Nos derniers articles

This paper discusses the origins of modern #riskmanagement concepts and applications in the #financialindustry, which were developed at Bankers Trust in the 1970s. The bank's "Resources Management" group applied #probability theory to measure #marketrisk, #creditrisk, #liquidityrisk, and #operationalrisk, which were later brought together in a metric called Risk Adjusted Return On Capital (RAROC). RAROC was used to evaluate profitability, guide strategic planning, capital allocation, and incentive compensation. The article also discusses how Bankers Trust's risk management culture deteriorated after 1995, leading to its acquisition by #deutschebank Bank in 1998.

"We respond to Tetlock et al. (2022) showing 1) how expert judgment fails to reflect tail risk, 2) the lack of compatibility between forecasting tournaments and tail risk assessment methods (such as extreme value theory). More importantly, we communicate a new result showing a greater gap between the properties of tail expectation and those of the corresponding probability."

"Bayesian estimates from experimental data can be influenced by highly diffuse or "uninformative" priors. This paper discusses how practitioners can use their own expertise to critique and select a prior that (i) incorporates our knowledge as experts in the field, and (ii) achieves favorable sampling properties. I demonstrate these techniques using data from eleven experiments of decision-making under risk, and discuss some implications of the findings."

"... we study the behavior of the asymptotic tail distribution of independent sums of heavy-tailed random vectors under the paradigm of multivariate regular variation. Assessment of such tail probabilities are of interest in risk management for many finance, insurance, queueing, and environmental applications. Multidimensional tail events are often characterized by at least one variable exceeding a high threshold, and the asymptotic probability of such events follow the so-called “one large jump” principle..."

" In this paper, we use stochastic algorithms schemes in estimating MSRM [market data based systemic risk measure] and prove that the resulting estimators are consistent and asymptotically normal. We also test numerically the performance of these algorithms on several examples."

"Our paper contributes to the theory of conditional risk measures and conditional certainty equivalents. We adopt a random modular approach which proved to be effective in the study of modular convex analysis and conditional risk measures."

" The aim is to come up with a convex risk functional that incorporates a sefety margin with respect to nonparametric uncertainty and still can be approximated through parametrized models. The particular form of the parametrization allows us to develop a numerical method, based on neural networks, which gives both the value of the risk functional and the optimal perturbation of the reference measure."

"We present a framework for constructing multivariate risk measures that is inspired from univariate Optimized Certainty Equivalent (OCE) risk measures. We show that this new class of risk measures verifies the desirable properties such as convexity, monotonocity and cash invariance. We also address numerical aspects of their computations using stochastic algorithms instead of using Monte Carlo or Fourier methods that do not provide any error of the estimation."

"... we characterize Pareto-optimal risk-sharing contracts in a market with multiple policyholders and one representative insurer. With minimal assumptions on the risk measures of the parties involved, we characterize Pareto optimality in terms of the minimization of a sum of the agents' risk positions, and we relate it to both the core and coalitional stability of an associated market game. In the special case of coherent risk measures, the optimal indemnity schedules are further characterized in explicit form, in terms of what can be called "worst-case probability measures". "

"Quasi-convexity in probabilistic mixtures is a common and useful property in decision analysis. We study a general class of non-monotone mappings, called the generalized rank-dependent functions, which include the preference models of expected utilities, dual utilities, and rank-dependent utilities as special cases, as well as signed Choquet integrals used in risk management."