Quantitative Finance

Mascolo, Federica; Bearth, Nora; Muny, Fabian; Lechner, Michael; Mareckova, Jana

doi: 10.48550/arxiv.2410.23322pmid: N/A

Abstract:Active labor market policies are widely used by the Swiss government, enrolling over half of all unemployed individuals. This paper evaluates the effectiveness of Swiss programs in improving employment and earnings outcomes using causal machine learning and rich administrative data on unemployed individuals in 2014 and 2015, including detailed labor market histories and other covariates. The findings for Swiss citizens and immigrants with permanent residency indicate a small positive average effect of a Temporary Wage Subsidy program on employment and earnings in the third year after program start. In contrast, Basic Courses, such as job application training, exhibit negative effects on both outcomes over the same period. No significant impacts are found for Employment Programs conducted outside the regular labor market or for Training Courses such as language or computer classes. The programs are most effective for individuals with a non-EU migration background, while Temporary Wage Subsidies also benefit those with lower educational attainment. Finally, shallow policy trees provide practical guidance for improving the targeting of program assignments.

Chong, Wing Fung; Liang, Gechun

doi: 10.48550/arxiv.2410.01378pmid: N/A

Abstract:This paper studies robust forward investment and consumption preferences and optimal strategies for a risk-averse and ambiguity-averse agent in an incomplete financial market with drift and volatility uncertainties. We focus on non-zero volatility and constant relative risk aversion forward preferences. Given the non-convexity of the Hamiltonian with respect to uncertain volatilities, we first construct robust randomized forward preferences through endogenous randomization in an auxiliary market. {Therein, w}e derive the corresponding optimal and robust investment and consumption strategies. Furthermore, we show that such forward preferences and strategies, developed in the auxiliary market, remain optimal and robust in the physical market, offering a comprehensive {analysis} for forward investment and consumption under model uncertainty.

Barros, Laura; Schmeißer, Aiko

doi: 10.48550/arxiv.2410.23705pmid: N/A

Abstract:Adverse economic shocks are known to reshape voter behavior -- the demand side of politics. Much less is known about their consequences for the supply side: how such shocks affect who becomes a politician. This paper examines how job losses influence individuals' decisions to enter politics and the implications for political selection. Using administrative data linking political participation records to matched employer-employee data covering all formal workers in Brazil, and exploiting mass layoffs for causal identification, we find that job loss significantly increases the likelihood of joining a political party and running for local office. Layoff-induced candidates are positively selected on various competence measures, indicating that economic shocks can improve the quality of political entrants. The increase in candidacies is strongest among laid-off individuals with greater financial incentives from holding office and higher predicted income losses. A regression discontinuity design further shows that eligibility for unemployment benefits increases political entry. These results are consistent with a reduction in individuals' opportunity costs -- both in terms of reduced private-sector income and increased time resources -- facilitating greater political engagement.

Gao, Yuan; Lee, Dokyun; Burtch, Gordon; Fazelpour, Sina

doi: 10.48550/arxiv.2410.19599pmid: N/A

Abstract:Recent studies suggest large language models (LLMs) can exhibit human-like reasoning, aligning with human behavior in economic experiments, surveys, and political discourse. This has led many to propose that LLMs can be used as surrogates or simulations for humans in social science research. However, LLMs differ fundamentally from humans, relying on probabilistic patterns, absent the embodied experiences or survival objectives that shape human cognition. We assess the reasoning depth of LLMs using the 11-20 money request game. Nearly all advanced approaches fail to replicate human behavior distributions across many models. Causes of failure are diverse and unpredictable, relating to input language, roles, and safeguarding. These results advise caution when using LLMs to study human behavior or as surrogates or simulations.

Zhu, Brian Z.; Liu, Dingyue; Wan, Xin; Liao, Gordon; Moallemi, Ciamac C.; Bachu, Brad

doi: 10.48550/arxiv.2410.19107pmid: N/A

Abstract:We study liquidity on decentralized exchanges (DEXs), identifying factors at the platform, blockchain, token pair, and liquidity pool levels with predictive power for market depth metrics. We introduce the v2 counterfactual spread metric, a novel criterion which assesses the degree of liquidity concentration in pools using the ``concentrated liquidity'' mechanism, allowing us to decompose the effect of a factor on market depth into two channels: total value locked (TVL) and concentration. We further explore how external liquidity from competing DEXs and private inventory on DEX aggregators influence market depth. We find that (i) gas prices, returns, and a DEX's share of trading volume affect liquidity through concentration, (ii) internalization of order flow by private market makers affects TVL but not the overall market depth, and (iii) volatility, fee revenue, and markout affect liquidity through both channels.

Liebau, Daniel

doi: 10.48550/arxiv.2410.14173pmid: N/A

Abstract:How will Decentralized Finance transform financial services? Using New Institutional Economics and Dynamic Capabilities Theory, I analyse survey data from 109 experts using non-parametric methods. Experts span traditional finance, DeFi industry, and academia. Four insights emerge: adoption expectations rise from negligible to 43% expecting at least high adoption by 2034; experts expect convergence scenarios over disruption, with traditional finance embracing DeFi most likely; back-office transforms before customer-facing functions; strategic competencies eclipse DeFi-sector specific- and technical skills. This challenges technology-centric adoption models. DeFi represents emerging market entry requiring organizational transformation, not just technological implementation. SEC developments validate predictions. Financial institutions should prioritize developing strategic capabilities over mere technical training.

Kato, Masahiro

doi: 10.48550/arxiv.2410.16333pmid: N/A

Abstract:This study examines portfolio selection using predictive models for portfolio returns. Portfolio selection is a fundamental task in finance, and a variety of methods have been developed to achieve this goal. For instance, the mean-variance approach constructs portfolios by balancing the trade-off between the mean and variance of asset returns, while the quantile-based approach optimizes portfolios by considering tail risk. These methods often depend on distributional information estimated from historical data using predictive models, each of which carries its own uncertainty. To address this, we propose a framework for predictive portfolio selection via conformal prediction , called \emph{Conformal Predictive Portfolio Selection} (CPPS). Our approach forecasts future portfolio returns, computes the corresponding prediction intervals, and selects the portfolio of interest based on these intervals. The framework is flexible and can accommodate a wide range of predictive models, including autoregressive (AR) models, random forests, and neural networks. We demonstrate the effectiveness of the CPPS framework by applying it to an AR model and validate its performance through empirical studies, showing that it delivers superior returns compared to simpler strategies.

Koffi, Cedric H. A.; Djeundje, Viani Biatat; Pamen, Olivier Menoukeu

doi: 10.48550/arxiv.2410.13100pmid: N/A

Abstract:We develop and evaluate a family of discrete-time logit-link (LLink) models (including fixed-effects and frailty extensions) to capture latent heterogeneity in repayment behaviour and quantify the effects of socio-temporal factors in microfinance. Our findings highlight the importance of unobserved borrower risk, revealing that simple random intercept structures are sufficient to model latent heterogeneity in this context. Additionally, socio-temporal variables--such as festive seasons and long school breaks--consistently associate with delinquency transitions, offering key insights into repayment dynamics. While LLink models provide clear interpretability, tree-based methods outperform them in predictive accuracy, making them suitable for multistate classification tasks. Building on this, we propose an optimised classification strategy based on the Matthews Correlation Coefficient to enhance next-state prediction. Overall, our results highlight the benefit of combining interpretable risk modeling with advanced machine learning to support robust, data-driven decision-making in microfinance operations.

Karagiorgis, Ariston; Ballis, Antonis; Drakos, Konstantinos; Kallandranis, Christos

doi: 10.48550/arxiv.2410.12801pmid: N/A

Abstract:We examine how skewness interacts with kurtosis within the cryptocurrency market. We show that during the COVID-19 pandemic there are more clusters of observations around the two flanks, highlighting the presence of a volatile behavior. Moreover, we document the evolvement of the interrelationship as the pandemic progresses, identifying the domination of the extremes. Our findings advance the thinking that by exploiting the interrelationship between the two higher moments of cryptocurrencies, investors and researchers can have in their arsenal an additional analytic tool.

Zhou, Hao; Dang, Duy-Minh

doi: 10.48550/arxiv.2410.04745pmid: N/A

Abstract:This paper addresses an important gap in rigorous numerical treatments for pricing American options under correlated two-asset jump-diffusion models using the viscosity solution framework, with a particular focus on the Merton model. The pricing of these options is governed by complex two-dimensional (2-D) variational inequalities that incorporate cross-derivative terms and nonlocal integro-differential terms due to the presence of jumps. Existing numerical methods, primarily based on finite differences, often struggle with preserving monotonicity in the approximation of cross-derivatives, a key requirement for ensuring convergence to the viscosity solution. In addition, these methods face challenges in accurately discretizing 2-D jump integrals. We introduce a novel approach to effectively tackle the aforementioned variational inequalities while seamlessly handling cross-derivative terms and nonlocal integro-differential terms through an efficient and straightforward-to-implement monotone integration scheme. Within each timestep, our approach explicitly enforces the inequality constraint, resulting in a 2-D Partial Integro-Differential Equation (PIDE) to solve. Its solution is expressed as a 2-D convolution integral involving the Green's function of the PIDE. We derive an infinite series representation of this Green's function, where each term is non-negative and computable. This facilitates the numerical approximation of the PIDE solution through a monotone integration method. To enhance efficiency, we develop an implementation of this monotone scheme via FFTs, exploiting the Toeplitz matrix structure. The proposed method is proved to be both $\ell_{\infty} $-stable and consistent in the viscosity sense, ensuring its convergence to the viscosity solution of the variational inequality. Extensive numerical results validate the effectiveness and robustness of our approach.