6 résultats pour « stochastic »

Uncertainty Propagation and Dynamic Robust Risk Measures

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The framework presents a method to quantify #uncertainty propagation in #dynamic #scenarios, focusing on discrete #stochastic processes over a limited time span. These dynamic uncertainty sets encompass various uncertainties like distributional ambiguity, utilizing tools like the Wasserstein distance and $f$-divergences. Dynamic robust #risk #measures, defined as maximum #risks within uncertainty sets, exhibit properties like convexity and coherence based on uncertainty set conditions. $f$-divergence-derived sets yield strong time-consistency, while Wasserstein distance leads to a new non-normalized time-consistency. Recursive representations of one-step conditional robust risk measures underlie strong or non-normalized time-consistency.

Optimal Premium Pricing in a Competitive Stochastic Insurance Market with Incomplete Information

"This paper examines a #stochastic one-period #insurancemarket with incomplete information. The aggregate amount of #claims follows a compound #poisson distribution. #insurers are assumed to be exponential utility maximizers, with their degree of #riskaversion forming their private information. A premium strategy is defined as a map between risk types and premium rates. The optimal premium strategies are denoted by the pure-strategy #bayesian #nash equilibrium, whose existence and uniqueness are demonstrated under specific conditions for the demand function..."

A stochastic volatility model for the valuation of temperature derivatives

"This model allows to be more conservative regarding extreme events while keeping tractability. We give a method based on Conditional Least Squares to estimate the parameters on daily data and estimate our model on eight major European cities... This new model allows to better assess the risk related to temperature volatility."

A Stochastic Climate Model -- An approach to calibrate the Climate‑Extended Risk Model (CERM)

"These parameters can be calibrated using public data. This new approach means not only to evaluate climate risks without picking any specific scenario but also allows to fill the gap between current one year approach of regulatory and economic capital models and the necessarily long-term view of climate risks by designing a framework to evaluate the resulting credit loss on each step (typically yearly) of the transition path. This new approach could prove instrumental in the 2022 context of central banks weighing the pros and cons of a climate capital charge."