“While the main discussion of the paper is tailored to the management of systemic cyber risk in digital networks, we also draw parallels to similar risk management frameworks for other types of complex systems.”
The study explores the link between capital ratios and bank portfolio choices during financial strain using a unique approach. By treating portfolio adjustments as discrete decisions and comparing expected correlations to Bayesian model estimates, it identifies primary factors guiding banks' responses. Analyzing US commercial banks during the 1990s Credit Crunch, it suggests that while Basel Accord's risk-based capital requirements weren't the primary driver, banks likely responded to capital shocks, navigating constraints from leverage ratio requirements, and reacting to economic conditions.
The Three Lines of Defence model (based on defence-in-depth approaches) has become one of the primary risk management frameworks. Yet, its application in the cybersecurity space, one of the fastest-growing areas of risk for modern organisations, has been fragmented at best. In this article, we conducted a systematic literature review on the application of this model in cybersecurity.
A new copula class, Principal Component Copulas, merges copula-based methods with principal component models. It excels in modeling tail dependence in multivariate data by leveraging key directions. These copulas resemble factor copulas but exhibit distinct technicalities. They offer advantages in complex dependency modeling, especially in high dimensions, as demonstrated in simulations and applied to return data. Notably, they mitigate dimensionality issues in large models and excel in assessing tail risk, crucial for capital modeling.
The study designs optimal reinsurance contracts maximizing insurer dividends under budget and solvency constraints. Dynamic scenarios simplify to static problems. Tailored dividend rules add complexity, solved through infinite-dimensional Lagrangian problems. Multi-layer contracts, determined by Lagrangian multipliers, are approximated using a linear programming algorithm for practical application in reinsurance design.
The study investigates how opting for cyber insurance impacts firms' risk management. It reveals that while cyber insurance often decreases proactive risk prevention (ex-ante moral hazard), it enhances post-breach mitigation efforts, improving outcomes. The key lies in contract design balancing breach coverage and co-insurance rates, emphasizing the need for a robust risk mitigation market in cybersecurity management.
This article explores AI's societal risks, including harm to communities, security threats, and existential risks. It proposes a framework for systemic AI regulation, advocating a precautionary approach focusing on technology rather than specific applications. The article suggests principles for cohesive regulation, including oversight and diverse regulatory strategies.
The book divides into four parts. Part I introduces machine learning in finance, tracing its history. Part II covers practical aspects like model implementation, laden with formulas. Part III details supervised, unsupervised, and reinforcement learning in asset management with case studies. Part IV tackles ethics, regulations, risk, and future trends, aiming for a holistic understanding.
In 1921, Keynes and Knight stressed the distinction between uncertainty and risk. While risk involves calculable probabilities, uncertainty lacks a scientific basis for probabilities. Knightian uncertainty exists when outcomes can't be assigned probabilities. This poses challenges in decision-making and regulation, especially in scenarios like AI, urging caution for eliminating worst-case scenarios due to potential high costs and missed benefits.
The paper addresses challenges in risk assessment from limited, non-stationary historical data and heavy-tailed distributions. It introduces a novel method for scaling risk estimators, ensuring robustness and conservative risk assessment. This approach extends time scaling beyond conventional methods, facilitates risk transfers, and enables unbiased estimation in small sample settings. Demonstrated through value-at-risk and expected shortfall estimation examples, the method's effectiveness is supported by an empirical study showcasing its impact.