Quantitative Finance

Lee, Tae-Hwy; Wang, Tao

doi: 10.48550/arxiv.2309.09481pmid: N/A

Abstract:We in this paper utilize P-GMM (Cheng and Liao, 2015) moment selection procedure to select valid and relevant moments for estimating and testing forecast rationality under the flexible loss proposed by Elliott et al. (2005). We motivate the moment selection in a large dimensional setting, explain the fundamental mechanism of P-GMM moment selection procedure, and elucidate how to implement it in the context of forecast rationality by allowing the existence of potentially invalid moment conditions. A set of Monte Carlo simulations is conducted to examine the finite sample performance of P-GMM estimation in integrating the information available in instruments into both the estimation and testing, and a real data analysis using data from the Survey of Professional Forecasters issued by the Federal Reserve Bank of Philadelphia is presented to further illustrate the practical value of the suggested methodology. The results indicate that the P-GMM post-selection estimator of forecaster's attitude is comparable to the oracle estimator by using the available information efficiently. The accompanying power of rationality and symmetry tests utilizing P-GMM estimation would be substantially increased through reducing the influence of uninformative instruments. When a forecast user estimates and tests for rationality of forecasts that have been produced by others such as Greenbook, P-GMM moment selection procedure can assist in achieving consistent and more efficient outcomes.

Wang, Ying-Li; Xu, Cheng-Long; He, Ping

doi: 10.48550/arxiv.2309.08175pmid: N/A

Abstract:In this paper, we introduce a data-driven, single-parameter Markov diffusion model for the VIX. The volatility factor evolves in $(-1,1)$ with a uniform invariant distribution ensured by Legendre polynomials, mapped to the empirical distribution. We derive analytical series solutions for VIX futures and options using separation of variables to solve the Feynman-Kac PDE. Compared to the 3/2 model, our approach offers equal or superior accuracy and flexibility, providing an efficient, robust alternative for VIX pricing and risk management. Code and data are available at this http URL.

Jensen, Rasmus Ingemann Tuffveson; Ferwerda, Joras; Wewer, Christian Remi

doi: 10.48550/arxiv.2309.12704pmid: N/A

Abstract:Objectives: To combat money laundering, banks raise and review alerts on transactions that exceed confidential thresholds. However, the thresholds may be leaked to criminals, allowing them to break up large transactions into amounts under the thresholds. This paper introduces a data-driven approach to detect the phenomenon, popularly known as smurfing. Methods: Our approach compares an observed transaction distribution to a counterfactual distribution estimated using a high-degree polynomial. We investigate the approach with simulation experiments and real transaction data from a systemically important Danish bank. Results: Our simulation experiments suggest that the approach can detect smurfing when as little as 0.1-0.5% of all transactions are subject to smurfing. On the real transaction data, we find no evidence of smurfing and, thus, no evidence of leaked thresholds. Conclusions: Our approach may be used to test if transaction thresholds have been leaked. This has practical implications for criminal justice and anti-money laundering (AML) systems. If criminals gain knowledge of AML alert thresholds, the effectiveness of the systems may be undermined. An implementation of our approach is available online, providing a free and easy-to-use tool for banks and financial supervisors. The null result obtained on our real data helps raise confidence in (though it cannot prove the effectiveness of) anti-money laundering efforts.

Lee, Mingyu; Shin, Myeongjin; Lee, Junseo; Jeong, Kabgyun

doi: 10.1038/s41598-025-18476-ypmid: 40999027

Abstract:One of the most promising applications in the era of Noisy Intermediate-Scale Quantum (NISQ) computing is quantum generative adversarial networks (QGANs), which offer significant quantum advantages over classical machine learning in various domains. However, QGANs suffer from mode collapse and lack explicit control over the features of generated outputs. To overcome these limitations, we propose InfoQGAN, a novel quantum-classical hybrid generative adversarial network that integrates the principles of InfoGAN with a QGAN architecture. Our approach employs a variational quantum circuit for data generation, a classical discriminator, and a Mutual Information Neural Estimator (MINE) to explicitly optimize the mutual information between latent codes and generated samples. Numerical simulations on synthetic 2D distributions and Iris dataset augmentation demonstrate that InfoQGAN effectively mitigates mode collapse while achieving robust feature disentanglement in the quantum generator. By leveraging these advantages, InfoQGAN not only enhances training stability but also improves data augmentation performance through controlled feature generation. These results highlight the potential of InfoQGAN as a foundational approach for advancing quantum generative modeling in the NISQ era.

Chen, Wayne; Chen, Songwei; Rozwood, Preston

doi: 10.48550/arxiv.2309.00632pmid: N/A

Abstract:Current approaches to the cryptocurrency automated market makers result in poor impermanent loss and capital efficiency. We analyze the mechanics underlying DODO Exchange's proactive market maker (PMM) to probe for solutions to these issues, leading to our key insight of multi-token trading pools. We explore this paradigm primarily through the construction of a generalization of PMM, the multi-token token proactive market maker (MPMM). We show via simulations that MPMM has better impermanent loss and capital efficiency than comparable market makers under a variety of market scenarios. We also test multi-token generalizations of other common 2-token pool market makers. Overall, this work demonstrates several advantages of multi-token pools and introduces a novel multi-token pool market maker.

Das, Bikramjit; Fasen-Hartmann, Vicky

doi: 10.48550/arxiv.2309.15511pmid: N/A

Abstract:The stability of a complex financial system may be assessed by measuring risk contagion between various financial institutions with relatively high exposure. We consider a financial network model using a bipartite graph of financial institutions (e.g., banks, investment companies, insurance firms) on one side and financial assets on the other. Following empirical evidence, returns from such risky assets are modeled by heavy-tailed distributions, whereas their joint dependence is characterized by copula models exhibiting a variety of tail dependence behavior. We consider CoVaR, a popular measure of risk contagion and study its asymptotic behavior under broad model assumptions. We further propose the Extreme CoVaR Index (ECI) for capturing the strength of risk contagion between risk entities in such networks, which is particularly useful for models exhibiting asymptotic independence. The results are illustrated by providing precise expressions of CoVaR and ECI when the dependence of the assets is modeled using two well-known multivariate dependence structures: the Gaussian copula and the Marshall-Olkin copula.