The paper introduces a new robust estimation technique, the Method of Truncated Moments (MTuM), tailored for estimating the tail index of a Pareto distribution from grouped data. It addresses limitations in existing methods for grouped loss severity data, providing inferential justification through the central limit theorem and simulation studies.
“This paper explores the factors that impact how experts perceive the risk of money laundering during Anti-Money Laundering (AML) risk assessments. To achieve this, we utilized two different exploratory methods... The study’s results suggest that experts heavily rely on their organization’s risk response and are often influenced by preconceived notions or fear.”
"We envision a polycrisis as a state in which multiple, macroregional, ecologically embedded, and inexorably interconnected systems face high – and advancing – risk across socioeconomic, political, and other dimensions. We differentiate the term from others widely used, such as cascading disasters, compound disasters, and recurring acute disasters."
Open banking creates diverse models: competitive and monopolistic banks. Policy changes impacting relative profitability lead banks to shift types. Increased capital requirements favor competitive banks, potentially raising system risk. Deposit rate ceilings can increase risk by promoting growth in the riskier competitive sector. Introducing a shadow banking sector benefits monopolistic banks, reducing overall system risk.
This paper explores risk factor distribution forecasting in finance, focusing on the widely used Historical Simulation (HS) model. It applies various deep generative methods for conditional time series generation and proposes new techniques. Evaluation metrics cover distribution distance, autocorrelation, and backtesting. The study reveals HS, GARCH, and CWGAN as top-performing models, with potential future research directions discussed.
Managing cyber risk in the supply chain is a major challenge in cybersecurity. Organizations struggle to evaluate suppliers' security postures, while suppliers face challenges in communicating these postures. This study, using interviews and surveys, formulates a process theory for supplier cyber risk assessment, highlighting the importance of secure technology. The findings provide actionable insights for improving supply chain cyber risk management.
Optimizing cybersecurity involves understanding it as an organizational concern with varying stakeholder perspectives. Instead of viewing it as a standalone issue, decision-makers should align security measures with business goals. This paper proposes a model considering organizational priorities, translating them into a utility function for evaluating security controls, and finding an optimal balance between risk, cost, and benefit.
“… the report underscores the critical role of emerging governance, risk, and compliance frameworks in ensuring organizations remain adaptive and resilient in the face of ever-evolving cyber threats. In an era where digital risks are continuously evolving...”
The article advocates for a shift in cyber risk assessment from a threat-centric to a harm-centric approach. Current models often neglect qualitative and cascading impacts of cyber incidents. The proposed Cyber Harm Model (CHM) aims to address this gap, providing a comprehensive framework for assessing and mitigating harm, using empirical data from Critical Information Infrastructures.
This research develops a mathematical model using Extreme Value Theory and Risk Measures to estimate and forecast major fire insurance claims, enhancing insurers' understanding of potential risks. Utilizing a three-parameter Generalized Pareto Distribution in the Extreme Value Theory framework, the study effectively models large losses, aiding in risk management and pricing strategies for insurance firms.