"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 analyzes the relationship between the #baseliii countercyclical #capitalbuffer (#CCyB) and the #liquidity coverage #ratio (#lcr) requirement. The study shows that banks face a risk-liquidity trade-off with the LCR, affecting the CCyB required level to dampen cyclicality in #bank actual #capitalratios.
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.
"#annuities, #longtermcareinsurance and #reversemortgages remain unpopular to manage #longevity, medical and housing price #risks after #retirement. We analyze low demand using a life-cycle model structurally estimated with a unique stated-preference survey experiment of #canadian households."
We define the degree of #banking integration in the #eurozone through different phases of the #economic cycle, from 2006 to 2020, with #complexnetworks and #clusteralgorithms … Regarding the nodes of the network, #germany yields the position of centrality in favor of #france.
The #covid19 #pandemic revealed shortcomings in #supplychainmanagement and highlighted the need for rebuilding #supplychains for #resilience to respond to #systemwide #disruptions. This study outlines an approach to rebuilding supply chains for resilience, integrating innovation in areas critical to supply chain management. The authors focus on three areas deemed foundational to #supplychainresilience: forecasting, #supplychainrisk #riskmanagement, and product design.
The #covid19 #pandemic challenged every aspect of business and forced organizations to shift into #crisismode. The pandemic re-exposed issues associated with #siloedthinking in #riskmanagement. For organizations with inadequate #erm policies, plans, or procedures, this is a crucial time to reflect on improving their ERM processes through the capture and transfer of Covid-related lessons. This study explores how concept #riskmapping can be a valuable tool to structure lessons learned capture, ensure risk information is considered, and focus on ERM practice improvements.
This study examines the use of #artificialintelligence (#ai) and #bigdata data analytics by #insurers in #belgium for segmentation purposes to determine #claims#probability for prospective policyholders. The implementation of AI and big data analytics can benefit insurers by increasing the accuracy of #riskassessment. However, pervasive segmentation can have negative implications and potentially harm policyholders if their risk is incorrectly calculated. Existing restrictions in #insurance#regulations fall short of protecting policyholders from inaccuracies in risk assessments, potentially resulting in incorrect #premiums or conditions.
"We assess the ability of #GPT … to serve as a financial robo-advisor for the masses, by combining a financial literacy test and an advice-utilization task (the Judge-Advisor System). #davinci and #chatgpt (variants of GPT) score 58% and 67% on the #financialliteracy literacy test, respectively, compared to a baseline of 31%. However, people overestimated GPT's performance (79.3%), and in a savings dilemma, they relied heavily on advice from GPT (WOA = 0.65). Lower subjective financial knowledge increased advice-taking. We discuss the risk of overreliance on current large #languagemodels models and how their utility to laypeople may change."
"We consider two possible approaches to the problem of incorporating explicit general (i.e. economic) #inflation in the #non_life [#insurance] #claims reserve estimates and in corresponding reserve SCR, defined - as in #solvencyii - under the one year view. The #actuarial approach provides a simplified solution to the problem, obtained under the assumption of deterministic #interestrates and absence of inflation risk premia."