Modeling the 2003 heatwave in Paris (paper)
In February 2005, opening the conference on Climate change: a global, national
and regional challenge, chairman Dennis Tirpak pointed out that there is no
longer any doubt that the Earth’s climate is changing […] globally, nine of
the past 10 years have been the warmest since records began in 1861“. He
singled out the heatwave that gripped western Europe in 2003 as an example:
Europe’s worst natural disaster in 50 years killed as many as 40; 000 people
and inflicted an estimated 30 billion dollars in damage. Hence, the summer of
2003 will be remembered for the extreme heat, and the approximately 40,000
heat-related deaths over western Europe (from 30,000 up to more than 52,000
depending on the source). More specifically, the period 1-15 August 2003 was
the most intense heat of the summer. The report of Pirard et al. (2005) states
that Europe experienced an unprecedented heat wave in the Summer 2003. In
France, it was the warmest summer recorded for 53 years in terms of minimal,
maximal and average temperature and in terms of duration” […]
Conférence Insurance and Adaptation to Climate Change, Paris, Mars 2007. The paper appeared in the Geneva Papers.
The IPCC 2007 report noted that both the frequency and strength of hurricanes, floods and droughts have increased during the past few years. Thus, climate risk, and more specifically natural catastrophes, are now hardly insurable: losses can be huge (and the actuarial pure premium might even be infinite), diversification through the central limit theorem is not possible because of geographical correlation (a lot of additional capital is required), there might exist no insurance market since the price asked by insurance companies can be much higher than the price householders are willing to pay (short-term horizon of policyholders), and, due to climate change, there is more uncertainty (and thus additional risk). The first idea we will discuss in this paper, about insurance markets and climate risks, is that insurance exists only if risk can be transferred, not only to reinsurance companies but also to capital markets (through securitization or catastrophes options). The second one is that climate is changing, and therefore, not only prices and capital required should be important, but also uncertainty can be very large. It is extremely difficult to insure in a changing environment.
The paper was presented in a conference, in Paris, in 2007
Exposé au séminaire d’Actuariat, à la faculté de Sciences Economiques à Amsterdam (UvA). L’exposé portera sur l’estimation de quantiles (et de Value-at-Risk) sur des données de pertes, avec une application de gestion de portefeuille moyenne-VaR.
In this talk we propose several nonparametric estimators of quantiles based on Beta kernel and applied to transformed data by the generalized Champernowne distribution initially fitted to the data. A Monte-Carlo based study will show that those estimators improve the efficiency, not only for light tailed distributions, but mainly for heavy tailed, when the probability level is close to 1.Another application will be seen, on portfolio optimization in the mean-VaR context.
Exposé à Toulouse 1, sur l’estimation nonparamétrique de quantiles.
In this talk we propose several nonparametric estimators of quantiles based on Beta kernel and applied to transformed data by the generalized Champernowne distribution initially fitted to the data. A Monte-Carlo based study will show that those estimators improve the efficiency, not only for light tailed distributions, but mainly for heavy tailed, when the probability level is close to 1. Another application will be seen, on portfolio optimization in the mean-VaR context.
The paper (with David Sibai) on High Frequency models in hydrology just appeared, in Environmetrics.
When working on river floods—annual river levels maxima—, two approaches are usually considered: one inspired from Emil Gumbel where annual maxima are supposed to be i.i.d. and distributed according to Gumbel’s distribution, and one inspired from Edwin Hurst where annual maxima are strongly dependent, and exhibit long range memory. This paper tries to solve this apparent paradox by deriving a dynamic model inspired from financial models, which does not take into account annual maxima only but also threshold exceedances. It studies the implications of such a paradox in terms of return period—a notion valid as long as the data are i.i.d—and of extremal events.
Deux séances dans le cadre de la formation Gestion des risques bancaires et financiers, chez EdF
10. Mesures de risques, aspect règlementaires et principes de base (slides)
11. Copules et dépendance (slides)
Quelques heures de cours à l’ESC Rennes sur sur le thème Sales Forecasting. Les slides sont en ligne, avec la partie 1 et la partie 2 (j’ai aussi mis en ligne la base de données sur le trafic autoroutier). Parmi les contraintes techniques, il fallait utiliser Excel (exclusivement). Je mets donc des liens vers macro1 et macro2 qui sont des add-ins permettant de faire un peu de séries temporelles. Sinon un petit exemple est aussi disponible…
La partie 1 est
et la partie 2
Cette semaine, je ferais un exposé aux XXXVIVe Journées de statistiques à Angers sur l’allocation optimale sous contrainte de VaR, et je serais chairman de la session Risk Management.
Formation interne pour AXA University sur Measuring and covering catastrophic risk. Les slides sont en ligne
Exposé aux Gemeinsame Jahrestagung der Deutschen Mathematiker-Vereinigung und der Gesellschaft für Didaktik der Mathematik, dans la session Multivariate Dependence Modelling using Copulas – Applications in Finance, Humboldt-Universität zu Berlin, Mars 2007, à partir du chapitre écrit avec Jean David Fermanian et Olivier Scaillet sur The estimation of copulas : theory and pratice.
Copulas are a way of formalising dependence structures of random vectors. Although they have been known about for a long time (Sklar (1959)), they have been rediscovered relatively recently in applied sciences (biostatistics, reliability, biology, etc). In finance, they have become a standard tool with broad applications: multiasset pricing (especially complex credit derivatives), credit portfolio modelling, risk management, etc. For example, see Li (1999), Patton (2001) and Longin and Solnik (1995). […] In this chapter, we focus on the practical issues practitioners are faced with, in particular concerning estimation and visualisation. In the first section, we give a general setting for the estimation of copulas. Such a framework embraces most of the available techniques. In the second section, we deal with the estimation of the copula density itself, with a particular focus on estimation near the boundaries of the unit square.