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.
The paper explores convex risk measures with weak optimal transport penalties, demonstrating explicit representations via nonlinear transformations of loss functions. It delves into computational aspects, discussing approximations using neural networks and applies these concepts to diverse examples. Finally, it demonstrates practical applications in insurance and finance for worst-case losses and no-arbitrage pricing beyond quoted maturities.
Amid a surge in corporate social responsibility (CSR) communication, this study delves into the prevalence of symbolic CSR actions versus substantive efforts. Focusing on US-listed firms, it links CSR decoupling with heightened financial fraud risks. Factors like governance, audit quality, and ownership concentration amplify this vulnerability, emphasizing caution for stakeholders and regulators when assessing CSR claims.
This study explores cyber risk in businesses, suggesting cybersecurity investment and insurance as key strategies. Using a network model, it examines firms' interconnected decisions, defining a Nash equilibrium where firms optimize cybersecurity and insurance. Findings highlight their interdependence and how network structures affect choices, reinforced by numerical analyses.