How Crime Shapes Insurance and Insurance Shapes Crime

Examines the relationship between #crime and #insurance, with a focus on the role of #governance, #riskassessment and #riskmanagement, #crimeprevention, #securitytechnology, #behavioraleconomics, #theft, #kidnap and #hijack for ransom, #fraud, and #ransomware. It analyzes five case studies to identify a co-evolutionary process in which #insurers collaborate with insureds, governments, and #thirdparty to #mitigaterisk, particularly when criminal innovations destabilize the #insurancemarket.

Optimal Reinsurance with Multivariate Risks and Dependence Uncertainty

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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.

An axiomatic approach to default risk and model uncertainty in rating systems

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"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."

Bank Countercyclical Capital Buffer Under the Liquidity Coverage Ratio Regulation

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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.

Bayesian Mixed‑Frequency Quantile Vector Autoregression: Eliciting Tail Risks of Monthly Us GDP

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.

From Supply Chain Risk to Systemwide Disruptions

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.

Capturing ERM Lessons Learned from the Covid -19 Pandemic through Concept Mapping

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.