New articles on Quantitative Finance


[1] 2405.17682

Managing Financial Climate Risk in Banking Services: A Review of Current Practices and the Challenges Ahead

The document discusses the financial climate risk in the context of the banking industry, emphasizing the need for a comprehensive understanding of climate change across different spatial and temporal scales. It highlights the challenges in estimating physical and transition risks, specifically extreme events and limitations of current climate models. The document also reviews current gaps in assessing physical and transition risks, including the development, improvement of modeling frameworks, highlighting the need for detailed databases of exposed physical assets and climatic hazard modeling. It also emphasizes the importance of integrating financial climate risks into financial risk management practices, particularly in smaller banks and lending organizations.


[2] 2405.17762

Tuition too high? Blame competition

We develop a feedback theory that includes reinforcing and balancing feedback effects that emerge when colleges compete for reputation, applicants, and tuition revenue. The feedback theory is replicated in a formal duopoly model consisting of two competing colleges. An independent ranking entity determines the relative order of the colleges. College applicants choose between the two colleges based on the rankings and the financial aid offered by the colleges. Contrary to the conventional wisdom that competition lowers prices and benefits consumers, our simulations show that competition between academic institutions for resources and reputation leads to tuition escalation that negatively affects students and their families. Four of the five scenarios -- rankings, a capital campaign, facilities improvements, and an excellence campaign -- increase college tuition, institutional debt, and expenditures per student; only the scenario of ignoring the rankings decreases these measures. By referring to the feedback structure of academic competition, the article makes several recommendations for controlling tuition inflation. This article contributes to the literature on the economics of higher education and illustrates the value of feedback economics in developing economic theory.


[3] 2405.17770

Risk-Neutral Generative Networks

We present a functional generative approach to extract risk-neutral densities from market prices of options. Specifically, we model the log-returns on the time-to-maturity continuum as a stochastic curve driven by standard normal. We then use neural nets to represent the term structures of the location, the scale, and the higher-order moments, and impose stringent conditions on the learning process to ensure the neural net-based curve representation is free of static arbitrage. This specification is structurally clear in that it separates the modeling of randomness from the modeling of the term structures of the parameters. It is data adaptive in that we use neural nets to represent the shape of the stochastic curve. It is also generative in that the functional form of the stochastic curve, although parameterized by neural nets, is an explicit and deterministic function of the standard normal. This explicitness allows for the efficient generation of samples to price options across strikes and maturities, without compromising data adaptability. We have validated the effectiveness of this approach by benchmarking it against a comprehensive set of baseline models. Experiments show that the extracted risk-neutral densities accommodate a diverse range of shapes. Its accuracy significantly outperforms the extensive set of baseline models--including three parametric models and nine stochastic process models--in terms of accuracy and stability. The success of this approach is attributed to its capacity to offer flexible term structures for risk-neutral skewness and kurtosis.


[4] 2405.17841

Constrained monotone mean--variance investment-reinsurance under the Cramér--Lundberg model with random coefficients

This paper studies an optimal investment-reinsurance problem for an insurer (she) under the Cram\'er--Lundberg model with monotone mean--variance (MMV) criterion. At any time, the insurer can purchase reinsurance (or acquire new business) and invest in a security market consisting of a risk-free asset and multiple risky assets whose excess return rate and volatility rate are allowed to be random. The trading strategy is subject to a general convex cone constraint, encompassing no-shorting constraint as a special case. The optimal investment-reinsurance strategy and optimal value for the MMV problem are deduced by solving certain backward stochastic differential equations with jumps. In the literature, it is known that models with MMV criterion and mean--variance criterion lead to the same optimal strategy and optimal value when the wealth process is continuous. Our result shows that the conclusion remains true even if the wealth process has compensated Poisson jumps and the market coefficients are random.


[5] 2405.17753

Regression Equilibrium in Electricity Markets

Renewable power producers participating in electricity markets build forecasting models independently, relying on their own data, model and feature preferences. In this paper, we argue that in renewable-dominated markets, such an uncoordinated approach to forecasting results in substantial opportunity costs for stochastic producers and additional operating costs for the power system. As a solution, we introduce Regression Equilibrium--a welfare-optimal state of electricity markets under uncertainty, where profit-seeking stochastic producers do not benefit by unilaterally deviating from their equilibrium forecast models. While the regression equilibrium maximizes the private welfare, i.e., the average profit of stochastic producers across the day-ahead and real-time markets, it also aligns with the socially optimal, least-cost dispatch solution for the system. We base the equilibrium analysis on the theory of variational inequalities, providing results on the existence and uniqueness of regression equilibrium in energy-only markets. We also devise two methods for computing the regression equilibrium: centralized optimization and a decentralized ADMM-based algorithm that preserves the privacy of regression datasets.


[6] 2405.17924

Generative AI Enhances Team Performance and Reduces Need for Traditional Teams

Recent advancements in generative artificial intelligence (AI) have transformed collaborative work processes, yet the impact on team performance remains underexplored. Here we examine the role of generative AI in enhancing or replacing traditional team dynamics using a randomized controlled experiment with 435 participants across 122 teams. We show that teams augmented with generative AI significantly outperformed those relying solely on human collaboration across various performance measures. Interestingly, teams with multiple AIs did not exhibit further gains, indicating diminishing returns with increased AI integration. Our analysis suggests that centralized AI usage by a few team members is more effective than distributed engagement. Additionally, individual-AI pairs matched the performance of conventional teams, suggesting a reduced need for traditional team structures in some contexts. However, despite this capability, individual-AI pairs still fell short of the performance levels achieved by AI-assisted teams. These findings underscore that while generative AI can replace some traditional team functions, more comprehensively integrating AI within team structures provides superior benefits, enhancing overall effectiveness beyond individual efforts.