154 résultats pour « riskmanagement »

Bayesian Model Selection and Prior Calibration for Structural Models in Economic Experiments

"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."

Aggregating heavy‑tailed random vectors: from finite sums to Lévy processes

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"... 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..."

Building up Cyber Resilience by Better Grasping Cyber Risk Via a New Algorithm for Modelling...

"We propose here an analysis of the database of the cyber complaints filed at the Gendarmerie Nationale.We perform this analysis with a new algorithm developed for non-negative asymmetric heavy-tailed data, which could become a handy tool in applied fields. This method gives a good estimation of the full distribution including the tail. Our study confirms the finiteness of the loss expectation, necessary condition for insurability."

Financing Constraints and Risk Management: Evidence From Micro‑Level Insurance Data

"Using data on credit scores matched with unique information on firm level commercial insurance purchases, we find that financing constraints lead to higher insurance spending. We adopt a regression discontinuity design and show that financially constrained firms spend 5–14% more on insurance than otherwise similar unconstrained firms. "

Machine Learning for Categorization of Operational Risk Events Using Textual Description

"... an overview of how machine learning can help in categorizing textual descriptions of operational loss events into Basel II event types. We apply PYTHON implementations of support vector machine and multinomial naive Bayes algorithms to precategorized Öffentliche Schadenfälle OpRisk (ÖffSchOR) data to demonstrate that operational loss events can be automatically assigned to one of the seven Basel II event types with very few costs and satisfactory accuracy."

Strategic Data Access Management

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"An employee may be attacked by a potentially sophisticated adversary whose goal is to steal all their data. Therefore, the firm trades off the efficiency benefit of the more permissive data access architecture with the adversarial risk it incurs. We characterize the firm's optimal data access architecture and investigate how it depends both on the adversarial environment and the firm's technology."