Nos derniers articles

This paper presents a fundamental #mathematical duality linking utility transforms and #probability distortions, which are vital in #decisionmaking under #risk. It reveals that these concepts are characterized by commutation, allowing for simple axiomatization with just one property. Additionally, rank-dependent utility transforms are further characterized under monotonicity conditions.

"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..."

This study examines interpersonal heterogeneity in #risk attitudes in #decisionmaking experiments. The use of #bayesian and classical methods for estimating the hierarchical model has sparked debate. Both approaches use the population distribution of risk attitudes to identify individual-specific risk attitudes. Comparing existing experimental data, both methods yield similar conclusions about risk attitudes.

#bayesian data imputation is a technique used to fill in missing data in a variety of fields, including #riskmanagement. By employing imputation techniques to fill in the gaps, #riskmanagers can obtain a more comprehensive and reliable understanding of the underlying #risk factors, enabling them to make informed decisions and develop effective strategies for #riskmitigation.

"#expectile is a #risk measure that, similar to #var (quantile) and CVaR (superquantile), can be employed in #riskmanagement."

"This paper introduces the multivariate range Value-at-Risk (MRVaR) and multivariate range covariance (MRCov) as #risk#measures for #riskmanagement in #regulation and investment… Frequently-used cases in industry, such as normal, student-t, logistic, Laplace, and Pearson type VII distributions, are presented with numerical examples."

This paper focuses on predicting #corporate #default #risk using frailty correlated default #models with subjective judgments. The study uses a #bayesian approach with the Particle Markov Chain #montecarlo algorithm to analyze data from #us public non-financial firms between 1980 and 2019. The findings suggest that the volatility and mean reversion of the hidden factor have a significant impact on the default intensities of the firms.

This study proposes a new approach to the analysis of #systemicrisk in #financialsystems, which is based on the #probability amount of exogenous shock that can be absorbed by the system before it deteriorates, rather than the size of the impact that exogenous events can exhibit. The authors use a linearized version of DebtRank to estimate the onset of financial distress, and compute localized and uniform exogenous shocks using spectral graph theory. They also extend their analysis to heterogeneous shocks using #montecarlo#simulations. The authors argue that their approach is more general and natural, and provides a standard way to express #failure#risk in financial systems.

"In this paper we propose efficient #bayesian Hamiltonian #montecarlo method for estimation of #systemicrisk#measures , LRMES, SRISK and ΔCoVaR, and apply it for thirty global systemically important banks and for eighteen largest #us#financialinstitutions over the period of 2000-2020. The systemic risk measures are computed based on the Dynamic Conditional Correlations model with generalized asymmetric #volatility. A policymaker may choose to rank the firms using some quantile of their systemic risk distributions such as 90, 95, or 99% depending on #risk preferences with higher quantiles being more conservative."

Effective #riskmanagement, including #operationalriskmanagement, is crucial for minimizing #financialrisks posed by #operationalrisk. Risk evaluation, which includes assessing potential risks and their #probabilities, is also vital. #bibliometric analysis using #metrics such as citations, networks, co-authorship, and region-based #publications can provide insights into the quality of #research on operational risk and identify gaps. Such analysis reveals a growing interest in the study of operational risk, but also highlights research gaps that need to be addressed for effective risk management.