8 résultats
pour « riskmeasures »
This research addresses the critical challenge of model ambiguity in insurance, where the true probabilities of losses are uncertain. It introduces randomly distorted Choquet integrals, a novel mathematical tool for creating flexible and dynamic risk measures. This provides a formal, unified methodology to resolve expert disagreements by extending industry-standard metrics like Value at Risk (VaR) and Average Value at Risk (AVaR). The framework allows a decision-maker to synthesize divergent opinions—whether on key parameters like a VaR confidence level or on the fundamental risk model itself (e.g., VaR vs. AVaR)—into a single, coherent, and scenario-dependent assessment.
Outsiders in #bank#boards improve #riskgovernance (decrease #risktaking, increase #riskmonitoring) for #regulatory#riskmeasures but worsen risk governance for economic risk measures.
This paper explores the optimal #reinsurance design for an #insurer with multiple lines of business, where the dependence structure between #risks is unknown. The study considers Value-at-Risk (#var) and Range-Value-at-Risk (#rvar) as #riskmeasures and applies general premium principles. The optimal reinsurance strategies are obtained under budget constraint and expected profit constraint.
"We discuss different properties and representations of default #riskmeasures via monetary risk measures, families of related #tailrisk measures, and Choquet capacities. In a second step, we turn our focus on #defaultrisk measures, which are given as worst-case [#probability of #default] PDs and distorted PDs. The latter are frequently used in order to take into account model risk for the computation of #capitalrequirements through risk-weighted assets (#rwas), as demanded by the Capital Requirement #regulation (#crr). In this context, we discuss the impact of different default risk measures and margins of conservatism on the amount of risk-weighted assets."
This paper proposes a novel mixed-frequency quantile vector autoregression (MF-QVAR) model that uses a #bayesian framework and multivariate asymmetric Laplace distribution to estimate missing low-frequency variables at higher frequencies. The proposed method allows for timely policy interventions by analyzing conditional quantiles for multiple variables of interest and deriving quantile-related #riskmeasures at high frequency. The model is applied to the US economy to #nowcast conditional quantiles of #gdp, providing insight into #var, Expected Shortfall, and distance among percentiles of real GDP nowcasts.
"In the [#riskmanagement] context of #capitalallocation principles for (not necessarily coherent) #riskmeasures, we derive - under mild conditions - some representation results as ``collapse to the mean'' in a generalized sense. This approach is related to the well-known Gradient allocation and allows to extend a result of Kalkbrener (Theorem 4.3 in \cite{kalkbr05}) to a non-differentiable setting as well as to more general capital allocation rules and risk measures."
" 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."
"The potential use of the proposed risk measures in insurance is illustrated by two concrete applications, capital risk allocation and premia calculation under uncertainty."