|
on Payment Systems and Financial Technology |
| By: | José Ramón Martínez Resano (BANCO DE ESPAÑA) |
| Abstract: | This paper explores the financial stability nexus within a monetary ecosystem that has been expanded to include a central bank digital currency (CBDC). The paper examines the new risks associated with the introduction of a CBDC, their mitigants and their potential amplification factors. Economists and academics still seem to be split on the validity of the traditional principle of separating money into two tiers of public and private money, as a structural mitigant of the risks of deposit substitution and banking disintermediation towards CBDCs. The potential amplification of the risks associated with CBDCs through credit-related second-round effects is an additional concern. The systematic study of the risks and mitigants carried out in the paper highlights the importance of partially adapting the two-tier system of money by implementing certain limits, as envisaged in CBDC plans. The endogenous mitigation of the risks through improved bank competition often attributed to CBDCs is uncertain and may be insufficient from a systemic risk perspective. The introduction of exogenous mitigants, like CBDC holding limits calibrated on the basis of a robust methodology, seems instrumental to ensure the consistency of a monetary ecosystem that includes a CBDC. Hence, the paper addresses some fundamental methodological issues related to these limits, such as the rationale for alternative targets for the limits, the influence of disintermediation speed, the time horizons involved in the limitation and adaptation process, and the role of regulatory and market frictions. An illustrative empirical analysis for the Spanish case indicates that financial stability might not be a concern for reasonable levels of CBDC take-up, although the complexity and novelty of this instrument call for a more in-depth analysis in the future. |
| Keywords: | central bank digital currency, digital money, payments, financial stability |
| JEL: | E41 E42 E51 E52 E58 G21 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:bde:opaper:2436 |
| By: | Rahul Arulkumaran; Suyash Kumar; Shikha Tomar; Manideep Gongalla; Harshitha |
| Abstract: | Cryptocurrencies are highly volatile financial instruments with more and more new retail investors joining the scene with each passing day. Bitcoin has always proved to determine in which way the rest of the cryptocurrency market is headed towards. As of today Bitcoin has a market dominance of close to 50 percent. Bull and bear phases in cryptocurrencies are determined based on the performance of Bitcoin over the 50 Day and 200 Day Moving Averages. The aim of this paper is to foretell the performance of bitcoin in the near future by employing predictive algorithms. This predicted data will then be used to calculate the 50 Day and 200 Day Moving Averages and subsequently plotted to establish the potential bull and bear phases. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13586 |
| By: | Mabsur Fatin Bin Hossain; Lubna Zahan Lamia; Md Mahmudur Rahman; Md Mosaddek Khan |
| Abstract: | Time series forecasting is a key tool in financial markets, helping to predict asset prices and guide investment decisions. In highly volatile markets, such as cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), forecasting becomes more difficult due to extreme price fluctuations driven by market sentiment, technological changes, and regulatory shifts. Traditionally, forecasting relied on statistical methods, but as markets became more complex, deep learning models like LSTM, Bi-LSTM, and the newer FinBERT-LSTM emerged to capture intricate patterns. Building upon recent advancements and addressing the volatility inherent in cryptocurrency markets, we propose a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) networks with FinBERT to enhance forecasting accuracy for these assets. This approach fills a key gap in forecasting volatile financial markets by blending advanced time series models with sentiment analysis, offering valuable insights for investors and analysts navigating unpredictable markets. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.12748 |
| By: | Fritz, Benedikt; Krüger, Ulrich; Wong, Lui Hsian |
| Abstract: | We examine the impact of introducing a digital euro, as currently conceptualized in the proposal by the European Commission, on the liquidity situation of banks in Germany. The analyses are the basis for assessing the effects of a digital euro on banks' liquidity, as presented in the 11th Annual Report of the German Financial Stability Committee. This paper extensively addresses the technical details of the analyses and substantiates the robustness of the discussed findings. Our analysis focuses on short-term effects. In this environment, deposits are swiftly withdrawn and converted into digital euros, leaving banks with limited opportunities to adapt. We consider a scenario where users fully utilize the holding limit of the digital euro, along with additional scenarios that account for risk-mitigating factors. We employ a unique dataset that combines banking supervisory data with payment transaction information. Our analysis demonstrates that particular savings banks and cooperative banks are vulnerable to retail deposit outflows from exchanges into digital euro. However, only few banks would experience a liquidity shortfall if liquidity in the form of high-quality liquid assets could be redistributed within the banking associations (liquidity balancing). Furthermore, our analysis indicates that based on a holding limit of €3, 000 the liquidity shortfall based on the Liquidity Coverage Ratio remains relatively small in aggregate compared to the level of high-quality liquid assets of the entire banking system in all scenarios (up to 2%). |
| Keywords: | Central bank digital currency, holding limits, bank liquidity, systemic risk |
| JEL: | G21 G32 G38 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bubtps:307139 |
| By: | Ling Cheng; Qian Shao; Fengzhu Zeng; Feida Zhu |
| Abstract: | Since its advent in 2009, Bitcoin (BTC) has garnered increasing attention from both academia and industry. However, due to the massive transaction volume, no systematic study has quantitatively measured the asset decentralization degree specifically from a network perspective. In this paper, by conducting a thorough analysis of the BTC transaction network, we first address the significant gap in the availability of full-history BTC graph and network property dataset, which spans over 15 years from the genesis block (1st March, 2009) to the 845651-th block (29, May 2024). We then present the first systematic investigation to profile BTC's asset decentralization and design several decentralization degrees for quantification. Through extensive experiments, we emphasize the significant role of network properties and our network-based decentralization degree in enhancing Bitcoin analysis. Our findings demonstrate the importance of our comprehensive dataset and analysis in advancing research on Bitcoin's transaction dynamics and decentralization, providing valuable insights into the network's structure and its implications. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13603 |
| By: | Lars Fluri; A. Ege Yilmaz; Denis Bieri; Thomas Ankenbrand; Aurelio Perucca |
| Abstract: | This research investigates liquidity dynamics in fractional ownership markets, focusing on illiquid alternative investments traded on a FinTech platform. By leveraging empirical data and employing agent-based modeling (ABM), the study simulates trading behaviors in sell offer-driven systems, providing a foundation for generating insights into how different market structures influence liquidity. The ABM-based simulation model provides a data augmentation environment which allows for the exploration of diverse trading architectures and rules, offering an alternative to direct experimentation. This approach bridges academic theory and practical application, supported by collaboration with industry and Swiss federal funding. The paper lays the foundation for planned extensions, including the identification of a liquidity-maximizing trading environment and the design of a market maker, by simulating the current functioning of the investment platform using an ABM specified with empirical data. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13381 |
| By: | Yuval Boneh; Ethan Jones |
| Abstract: | This paper delves into the spectrum of credit risks associated with decentralized stablecoin issuance, ranging from overcollateralized lending to business-to-business credit. It examines the mechanisms, risks, and mitigation strategies at each layer, highlighting the potential for scaling decentralized stablecoins while ensuring systemic health. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13762 |
| By: | Fayssal Jamhamed (Systematic Equity Fund Manager, Arkéa Investment Services, CREM – UMR6211); Franck Martin (Univ Rennes, CNRS, CREM – UMR6211, F-35000 Rennes, France); Fabien Rondeau (Univ Rennes, CNRS, CREM – UMR6211, F-35000 Rennes, France); Josué Thélissaint (Univ Rennes, CNRS, CREM – UMR6211, F-35000 Rennes, France); Stéphane Tufféry (Crédit Mutuel CIC) |
| Abstract: | This paper addresses market efficiency of cryptocurrencies. We investigate predictability of daily returns and strive to uncover the underlying dynamics. Four major cryptocurrencies are considered for their representativeness of the market: Bitcoin, Ethereum, Binance Coin and Litecoin. A Gaussian Mixture Modeling (GMM) is applied as framework in a two-step process. The first step targets the clustering of returns while the second focuses on regime-specific dynamics of returns. The ensemble aims to capture nonlinearity and to assess asymmetric behavior. On purpose we use macro-financial variables, coin-specific and global market sentiment indicators. We find significant predictability in terms of conditional mean prediction, trend prediction and market regime prediction. Moreover, economic value of forecasts for these four coins shows evidence of counterarguments to the Efficient Market Hypothesis (EMH). Our findings provide insights for profitable investment strategies and enable a better understanding of returns dynamics. The results are robust enough to motivate active strategies and replication on larger panel of cryptocurrencies. Simultaneously, evidence highlights new issues that necessitate further investigation into the observed asymmetrie |
| Keywords: | Cryptocurrency market, Efficient Market Hypothesis, Gaussian Mixture Modeling, Penalized Linear Regression, Market Prediction |
| JEL: | G14 G17 C15 C58 C53 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:tut:cremwp:2024-13 |
| By: | Qi Deng; Zhong-Guo Zhou |
| Abstract: | Wash trading among crypto assets induces short-term price jumps, which manifest as liquidity fluctuation. We develop a model to decompose asset liquidity into two components: liquidity jump and liquidity diffusion, which quantify the size and probability of wash trading. Using the trading data from US stock markets as a benchmark, we establish that the combination of high liquidity diffusion and high liquidity jump indicates wash trading. On the other hand, the majority of large-volume trades with high liquidity jump but low liquidity diffusion ( |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05803 |
| By: | Shuochen Bi; Tingting Deng; Jue Xiao |
| Abstract: | The outlook for the future of artificial intelligence (AI) in the financial sector, especially in financial forecasting, the challenges and implications. The dynamics of AI technology, including deep learning, reinforcement learning, and integration with blockchAIn and the Internet of Things, also highlight the continued improvement in data processing capabilities. Explore how AI is reshaping financial services with precisely tAIlored services that can more precisely meet the diverse needs of individual investors. The integration of AI challenges regulatory and ethical issues in the financial sector, as well as the implications for data privacy protection. Analyze the limitations of current AI technology in financial forecasting and its potential impact on the future financial industry landscape, including changes in the job market, the emergence of new financial institutions, and user interface innovations. Emphasizing the importance of increasing investor understanding and awareness of AI and looking ahead to future trends in AI tools for user experience to drive wider adoption of AI in financial decision making. The huge potential, challenges, and future directions of AI in the financial sector highlight the critical role of AI technology in driving transformation and innovation in the financial sector |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13562 |
| By: | Aurel Ruben Mäder; Dr. Matthias Jüttner; Dr. Daniel Gatica-Perez |
| Abstract: | Understanding the payment behavior of sociodemographic groups is important for public institutions in designing inclusive policies. Thus, public institutions regularly conduct payment surveys to monitor the payment behavior of these groups. However, such surveys are costly, conducted infrequently, and limited in the number of participants. This paper presents a methodology that enables policy-makers to monitor the payment behavior of sociodemographic groups with card data while complying with privacy rights. Specifically, it provides a correlational analysis of payment behavior across sociodemographic groups, demonstrates the potential of payment data to infer sociodemographic information and proposes a methodology for enriching card data with this information. This paper reveals that sociodemographic groups exhibit different payment behaviors, that groups can be inferred from payment data, and that anonymized card data can be enriched with sociodemographic information. |
| Keywords: | Payment behavior, Sociodemographics, Inference, Card data, Payment surveys |
| JEL: | C55 C83 D12 E42 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:snb:snbwpa:2024-13 |
| By: | Suyash Rai (xKDR Forum) |
| Abstract: | The digital public infrastructure (DPI) approach has gained prominence in digital transformation, with India's Aadhaar often cited as a success story for accelerating financial inclusion. This paper critically evaluates India's progress on financial inclusion from 2011 to 2021, revealing a paradox: while account ownership surged, account usage remained low. The paper highlights that government and Reserve Bank of India (RBI) mandates drove rapid account opening, with Aadhaar enabling account opening mainly as a physical ID and an authentication tool for transactions. The financial inclusion efforts focused on expanding account ownership for direct benefit transfers, often at the expense of service quality. Banks, pressured to meet political targets, faced weak commercial incentives due to restrictive pricing regulations and mismatched service delivery models. These constraints hampered sustainable account usage. The paper explores whether alternative policy designs could have balanced electoral and economic goals more effectively. Demand-side constraints, shaped by socioeconomic conditions, and supply-side path dependencies influenced government choices. However, the analysis suggests room for greater political creativity in defining the policy objectives, liberalizing regulations liberalization to enhance financial inclusion, and leveraging the public sector banks. Finally, the paper discusses implications of this analysis for institutional reforms, including redesigning welfare schemes, revisiting public sector bank ownership, and rethinking top-down financial mandates. It also situates India's experience within the broader debate on the state's role in DPIs, challenging the notion that state-led DPI initiatives inherently maximize public value. |
| JEL: | G28 H53 L86 O33 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:anf:wpaper:35 |
| By: | Klaus M. Miller; Julia Schmitt; Bernd Skiera |
| Abstract: | Privacy regulations often necessitate a balance between safeguarding consumer privacy and preventing economic losses for firms that utilize consumer data. However, little empirical evidence exists on how such laws affect firm performance. This study aims to fill that gap by quantifying the impact of the European Union's General Data Protection Regulation (GDPR) on online usage behavior over time. We analyzed data from 6, 286 websites across 24 industries, covering 10 months before and 18 months after the GDPR's enactment in 2018. Employing a generalized synthetic control estimator, we isolated the short- and long-term effects of the GDPR on user behavior. Our results show that the GDPR negatively affected online usage per website on average; specifically, weekly visits decreased by 4.88% in the first 3 months and 10.02% after 18 months post-enactment. At the 18-month mark, these declines translated into average revenue losses of about USD 7 million for e-commerce websites and nearly USD 2.5 million for ad-based websites. Nonetheless, the GDPR's impact varied across website size, industry, and user origin, with some large websites and industries benefiting from the regulation. Notably, the largest 10% of websites pre-GDPR suffered less, suggesting that the GDPR has increased market concentration. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.11589 |
| By: | Paul M. Lohmann (University of Cambridge); Elisabeth Gsottbauer (Free University of Bozen-Bolzano); Christina Gravert (Department of Economics, University of Copenhagen); Lucia A. Reisch (Judge Business School, University of Cambridge,) |
| Abstract: | This paper explores the relationship between decision-making speed and the effectiveness of two nudges – carbon footprint labelling and menu repositioning – aimed at encouraging climate-friendly food choices. Building on Kahneman’s dual-process theory of decision-making, we examine whether these interventions are more effective in fast, intuitive (System 1) contexts compared to reflective, deliberate (System 2) ones. Using an incentivized online randomized controlled trial with a quasirepresentative sample of British consumers (N=3, 052) ordering meals through an experimental food-delivery platform, we introduced a time-pressure mechanism to capture both fast and slow decision-making processes. Our findings suggest that menu repositioning is an effective tool for promoting climate-friendly choices when decisions are made quickly, though the effect fades with extended deliberation. Carbon labels, in contrast, showed minimal impact overall but reduced emissions among highly educated, climate-conscious individuals under time pressure. The results imply that choice architects should apply both interventions in contexts where consumers make rapid decisions, such as digital platforms, to help mitigate climate externalities. |
| Keywords: | carbon-footprint labelling, choice architecture, food-delivery apps, low-carbon diets, dual-process models, system 1 |
| JEL: | C90 D04 I18 D90 Q18 Q50 |
| Date: | 2024–12–13 |
| URL: | https://d.repec.org/n?u=RePEc:kud:kucebi:2419 |
| By: | Kind, Hans Jarle (Dept. of Business and Management Science, Norwegian School of Economics); Schjelderup, Guttorm (Dept. of Business and Management Science, Norwegian School of Economics) |
| Abstract: | Many of the largest and most influential industries in the global economy operate digitally as multi-sided platforms, catering to different groups who are connected through intergroup network effects. This paper provides a survey of the theoretical literature on the effects of taxing these firms via indirect and corporate taxes. It seeks to establish an understanding of why traditional insights from taxation in one-sided markets may not apply to firms in multi-sided markets. Indeed, governments risk implementing counterproductive tax policies in multi-sided markets if they base their strategies on what constitutes efficient taxation in traditional markets. |
| Keywords: | Multisided platforms; taxation; imperfect competition |
| JEL: | D40 D43 H21 H22 L13 |
| Date: | 2024–12–20 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:nhhfms:2024_012 |
| By: | Limin Wen; Junxue Li; Tong Pu; Yiying Zhang |
| Abstract: | Conditional risk measures and their associated risk contribution measures are commonly employed in finance and actuarial science for evaluating systemic risk and quantifying the effects of risk contagion. This paper introduces various types of contribution measures based on the MCoVaR, MCoES, and MMME studied in Ortega-Jim\'enez et al. (2021) and Das & Fasen-Hartmann (2018) to assess both the absolute and relative effects of a single risk when other risks in a group are in distress. The properties of these contribution risk measures are examined, and sufficient conditions for comparing these measures between two sets of random vectors are established using univariate and multivariate stochastic orders and stochastic dependence notions. Numerical examples are presented for validating the conditions. Finally, a real dataset from the cryptocurrency market is also utilized to analyze the contagion effect in terms of our proposed contribution measures. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13384 |
| By: | Deiana, C; Dragone, D; Giua, L |
| Abstract: | We propose a model of addictive consumption to study the demand for imperfect substitutes involving substances like alcohol, nicotine and opioids, as well as behavioral addictions like gambling and digital addiction. We study a 2017 Italian policy aimed at reducing gambling by limiting the number of available slot machines. Despite the reduction in slot machines, the policy produced an unintended 25% increase in net expenditure, particularly among low-wealth and low-educated individuals who also engage in other addictive behaviors. This result can be rationalized as the consequence of changes in self-control costs due to social contagion effects. |
| Keywords: | addiction; gambling; horizontal differentiation; self-control; slot machines; temptation; |
| JEL: | I18 L43 L83 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:yor:hectdg:24/21 |
| By: | Donato Masciandaro (Department of Economics, Bocconi University); Davide Romelli (Department of Economics, Trinity College Dublin); Stefano Ugolini (Department of Economics, Universit' Toulouse Capitole) |
| Abstract: | This paper focuses on an early unique experiment of freely floating State-issued money, implemented in Venice between 1619 and 1666. Building on a new hand-collected database from a previously unexplored archival source, we show that, despite the Venetian ducat's status as an international currency and the government's reputation for fiscal prudence, its external value was significantly, and increasingly, affected by episodes of automatic government deficit monetization through the Banco del Giro during the crises of 1630 (outbreak of the bubonic plague) and 1648-50 (escalation of the Cretan War). This suggests that the institutional context plays an important role in the transmission mechanism between government deficit monetization and exchange rates. |
| Keywords: | Fiscal Dominance, Monetary Policy, Early Modern Venice, Banco del Giro, Fiat Money, Deficit Monetization, Historical Exchange Rates |
| JEL: | F31 E63 N33 N43 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:tcd:tcduee:tep1124 |
| By: | Zanfrillo, Alicia Inés; Morettini, Mariano; Narvarte, Alejandra; Huergo, María Consuelo |
| Abstract: | El objetivo del presente trabajo consiste en describir el nivel de madurez digital en las empresas de la industria pesquera de la ciudad de Mar del Plata, enfocándose en el eje estratégico, que abarca la cadena de valor, el modelo de negocios y los productos y servicios en su interrelación con los ODS. |
| Keywords: | Transformación Digital; Industria Pesquera; Mar del Plata; |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:nmp:nuland:4222 |
| By: | Imelda (Geneva Graduate Institute (IHEID), Department of International Economics); Anna Lou Abatayo (Environmental Economics and Natural Resources Group, Wageningen University and Research); Budy Resusodarmo (Australian National University, Arndt-Corden Department of Economics, Crawford School of Public Policy) |
| Abstract: | The timing of payment can enhance salience, making customers more price-responsive when paying before consumption rather than after. This study examines Indonesia’s nationwide switch to prepaid electricity metering, impacting over 40 million households. We find that prepaid metering users are twice as price-elastic as postpaid users. We also find a positive willingness to pay for prepaid metering, suggesting consumer welfare gains. As prices rise, prepaid metering reduces excess burden by 1.5% and CO2 emissions by nearly 6%. These findings suggest prepaid meters can support climate policy goals by promoting energy conservation without imposing significant burdens on consumers. |
| Keywords: | electricity; prepayment; elasticity; salience; energy conservation |
| JEL: | Q41 Q48 I30 |
| Date: | 2024–12–17 |
| URL: | https://d.repec.org/n?u=RePEc:gii:giihei:heidwp22-2024 |
| By: | Tatsuru Kikuchi |
| Abstract: | In this study, we perform some analysis for the probability distributions in the space of frequency and time variables. However, in the domain of high frequencies, it behaves in such a way as the highly non-linear dynamics. The wavelet analysis is a powerful tool to perform such analysis in order to search for the characteristics of frequency variations over time for the prices of major cryptocurrencies. In fact, the wavelet analysis is found to be quite useful as it examine the validity of the efficient market hypothesis in the weak form, especially for the presence of the cyclical persistence at different frequencies. If we could find some cyclical persistence at different frequencies, that means that there exist some intrinsic causal relationship for some given investment horizons defined by some chosen sampling scales. This is one of the characteristic results of the wavelet analysis in the time-frequency domains. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.14058 |
| By: | A T M Omor Faruq; Md Toufiqul Huq |
| Abstract: | This paper examines the pivotal role central banks play in advancing sustainable finance, a crucial component in addressing global environmental and social challenges. As supervisors of financial stability and economic growth, central banks have dominance over the financial system to influence how a country moves towards sustainable economy. The chapter explores how central banks integrate sustainability into their monetary policies, regulatory frameworks, and financial market operations. It highlights the ways in which central banks can promote green finance through sustainable investment principles, climate risk assessments, and green bond markets. Additionally, the chapter examines the collaborative efforts between central banks, governments, and international institutions to align financial systems with sustainability goals. By investigating case studies and best practices, the chapter provides a comprehensive understanding of the strategies central banks employ to foster a resilient and sustainable financial landscape. The findings underscore the imperative for central banks to balance traditional mandates with the emerging necessity to support sustainable development, ultimately contributing to the broader agenda of achieving global sustainability targets. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13576 |