Regulatory versus Informational Value of Bond Ratings: Hints from History ...

A multivariate analysis can be used in order to investigate the relationship between bond yields, ratings and standard control variables. Replicating such a test on a number of cross-sections may evidence a possible impact of financial regulations relying on ratings. Datasets for American corporate bond issues allow a focus on two key events of the development of rating driven regulations: the valuation of bank and life insurance portfolios introduced in the 1930’s and the net capital requirements for broker dealers introduced in the 1970’s. The “value” of bond ratings does show some improvement once these regulations have been passed.Bond ratings, bond yields, financial regulation.

A Century of Bond Ratings as a Business

Historical accounting datasets about a leader of the bond rating industry have been gathered in order to provide an unprecedented long term view on this business. To better judge of the dynamics at play, similar data for representatives of older and broader business fields is also introduced. Overall, this empirical discussion plays down the importance of regulatory « licenses » given to bond rating firms and puts forward the coming of a « modern » business model where issuers pay for ratings.Industry study, bond ratings, financial regulation

The Crux of the Matter: Ratings and Credit Risk Valuation at the heart of the Structured Finance Crisis

The 2007/2008 global credit crisis was born out of opaque securitization transactions. Introducing structured products risk estimation techniques shows how the most basic investment analysis could not be done without detailed and updated knowledge on the assets of the pool. Access to such details was crucial for investors to perform an autonomous valuation, the lack of which led to a pervading acceptance of ratings at face value. The crisis brought numerous delusions to naïve users of these privately issued opinions. Coming back to the central role that investor played during the previous speculative episode and introducing a theoretical discussion on the dynamics of market finance, it is shown that trusting market discipline and due diligence was bound to end up being misguiding. Given that unprecedented rating volatility brought a share of the blame game to rating firms, strategies that would aim at securing an informed use of ratings are finally outlined.financial crisis, credit risk, rating agencies

Stochastic target problems with controlled loss in jump diffusion models

In this paper, we consider a mixed diffusion version of the stochastic target problem introduced by Bouchard et al. (2009). This consists in finding the minimum initial value of a controlled process which guarantees to reach a controlled stochastic target with a given lovel of expected loss. As in Bouchard et al. (2009), it can be converted into a standard stochastic target problem, as already studied by Soner and Touzi (2002) or Bouchard (2002) for the mixed diffusion case, by increasing both the state space and the dimension of the control. In our mixed-diffusion setting, the main difficulty comes from the presence of jumps, which leads to the introduction of a new kind of controls that take values in an unbounded set of measurable maps. This has non trivial impacts on the formulation and derivation of the associated partial differential equations.stochastic target problem; mixed diffusion process; discontinuous viscosity solutions; quantile hedging

Locating structural changes in a multiple scattering domain with an irregular shape

International audienceLocadiff is a method for imaging local structural changes in a random, heterogeneous medium. It relies on the combination of a forward model to calculate the sensitivity kernel of the source-receiver pairs, with an inversion method to determine the position of the changes. So far, the sensitivity kernel has been evaluated based on an analytical solution of the diffusion equation, which lacks the flexibility to handle problems where the domain has boundaries with an irregular shape. Moreover, the accuracy of the previous inversion method, based on linear algebra tools, was very sensitive to the values of the inversion parameters. This paper introduces a more generic approach to solve both these issues. The first problem is tackled by the implementation of numerical method as an alternative for solving the diffusion equation. The second problem is tackled by the introduction of enhanced optimization algorithms to improve the stability of the inversion. This improved version of Locadiff is validated via both numerical examples and experimental data from an actual civil engineering problem

Analysis of micro-seismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring

In the perspective of upcoming seasonally ice-free Arctic, understanding the dynamics of sea ice in the changing climate is a major challenge in oceanography and climatology. In particular, the new generation of sea ice models will require fine parameterization of sea ice thickness and rheology. With the rapidly evolving state of sea ice, achieving better accuracy, as well as finer temporal and spatial resolutions of its thickness will set new monitoring standards, with major scientific and geopolitical implications. Recent studies have shown the potential of passive seismology to monitor the thickness, density and elastic properties of sea ice with significantly reduced logistical constraints. For example, human intervention is no longer required, except to install and uninstall the geophones. Building up on this approach, we introduce a methodology for estimating sea ice thickness with high spatial and temporal resolutions from the analysis of icequakes waveforms. This methodology is based on a deep convolutional neural network for automatic clustering of the ambient seismicity recorded on sea ice, combined with a Bayesian inversion of the clustered waveforms. By applying this approach to seismic data recorded in March 2019 on fast ice in the Van Mijen fjord (Svalbard), we observe the spatial clustering of icequakes sources along the shore line of the fjord. The ice thickness is shown to follow an increasing trend that is consistent with the evolution of temperatures during the four weeks of data recording. Comparing the energy of the icequakes with that of calibrated seismic sources, we were able to derive a power law of icequake energy, and to relate this energy to the size of the cracks that generate the icequakes.</p