme Court has repeatedly framed its rulings in a manner that shows sensitivity to the unavoidability of error. In doing so, it has adopted the principle of decision theory that Type I errors are generally to be preferred over Type II. It has crafted a pro-defendant body of jurisprudence accordingly. In 2008, the Justice Department picked up the gauntlet and published the first definitive attempt at extrapolating optimal error rules. Yet, in 2009, the new administration promptly withdrew the report, opining that it could “separate the wheat from the chaff” and thus marginalizing the issue of error. Notwithstanding this confident proclamation, error remains as visible as ever. Intel’s behavior in offering rebates has been subject to wildly fluctuating analysis by the U.S. and E.U. enforcement agencies. In a marked departure from precedent, the DOJ is again viewing vertical mergers with concern. And the agency has reversed course on the legality of exclusionary payments in the pharmaceutical industry. Antitrust divergence, both within and outside the United States, remains painfully apparent, demonstrable proof that vulnerability to error remains systemic. For this reason, error analysis may be the single most important unresolved issue facing modern competition policy. This Article seeks to challenge the contemporary mode of error analysis in antitrust law. We explain the causes and consequences of antitrust error and articulate a variety of suggested cures. In doing so, we debunk the current presumption that false positives are necessarily to be preferred over false negatives. We highlight a variety of cases in which the contemporary bias in favor of underenforcement should be revisited