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on Payment Systems and Financial Technology |
| By: | Coste, Charles-Enguerrand; Pantelopoulos, George |
| Abstract: | To ensure that means of payments are readily interchangeable at face value – i.e. fungible – for retail payments, three elements are required: (1) settlement finality; (2) interoperability; and (3) seamless convertibility of the means of payment into the “ultimate” or quasi-ultimate means of payment. This paper argues that stablecoins issued by different issuers on different blockchains can be fungible to the same extent as commercial bank deposits from different banks provided that (i) payment and settlement technologies are interoperable, (ii) payments are transacted on ledgers that offer settlement finality, and (iii) that central bank money acts as the anchor to the monetary system (assuming that the central bank money is itself underscored by a homogenous unit of account). On this basis, this paper asserts that tokenised funds and off-chain collateralised stablecoins are fungible means of payments under some conditions, and that on-chain collateralised stablecoins can be prima facie classified as fungible means of payments, so long as the identical preconditions associated with accomplishing means of payment fungibility for tokenised funds/off-chain collateralised stablecoins can be fulfilled, and on the premise that the on-chain collateral can be readily converted into higher level money. Finally, it is determined that algorithmic stablecoins are not fungible means of payments. JEL Classification: B26, E42 |
| Keywords: | central bank, electronic money token, fungibility, stablecoin |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253111 |
| By: | A. H. Nzokem |
| Abstract: | The cryptocurrency market presents both significant investment opportunities and higher risks relative to traditional financial assets. This study examines the tail behavior of daily returns for two leading cryptocurrencies, Bitcoin and Ethereum, using seven-parameter estimates from prior research, which applied the Generalized Tempered Stable (GTS) distribution. Quantile-quantile (Q-Q) plots against the Normal distribution reveal that both assets exhibit heavy-tailed return distributions. However, Ethereum consistently shows a greater frequency of extreme values than would be expected under its Bitcoin-modeled counterpart, indicating more pronounced tail risk. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.01983 |
| By: | ANEGUE, Jean De Dieu |
| Abstract: | The purpose of this project is to analyze the impact of mobile payments on financial inclusion in Cameroon using quasi-experimental impact evaluation methods. The results show that the use of mobile payments does increase the propensity of economic agents to hold accounts in formal financial institutions such as banks. |
| Keywords: | Banking Acces , Impact Evaluation Methods |
| JEL: | C91 O1 O12 |
| Date: | 2025–09–10 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126134 |
| By: | Craig Steven Wright |
| Abstract: | This paper presents a formal analysis of the Lightning Network as a monetary system structurally diverging from Bitcoin's base-layer settlement model. We demonstrate that under increasing transaction demand, BTC transaction fees rise superlinearly due to throughput constraints, while Lightning Network routing costs approach a bounded asymptote. Using mathematical modeling, game-theoretic proofs, and complexity analysis, we show that Lightning enables indefinite off-chain operation via the emergence of liquidity hub oligopolies. These hubs exhibit properties of unregulated financial intermediaries, including rent extraction, opacity, and systemic fragility. Strategic agent models show that channel closure becomes economically infeasible, and routing problems approach hardness limits in P-Space complexity. We conclude that Lightning does not merely extend Bitcoin, but constitutes a synthetic financial system with shadowbank characteristics, lacking reserve discipline, transparency, or enforceable settlement guarantees. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.19333 |
| By: | Gene Amromin; Kenechukwu E. Anadu; Falk Bräuning; Amy Chapel; Rebecca Chmielewski; Meeoak Cho; Patricia K. Cowperthwait; Lorenzo Garza; Cindy E. Hull; Siobhan Sanders; Sam Schulhofer-Wohl; Brett Solimine; Emma Weiss |
| Abstract: | Technology-focused Third-Party Service Providers (TPSPs) have become important players in the operations of financial institutions and the financial markets. This paper summarizes micro- and macro-prudential regulatory frameworks in place to address risks that TPSPs pose to the financial system. The key takeaways are as follows: First, in the U.S., TPSPs operate under limited comprehensive prudential regulatory oversight, aimed primarily at ensuring that their products are safe and resilient on an ongoing basis. Second, while banks rely on multiple TPSPs and hundreds of their services daily for their core banking businesses, U.S. banking supervisors have limited direct visibility into these activities and risks they may pose. Third, although the existing U.S. regulatory framework has some systemic risk considerations, there is no macroprudential structure in place for TPSP risks. Official bodies in other jurisdictions have developed macroprudential frameworks or high-level guidance to address TPSP risks, but their implementation in major economies is nascent at best. Finally, TPSPs are likely an important source of systemic vulnerability for financial institutions and financial markets, although vulnerabilities may be difficult to discern due to a need to assess the criticality of each activity performed by TPSPs and the concentration of TPSPs within that activity. |
| Keywords: | Financial stablity; third-party service providers; cyber risks |
| JEL: | G10 G23 G28 |
| Date: | 2025–06–23 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedhwp:101721 |
| By: | Craig Steven Wright |
| Abstract: | This paper integrates Austrian capital theory with repeated game theory to examine strategic miner behaviour under different institutional conditions in blockchain systems. It shows that when protocol rules are mutable, effective time preference rises, undermining rational long-term planning and cooperative equilibria. Using formal game-theoretic analysis and Austrian economic principles, the paper demonstrates how mutable protocols shift miner incentives from productive investment to political rent-seeking and influence games. The original Bitcoin protocol is interpreted as an institutional anchor: a fixed rule-set enabling calculability and low time preference. Drawing on the work of Bohm-Bawerk, Mises, and Hayek, the argument is made that protocol immutability is essential for restoring strategic coherence, entrepreneurial confidence, and sustainable network equilibrium. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.20965 |
| By: | Oscar Camacho (The Brattle Group); Michelle Garfinkel (University of California-Irvine); Constantinos Syropoulos (School of Economics, Drexel University); Yoto Yotov (School of Economics, Drexel University) |
| Abstract: | How do geopolitical frictions matter for the diffusion of technology? Based on a guns-versus-butter model involving two countries (a technology leader and a technology laggard), we study the direct and indirect effects dual-use (or general-purpose) technology transfers on the countries' payoffs and hence their preferences over such transfers. A central finding is that, when the initial technological distance between the two countries is large whereas the degree of output security is low and the laggard's capacity to absorb state-of-the-art technologies is relatively limited, the leader has an incentive to block a transfer to the laggard. The analysis also unveils the possible emergence of a "low-technology trap."" Using data on cross-border patent flows as a proxy for technology transfers and sanctions as a proxy for conflict over the 1995-2018 period, we present evidence in support of the theory. |
| Keywords: | output insecurity, arming policies, power, sanctions, low-technology trap |
| JEL: | D30 D74 F51 O33 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:drx:wpaper:202535 |
| By: | Timko, Christina; Ostrode, Nicholas; Roos, Michael W. M. |
| Abstract: | Smartphone apps deliberately apply behavioral design, including algorithms sensing human behavior and personalized design elements affecting it. Untransparent behavioral design exploits an information asymmetry between app vendors and consumers and can lead to negative effects on consumers such as diminished user autonomy or smartphone addiction. We address this topic through two main contributions. First, we present guidelines for a more consumer-friendly handling of behavioral design elements, deriving a sixstep approach for the design process of smartphone apps and its documentation that we call responsible interactive behavioral design. Second, we analyze the effects of behavioral design and of measures protecting consumers from unwanted behavioral design in a field experiment with a newsfeed reader app used by three study groups, showcasing the described design approach. In the group with behavioral design but no protection measures, participants used the app twice as long as in the group with the baseline version. Participants in the group with protection measures were most aware of being 'object' to behavioral design. Their usage time was between that of the other two groups. If it becomes adopted, the approach of responsible interactive behavioral design may contribute to viable market solutions, addressing some of the major consumer protection needs. |
| Abstract: | Smartphone-Apps setzen gezielt Behavioral Design ein, einschließlich Algorithmen, die menschliches Verhalten erkennen sowie personalisierter Designelemente, die dieses beeinflussen. Intransparentes Behavioral Design nutzt Informationsasymmetrie zwischen App-Anbietern und Konsumenten aus und kann bei Letzteren negative Effekte wie verringerte Nutzerautonomie oder Smartphone-Sucht hervorrufen. Wir adressieren dieses Thema durch zwei zentrale Beiträge. Erstens präsentieren wir Richtlinien für die nutzerfreundlichere Verwendung von Behavioral Design-Elementen und leiten einen sechsschrittigen Ansatz für das Design von Smartphone-Apps und dessen Dokumentation ab, bezeichnet als Responsible Interactive Behavioral Design. Zweitens analysieren wir die Auswirkungen von Behavioral Design und Schutzmaßnahmen für Konsumenten gegen unerwünschtes Behavioral Design in einem Feldexperiment mit einer durch drei Gruppen genutzten Newsfeed-Reader-App und führen den beschrieben Designansatz vor. In der Gruppe mit Behavioral Design, aber ohne Schutzmaßnahmen, nutzten Teilnehmende die App doppelt so lange wie in der Gruppe mit der Baseline-Version. Teilnehmende in der Gruppe mit Schutzmaßnahmen waren sich am stärksten darüber bewusst, Behavioral Design ausgesetzt zu sein. Ihre Nutzungszeit lag zwischen jener der beiden anderen Gruppen. In die Praxis aufgenommen, könnte der Ansatz des Responsible Interactive Behavioral Design zu marktfähigen Lösungen beitragen, um zentrale Verbraucherschutz-Aspekte zu adressieren. |
| Keywords: | Behavioral economics, consumer protection, field study, smartphone app, behavioral design |
| JEL: | C93 D82 D91 L86 O33 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:rwirep:325495 |
| By: | Dominika Langenmayr; Mikayel Tovmasyan; Sebastian Vosseler |
| Abstract: | Are sanctions bypassed by hiding money offshore? Using bilateral data on bank deposits, we compare how offshore deposits from sanctioned versus nonsanctioned countries develop after the U.S. and the EU impose financial sanctions. Sanctions targeting individuals increase offshore deposits, as (potential) targets attempt to hide their funds. Broader financial sanctions reduce offshore (and other foreign) deposits, as money is repatriated. A synthetic control case study of Russia following the annexation of Crimea confirms our main findings, showing a 15% post-sanction increase in offshore deposits. These findings highlight the limits of symbolic sanctions and the need for secondary sanctions and financial surveillance. |
| Keywords: | Sanctions, tax havens, illicit financial flows |
| JEL: | F51 H12 K42 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:bav:wpaper:243_langenmayr_tovmasyan_vosseler.rdf |
| By: | Ms. Emine Boz; Anja Brüggen; Camila Casas; Georgios Georgiadis; Ms. Gita Gopinath; Arnaud Mehl |
| Abstract: | This paper presents the most comprehensive and up-to-date panel dataset on global trade invoicing currency and examines recent pattern shifts with a focus on geopolitical alignment. Using data for 132 countries from 1990 to 2023—including new coverage of the Chinese renminbi—we document five key findings. First, the US dollar remains dominant, with global invoicing shares broadly stable. Second, renminbi use has grown steadily and expanded beyond Asia, though it remains modest. Third, countries not geopolitically aligned with the US continue to rely on the dollar, though this reliance has declined in a few key economies. Fourth, since 2021, the correlation between the use of a given invoicing currency and the geopolitical distance to its issuer has become more negative, reflecting growing polarization. Fifth, there is no robust evidence consistent with effective policy initiatives to reduce dollar reliance in oil exports. These findings highlight the resilience of dominant currencies and suggest emerging fragmentation in invoicing patterns along geopolitical lines. |
| Keywords: | Trade invoicing currency; dominant-currency paradigm; geopolitical alignment |
| Date: | 2025–09–12 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/178 |
| By: | Dominika Langenmayr (KU Eichstätt-Ingolstadt, WU Vienna, CESifo); Mikayel Tovmasyan (KU Eichstätt-Ingolstadt); Sebastian Vosseler (KU Eichstätt-Ingolstadt) |
| Abstract: | Are sanctions bypassed by hiding money offshore? Using bilateral data on bank deposits, we compare how offshore deposits from sanctioned versus non-sanctioned countries develop after the U.S. and the EU impose financial sanctions. Sanctions targeting individuals increase offshore deposits, as (potential) targets attempt to hide their funds. Broader financial sanctions reduce offshore (and other foreign) deposits, as money is repatriated. A synthetic control case study of Russia following the annexation of Crimea confirms our main findings, showing a 15% post-sanction increase in offshore deposits. These findings highlight the limits of symbolic sanctions and the need for secondary sanctions and financial surveillance. |
| Keywords: | Sanctions; tax havens; illicit financial flows |
| JEL: | F51 H12 K42 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:drx:wpaper:202536 |
| By: | Alexander Erlei |
| Abstract: | Generative AI is transforming the provision of expert services. This article uses a series of one-shot experiments to quantify the behavioral, welfare and distribution consequences of large language models (LLMs) on AI-AI, Human-Human, Human-AI and Human-AI-Human expert markets. Using a credence goods framework where experts have private information about the optimal service for consumers, we find that Human-Human markets generally achieve higher levels of efficiency than AI-AI and Human-AI markets through pro-social expert preferences and higher consumer trust. Notably, LLM experts still earn substantially higher surplus than human experts -- at the expense of consumer surplus - suggesting adverse incentives that may spur the harmful deployment of LLMs. Concurrently, a majority of human experts chooses to rely on LLM agents when given the opportunity in Human-AI-Human markets, especially if they have agency over the LLM's (social) objective function. Here, a large share of experts prioritizes efficiency-loving preferences over pure self-interest. Disclosing these preferences to consumers induces strong efficiency gains by marginalizing self-interested LLM experts and human experts. Consequently, Human-AI-Human markets outperform Human-Human markets under transparency rules. With obfuscation, however, efficiency gains disappear, and adverse expert incentives remain. Our results shed light on the potential opportunities and risks of disseminating LLMs in the context of expert services and raise several regulatory challenges. On the one hand, LLMs can negatively affect human trust in the presence of information asymmetries and partially crowd-out experts' other-regarding preferences through automation. On the other hand, LLMs allow experts to codify and communicate their objective function, which reduces information asymmetries and increases efficiency. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.06069 |
| By: | Noga Klein Elmalem; Rica Gonen; Erel Segal-Halevi |
| Abstract: | When allocating indivisible items, there are various ways to use monetary transfers for eliminating envy. Particularly, one can apply a balanced vector of transfer payments, or charge each agent a positive amount, or -- contrarily -- give each agent a positive amount as a ``subsidy''. In each model, one can aim to minimize the amount of payments used; this aim translates into different optimization objectives in each setting. This note compares the various models, and the relations between upper and lower bounds for these objectives. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.18794 |
| By: | Spears, Taylor C. (University of Edinburgh); Hansen, Kristian Bondo; Xu, Ruowen; Millo, Yuval |
| Abstract: | Synthetic datasets, artificially generated to mimic real-world data while maintaining anonymization, have emerged as a promising technology in the financial sector, attracting support from regulators and market participants as a solution to data privacy and scarcity challenges limiting machine learning deployment. This paper argues that synthetic data's effects on financial markets depend critically on how these technologies are embedded within existing machine learning infrastructural ``stacks'' rather than on their intrinsic properties. We identify three key tensions that will determine whether adoption proves beneficial or harmful: (1) data circulability versus opacity, particularly the "double opacity" problem arising from stacked machine learning systems, (2) model-induced scattering versus model-induced herding in market participant behaviour, and (3) flattening versus deepening of data platform power. These tensions directly correspond to core regulatory priorities around model risk management, systemic risk, and competition policy. Using financial audit as a case study, we demonstrate how these tensions interact in practice and propose governance frameworks, including a synthetic data labelling regime to preserve contextual information when datasets cross organizational boundaries. |
| Date: | 2025–09–08 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:ruxkh_v1 |
| By: | Anderson, Ronald W.; Jõeveer, Karin |
| Abstract: | We consider the determinants of pay in US banks since 1986 using a new structural model in which banking firms are matched in rank order with management teams of varying talent. We calibrate the model to data from US bank holding companies focussing on labor’s share of bank value-added, the level of bankers’ pay and its sensitivity to bank performance. We find that three changes in banking regulation have shaped bankers’ pay in the last three decades: (1) removal of obstacles to interstate banking set off a process of banking consolidation in the 1990s, (2) deregulation at the end of the 1990’s allowing banks to pursue non-interest income has driven a trend toward higher pay and higher incentive pay, (3) tougher regulations following the financial crisis imposing an implicit tax on size and complexity has moderated pay in large banks but in so-doing has allowed smaller banks to take on business outside of standard credit intermediation resulting higher pay in those banks. Taking these structural changes into account we find a tendency over three decades for a decline in labor’s share, in line with superstar effects implied by our structural model. |
| Keywords: | executive compensation; banking industry structure; rent extraction; superstar firms; regulation |
| JEL: | F3 G3 J1 |
| Date: | 2025–11–30 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:129436 |
| By: | Jayanth Athipatla |
| Abstract: | We introduce a novel rough Bergomi (rBergomi) model featuring a variance-driven exponentially weighted moving average (EWMA) time-dependent Hurst parameter $H_t$, fundamentally distinct from recent machine learning and wavelet-based approaches in the literature. Our framework pioneers a unified rough differential equation (RDE) formulation grounded in rough path theory, where the Hurst parameter dynamically adapts to evolving volatility regimes through a continuous EWMA mechanism tied to instantaneous variance. Unlike discrete model-switching or computationally intensive forecasting methods, our approach provides mathematical tractability while capturing volatility clustering and roughness bursts. We rigorously establish existence and uniqueness of solutions via rough path theory and derive martingale properties. Empirical validation on diverse asset classes including equities, cryptocurrencies, and commodities demonstrates superior performance in capturing dynamics and out-of-sample pricing accuracy. Our results show significant improvements over traditional constant-Hurst models. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.05820 |
| By: | Yang Chen; Yueheng Jiang; Zhaozhao Ma; Yuchen Cao Jacky Keung; Kun Kuang; Leilei Gan; Yiquan Wu; Fei Wu |
| Abstract: | The inherent non-stationarity of financial markets and the complexity of multi-modal information pose significant challenges to existing quantitative trading models. Traditional methods relying on fixed structures and unimodal data struggle to adapt to market regime shifts, while large language model (LLM)-driven solutions - despite their multi-modal comprehension - suffer from static strategies and homogeneous expert designs, lacking dynamic adjustment and fine-grained decision mechanisms. To address these limitations, we propose MM-DREX: a Multimodal-driven, Dynamically-Routed EXpert framework based on large language models. MM-DREX explicitly decouples market state perception from strategy execution to enable adaptive sequential decision-making in non-stationary environments. Specifically, it (1) introduces a vision-language model (VLM)-powered dynamic router that jointly analyzes candlestick chart patterns and long-term temporal features to allocate real-time expert weights; (2) designs four heterogeneous trading experts (trend, reversal, breakout, positioning) generating specialized fine-grained sub-strategies; and (3) proposes an SFT-RL hybrid training paradigm to synergistically optimize the router's market classification capability and experts' risk-adjusted decision-making. Extensive experiments on multi-modal datasets spanning stocks, futures, and cryptocurrencies demonstrate that MM-DREX significantly outperforms 15 baselines (including state-of-the-art financial LLMs and deep reinforcement learning models) across key metrics: total return, Sharpe ratio, and maximum drawdown, validating its robustness and generalization. Additionally, an interpretability module traces routing logic and expert behavior in real time, providing an audit trail for strategy transparency. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.05080 |