Permanent and Transitory Factors Affecting the Dynamics of the Term Structure of Interest Rates

This paper proposes a novel methodology, based on the Common Principal Component analysis, allowing one to estimate the factors driving the term structure of interest rates, in the presence of time-varying covariance structure. The advantages of this method are first, that, unlike classical principal component analysis, common factors can be estimated without assuming that the volatility of the factors is constant; and second, that the factor structure can be decomposed into permanent and transitory common factors. We conclude that only permanent factors are relevant for modeling the dynamics of interest rates, and that the common principal component approach appears to be more accurate than the classical principal component one to estimate the risk factor structure.Term Structure of Interest Rates, Principal Component Analy-sis, Common Principal Component Analysis

Evolution of Market Uncertainty around Earnings Announcements

This paper investigates theoretically and empirically the dynamics of the implied volatility (or implied standard deviation - ISD) around earnings announcements dates. The volatility implied by option prices can be interpreted as the level of volatility expected by the market over the remaining life of the option. We propose a theoretical framework for the evolution of the ISD that takes into account two well-known features of the instantaneous volatility: volatility clustering and the leverage effect. In this context, the ISD should decrease after an earnings announcement but the post-announcement ISD path depends on the content of the earnings announcement: good news or bad news. An empirical investigation is conducted on the Swiss market over the period 1989-1998.

Multi-scale problems, high performance computing and hybrid numerical methods

International audienceThe turbulent transport of a passive scalar is an important and challenging problem in many applications in fluid mechanics. It involves different range of scales in the fluid and in the scalar and requires important computational resources. In this work we show how hybrid numerical methods, combining Eulerian and Lagrangian schemes, are natural tools to address this multi-scale problem. One in particular shows that in homogeneous turbulence experiments at various Schmidt numbers these methods allow to recover the theoretical predictions of universal scaling at a minimal cost. We also outline hybrid methods can take advantage of heterogeneous platforms combining CPU and GPU processors

A high order purely frequency-based harmonic balance formulation for continuation of periodic solutions

Combinig the harmonic balance method (HBM) and a continuation method is a well-known technique to follow the periodic solutions of dynamical systems when a control parameter is varied. However, since deriving the algebraic system containing the Fourier coefficients can be a highly cumbersome procedure, the classical HBM is often limited to polynomial (quadratic and cubic) nonlinearities and/or a few harmonics. Several variations on the classical HBM, such as the incremental HBM or the alternating frequency/time domain HBM, have been presented in the literature to overcome this shortcoming. Here, we present an alternative approach that can be applied to a very large class of dynamical systems (autonomous or forced) with smooth equations. The main idea is to systematically recast the dynamical system in quadratic polynomial form before applying the HBM. Once the equations have been rendered quadratic, it becomes obvious to derive the algebraic system and solve it by the so-called ANM continuation technique. Several classical examples are presented to illustrate the use of this numerical approach.Comment: PACS numbers: 02.30.Mv, 02.30.Nw, 02.30.Px, 02.60.-x, 02.70.-

Long-Term Clinical Protection from Falciparum Malaria Is Strongly Associated with IgG3 Antibodies to Merozoite Surface Protein 3

Using data from malaria cases in Senegal, Pierre Druilhe and colleagues found that antibodies of the IgG3 isotype directed to merozoite surface protein 3 (MSP3) were strongly predictive of clinical outcome

Systemic Risk Score: A Suggestion

Abstract: We identify a potential bias in the methodology disclosed in July 2013 by the Basel Committee on Banking Supervision (BCBS) for identifying systemically important financial banks. Contrary to the original objective, the relative importance of the five categories of risk importance (size, cross‐ jurisdictional activity, interconnectedness, substitutability/financial institution infrastructure, and complexity) may not be equal and the resulting systemic risk scores are mechanically dominated by the most volatile categories. In practice, this bias proved to be serious enough that the substitutability category had to be capped by the BCBS. We show that the bias can be removed by simply standardizing each input prior to computing the systemic risk scores. 1

Machine Learning et nouvelles sources de données pour le scoring de crédit

In this article, we discuss the contribution of Machine Learning techniques and new data sources (New Data) to credit-risk modelling. Credit scoring was historically one of the first fields of application of Machine Learning techniques. Today, these techniques permit to exploit new sources of data made available by the digitalization of customer relationships and social networks. The combination of the emergence of new methodologies and new data has structurally changed the credit industry and favored the emergence of new players. First, we analyse the incremental contribution of Machine Learning techniques per se. We show that they lead to significant productivity gains but that the forecasting improvement remains modest. Second, we quantify the contribution of the "datadiversity", whether or not these new data are exploited through Machine Learning. It appears that some of these data contain weak signals that significantly improve the quality of the assessment of borrowers' creditworthiness. At the microeconomic level, these new approaches promote financial inclusion and access to credit for the most vulnerable borrowers. However, Machine Learning applied to these data can also lead to severe biases and discrimination.Dans cet article, nous proposons une réflexion sur l’apport des techniques d’apprentissage automatique (Machine Learning) et des nouvelles sources de données (New Data) pour la modélisation du risque de crédit. Le scoring de crédit fut historiquement l’un des premiers champs d’application des techniques de Machine Learning. Aujourd’hui, ces techniques permettent d’exploiter de « nouvelles » données rendues disponibles par la digitalisation de la relation clientèle et les réseaux sociaux. La conjonction de l’émergence de nouvelles méthodologies et de nouvelles données a ainsi modifié de façon structurelle l’industrie du crédit et favorisé l’émergence de nouveaux acteurs. Premièrement, nous analysons l’apport des algorithmes de Machine Learning à ensemble d’information constant. Nous montrons qu’il existe des gains de productivité liés à ces nouvelles approches mais que les gains de prévision du risque de crédit restent en revanche modestes. Deuxièmement, nous évaluons l’apport de cette « datadiversité », que ces nouvelles données soient exploitées ou non par des techniques de Machine Learning. Il s’avère que certaines de ces données permettent de révéler des signaux faibles qui améliorent sensiblement la qualité de l’évaluation de la solvabilité des emprunteurs. Au niveau microéconomique, ces nouvelles approches favorisent l’inclusion financière et l’accès au crédit des emprunteurs les plus fragiles. Cependant, le Machine Learning appliqué à ces données peut aussi conduire à des biais et à des phénomènes de discrimination

Margin Backtesting

This paper presents a validation framework for collateral requirements or margins on a derivatives exchange. It can be used by investors, risk managers, and regulators to check the accuracy of a margining system. The statistical tests presented in this study are based either on the number, frequency, magnitude, or timing of margin exceedances, which are de…ned as situations in which the trading loss of a market participant exceeds his or her margin. We also propose an original way to validate globally the margining system by aggregating individual backtesting statistics obtained for each market participant. JEL classi…cation: G1