Assessing Financial Model Risk

Model risk has a huge impact on any risk measurement procedure and its quantification is therefore a crucial step. In this paper, we introduce three quantitative measures of model risk when choosing a particular reference model within a given class: the absolute measure of model risk, the relative measure of model risk and the local measure of model risk. Each of the measures has a specific purpose and so allows for flexibility. We illustrate the various notions by studying some relevant examples, so as to emphasize the practicability and tractability of our approach.Comment: 23 pages, 6 figure

The Paradox of Precaution

In the United States and most industrialized countries, regulatory policies and decision-making pertaining to food safety, occupational health and environmental protection are science-based. The actual pace and complexity of technological innovation, however, make it increasingly necessary to deal with situations where science cannot yet provide a definite picture. In this context, a now widely invoked rule, known as the 'Precautionary Principle', recommends to 'err on the side of preservation' until better scientific information becomes available. We draw a formal representation of this statement, and we show that it exhibits a logical contradiction. This negative result conveys a clarification of the type of actions science-based regulation should consider in the presence of scientific uncertainty. Aux États-Unis et dans la plupart des pays industrialisés, les règlements et politiques publics relatifs à la sécurité alimentaire, la santé au travail et la protection de l'environnement sont en principe basés sur l'information émanant des scientifiques. L'accélération et la complexité du progrès technologique rendent toutefois inévitable pour le régulateur de devoir prendre des décisions avant que la science puisse fournir une représentation claire du risque. Dans ce contexte, l'approche dite du «Principe de précaution» recommande d'«errer du côté de la prévention» jusqu'à ce que les scientifiques puissent donner le ton juste. Nous produisons une représentation formelle de ce principe, et nous montrons qu'il contient une incohérence logique. Ce résultat négatif permet néanmoins de préciser le type d'actions que la réglementation des risques basée sur la science devrait promouvoir en présence d'incertitude scientifique.Environmental and health risks; science-based regulation; scientific uncertainty; Precautionary Principle, Risques à la santé humaine et à l'environnement, réglementation basée sur la science, incertitude scientifique, principe de précaution

Monotone stability of quadratic semimartingales with applications to general quadratic BSDEs and unbounded existence result

In this paper, we study the stability and convergence of some general quadratic semimartingales. Motivated by financial applications, we study simultaneously the semimartingale and its opposite. Their characterization and integrability properties are obtained through some useful exponential inequalities on the absolute value of the terminal condition. Then, a general stability result, including the strong convergence of the martingale parts, is derived under some mild integrability condition on the exponential of the terminal value of the semimartingale.\\ This strong convergence result is then applied to the study of general quadratic BSDEs, which does not involve the usual exponential transformation but relies on a regularization with both linear-quadratic growth of the quadratic coefficient it-self through inf-convolution. Strong convergence results for BSDEs are then obtained in a general framework using the stability results previously obtained using a forward point of view and considering the quadratic BSDEs as a particular type of quadratic semimartingales

A finite mixture modelling perspective for combining experts’ opinions with an application to quantile-based risk measures

The key purpose of this paper is to present an alternative viewpoint for combining expert opinions based on finite mixture models. Moreover, we consider that the components of the mixture are not necessarily assumed to be from the same parametric family. This approach can enable the agent to make informed decisions about the uncertain quantity of interest in a flexible manner that accounts for multiple sources of heterogeneity involved in the opinions expressed by the experts in terms of the parametric family, the parameters of each component density, and also the mixing weights. Finally, the proposed models are employed for numerically computing quantile-based risk measures in a collective decision making context

Pricing q-forward contracts: an evaluation of estimation window and pricing method under different mortality models

The aim of this paper is to study the impact of various sources of uncertainty on the pricing of a special longevity–based instrument: a q-forward contract. At the expiry of a q-forward contract, the realized mortality rate for a given population is exchanged in return for a fixed (mortality) rate that is agreed at the initiation of the contract. Pricing a q-forward involves determining this fixed rate. In our study, we disentangle three main sources of uncertainty and consider their impact on pricing: model choice for the underlying mortality rate, time-window used for estimation and the pricing method itself

A random forest based approach for predicting spreads in the primary catastrophe bond market

We introduce a random forest approach to enable spreads’ prediction in the primary catastrophe bond market. In a purely predictive framework, we assess the importance of catastrophe spread predictors using permutation and minimal depth methods. The whole population of non-life catastrophe bonds issued from December 2009 to May 2018 is used. We find that random forest has at least as good prediction performance as our benchmark-linear regression in the temporal context, and better prediction performance in the non-temporal one. Random forest also performs better than the benchmark when multiple predictors are excluded in accordance with the importance rankings or at random, which indicates that random forest extracts information from existing predictors more effectively and captures interactions better without the need to specify them. The results of random forest, in terms of prediction accuracy and the minimal depth importance are stable. There is only a small divergence between the drivers of catastrophe bond spread in the predictive versus explanatory framework. We believe that the usage of random forest can speed up investment decisions in the catastrophe bond industry both for would-be issuers and investors

Assessing contaminated land cleanup costs and strategies

The remediation of contaminated sites is often subject to substantial cost overruns. This persistent discrepancy between estimated and realized costs is chiefly responsible for misguided land use and wasteful delays in the reconversion of former industrial sites. In order to deal with incomplete information and uncertainty in this context, this paper draws on stochastic modeling and mathematical finance methods. We show that relatively simple and usable formulas can then be derived for better assessing cleanup strategies. These formulas apply to generic remediation technologies and scenarios. They are robust to misspecification of key parameters (like the effectiveness of a prescribed treatment). They also yield practical rules for decision making and budget provisioning