nep-pay New Economics Papers
on Payment Systems and Financial Technology
Issue of 2019‒12‒16
25 papers chosen by

  1. Determinant factors influencing people to use motorcycle taxi online services using the Analytical Hierarchy Process By Raco, Jozef; Raton, Yulius; Taroreh, Frankie; Muaja, Octavianus
  2. Digital Transformation in Latin America – A Bibliometric Landscape of a Nascent Field By Cortés-Sánchez, Julián David
  3. Cryptocurrency Price Prediction and Trading Strategies Using Support Vector Machines By David Zhao; Alessandro Rinaldo; Christopher Brookins
  4. El comercio digital en América Latina ¿Qué desafíos enfrentan las empresas y cómo superarlos? By Suominen, Kati
  5. The AI Techno-Economic Segment Analysis By Giuditta De Prato; Montserrat Lopez Cobo; Sofia Samoili; Riccardo Righi; Miguel Vazquez Prada Baillet; Melisande Cardona
  6. Token-Based Platform Finance By Cong, Lin W.; Li, Ye; Wang, Neng
  7. A Glancing Meteor of FinTech “Start-up” Business Model (Perspective Views of Italian, Turkish, and Indonesian Users) By Usman, Berto
  8. Birds of a feather flock together and get money from the crowd By Valeria Venturelli; Giovanni Gallo; Alessia Pedrazzoli
  9. Merger Policy in Digital Markets: An Ex-Post Assessment By Elena Argentesi; Paolo Buccirossi; Emilio Calvano; Tomaso Duso; Alessia Marrazzo; Salvatore Nava
  10. Technological innovation in mortgage underwriting and the growth in credit, 1985–2015 By Foote, Christopher L.; Loewenstein, Lara; Willen, Paul S.
  11. On the concentration of innovation in top cities in the digital age By Caroline Paunov; Dominique Guellec; Nevine El-Mallakh; Sandra Planes-Satorra; Lukas Nüse
  12. Re-Imagining Digital Copyright Through the Power of Imitation: Lessons from Confucius and Plato By Frosio, Giancarlo
  13. Consumer Use of Multiple Payment Methods By Shy, Oz
  14. Job Prestige and Mobile Dating Success:A Field Experiment By Brecht Neyt; Stijn Baert; Jana Vynckier
  15. Machine Learning et nouvelles sources de données pour le scoring de crédit By Christophe Hurlin; Christophe Pérignon
  16. Empowering women with digital extension in Uganda: Effects of information and role models By Lecoutere, Els; Spielman, David J.; Campenhout, Bjorn Van
  17. Credit Unions in Chile: What is their Role? By Antonio Lemus; Cristian Rojas
  18. The Hardware–Software Model: A New Conceptual Framework of Production, R&D, and Growth with AI By Jakub Growiec
  19. Risk of Bitcoin Market: Volatility, Jumps, and Forecasts By Junjie Hu; Wolfgang Karl H\"ardle; Weiyu Kuo
  20. The Impact of the General Data Protection Regulation on Internet Interconnection By Ran Zhuo; Bradley Huffaker; kc claffy; Shane Greenstein
  21. Digital platforms ‘without a cause’: why the impact on a museum organization should not be taken for granted. The case study of Civic Museums in Treviso By Michele Tamma; Stefania Zardini Lacedelli; Silvia Maria Carolo
  22. Performance Measurement of SMS and/or Chat Service By Haryadi, Sigit
  23. Of trackers and tractors: Using a smartphone app and compositional data analysis to explore the link between mechanization and intra-household allocation of time in Zambia By Daum, Thomas; Capezzone, Filippo; Birner, Regina
  24. A comparison of machine learning model validation schemes for non-stationary time series data By Schnaubelt, Matthias
  25. Who Bears the Welfare Costs of Monopoly? The Case of the Credit Card Industry By Kyle F. Herkenhoff; Gajendran Raveendranathan

  1. By: Raco, Jozef; Raton, Yulius; Taroreh, Frankie; Muaja, Octavianus
    Abstract: The advancement of technology and Smartphone applications offers a lot of opportunities and challenges for companies to increase their market share. Through this technology and its application, companies such as transportation industries can make a lot of money and bring their products and services closer, faster and more easily to customers. In addition the customers can gain access to companies’ services and products on time. On the other hand the advancement of Smartphone technology disrupts the common transportation business practices. Communication and negotiation are becoming more virtual. This technology brings about huge benefits to both customers and companies. However the same technology causes a huge problem especially to other transportation companies as they might lose market if they do not use it. This technology helps many transportation industries to make business innovations such as offering lower prices, faster services and deliveries. This research focuses on transportation companies, specifically motorcycle taxis with online booking, which use a Smartphone application. In Manado Indonesia there are three popular motorcycle taxi online companies that use a Smartphone online application, which are Gojek, Grab and Uber. A lot of people use an online motorcycle taxi rather than public transportation because of its convenience, affordable price, safety and speed compared to local public transport. This study aims to find out the determinant factors that influence people to use motorcycle taxi online services. This research is going to reveal the favorite motorcycle taxi online company and its criteria based on respondents’ perspectives. This paper will use the Analytical Hierarchy Process both for data gathering and data analysis. The research findings will contribute to the local government in formulating laws and policies specifically on motorcycle taxi online service.
    Date: 2018–06–22
  2. By: Cortés-Sánchez, Julián David (Universidad del Rosario)
    Abstract: This study presents the first bibliometric analysis of the subject of Digital Transformation (DT) in Latin America (LATAM). Social network analysis and text-mining was implemented. It was found that the co-authorship network is geographically diverse and influenced by Brazil and Mexico, yet disconnected from the majority of LATAM countries. Research output and impact are increasing but still far from global dynamics and not yet permeated by the open access agenda. Finally, the most consolidated research topics associated with DT are “Industry 4.0”, “smart manufacturing” and the “Internet of Things”.
    Date: 2019–04–26
  3. By: David Zhao; Alessandro Rinaldo; Christopher Brookins
    Abstract: Few assets in financial history have been as notoriously volatile as cryptocurrencies. While the long term outlook for this asset class remains unclear, we are successful in making short term price predictions for several major crypto assets. Using historical data from July 2015 to November 2019, we develop a large number of technical indicators to capture patterns in the cryptocurrency market. We then test various classification methods to forecast short-term future price movements based on these indicators. On both PPV and NPV metrics, our classifiers do well in identifying up and down market moves over the next 1 hour. Beyond evaluating classification accuracy, we also develop a strategy for translating 1-hour-ahead class predictions into trading decisions, along with a backtester that simulates trading in a realistic environment. We find that support vector machines yield the most profitable trading strategies, which outperform the market on average for Bitcoin, Ethereum and Litecoin over the past 22 months, since January 2018.
    Date: 2019–11
  4. By: Suominen, Kati
    Abstract: El conocimiento sobre cómo las empresas de América Latina y el Caribe usan Internet para involucrarse en el comercio, y en particular los desafíos que enfrentan para adoptar y usar el comercio electrónico para exportar bienes y servicios, es aún bastante incipiente. El propósito de este documento analizar los desafíos que enfrentan las empresas de América Latina y el Caribe en su comercio electrónico, mediante la explotación de datos de encuestas que cubren a más de 1.430 empresas de la región, junto con datos económicos y de consumo, así como entrevistas con firmas digitales. También propone algunas recomendaciones de políticas sobre cómo las economías regionales pueden trabajar juntas para potenciar el comercio electrónico transfronterizo en la región.
    Date: 2019–11–29
  5. By: Giuditta De Prato (European Commission - JRC); Montserrat Lopez Cobo (European Commission - JRC); Sofia Samoili (European Commission - JRC); Riccardo Righi (European Commission - JRC); Miguel Vazquez Prada Baillet (European Commission - JRC); Melisande Cardona (European Commission - JRC)
    Abstract: The Techno-Economics Segment (TES) analytical approach aims to offer a timely representation of an integrated and very dynamic technological domain not captured by official statistics or standard classifications. Domains of that type, such as photonics and artificial intelligence (AI), are rapidly evolving and expected to play a key role in the digital transformation, enabling further developments. They are therefore policy relevant and it is important to have available a methodology and tools suitable to map their geographic presence, technological development, economic impact, and overall evolution. The TES approach was developed by the JRC. It provides quantitative analyses in a micro-based perspective. AI has become an area of strategic importance with potential to be a key driver of economic development. The Commission announced in April 2018 a European strategy on AI in its communication "Artificial Intelligence for Europe", COM(2018)237, and in December a Coordinated Action Plan, COM(2018)795. In order to provide quantitative evidences for monitoring AI technologies in the worldwide economies, the TES approach is applied to AI in the present study. The general aim of this work is to provide an analysis of the AI techno-economic complex system, addressing the following three fundamental research questions: (i) Which are the economic players involved in the research and development as well as in the production and commercialisation of AI goods and services? And where are they located? (ii) Which specific technological areas (under the large umbrella of AI) have these players been working at? (iii) How is the network resulting from their collaboration shaped and what collaborations have they been developing? This report addresses these research questions throughout its different sections, providing both an overview of the AI landscape and a deep understanding of the structure of the socio-economic system, offering useful insights for possible policy initiatives. This is even more relevant and challenging as the considered technologies are consolidating and introducing deep changes in the economy and the society. From this perspective, the goal of this report is to draw a detailed map of the considered ecosystem, and to analyse it in a multidimensional way, while keeping the policy perspective in mind. The period considered in our analysis covers from 2009 to 2018. We detected close to 58,000 relevant documents and, identified 34,000 players worldwide involved in AI-related economic processes. We collected and processed information regarding these players to set up a basis from which the exploration of the ecosystem can take multiple directions depending on the targeted objective. In this report, we present indicators regarding three dimensions of analysis: (i) the worldwide landscape overview, (ii) the involvement of players in specific AI technological sub-domains, and (iii) the activities and the collaborations in AI R&D processes. These are just some of the dimensions that can be investigated with the TES approach. We are currently including and analysing additional ones.
    Date: 2019–11
  6. By: Cong, Lin W. (Cornell University); Li, Ye (Ohio State University); Wang, Neng (Columbia Business School - Finance and Economics; National Bureau of Economic Research (NBER))
    Abstract: We develop a dynamic model of platform economy where tokens derive value by facilitating transactions among users and the platform conducts optimal token-supply policy to finance investment in platform quality and to compensate platform owners. Even though token price is endogenously determined in a liquid market, the platform's financial constraint generates an endogenous token issuance cost that causes under-investment and conflict of interest between insiders (owners) and outsiders (users). The franchise value (seigniorage) incentivizes the owners to buy back and burn tokens out of circulation, reducing token price volatility. Blockchain technology is crucial for token-based platforms because it enables platform owners to commit to predetermined rules of token supply that can significantly improve efficiency by addressing platform owners' time inconsistency and mitigating under-investment.
    Date: 2019–11
  7. By: Usman, Berto (University of Bengkulu)
    Abstract: The purpose of this study is to elaborate the usage of decomposed theory of planned behavior, in terms of users’ perspective of utilizing financial technology (FinTech) application in startup firms. Hereby, there are 375 respondents participated in the online survey. The survey was conducted through several social media platforms namely; WhatApps, Messenger, Linkedln, e-mail and Line group. The targeted respondents are from Italy, Turkey, and Indonesia. In this study, the constructs are generated from the decomposed theory of planned behavior as developed by Shih & Fang, (2004). Every indicator used in this research must be following several instrumental tests such as validity and reliability test. In order to get more knowledge and description about the different perceptions of users in three cross-country tests, the correlation analysis and descriptive statistics analysis with countries’ mean score are employed. According to the results, it is noted that Italian mean score (3.61) in regard to behavior intention (BI) of using financial technology application as the product of FinTech startup firms is relatively higher than the mean scores of Turkish (3.27) and Indonesian users (3.27).
    Date: 2018–01–25
  8. By: Valeria Venturelli; Giovanni Gallo; Alessia Pedrazzoli
    Abstract: In constructing online alternative finance instruments as a new form of financial democratization and financial inclusion, this article aims at verifying the presence of similarity effect in equity crowdfunding investments. Discussion focuses on ethnic and gender similarity between the seekers and investors that sustained the project. Our analysis is based on 5,996 personal investors that have participated in 81 equity crowdfunding campaigns, on Crowdcube, a British equity crowdfunding platform from 2011 and 2016. Results show that in equity crowdfunding gender and ethnic similarities play different role based on investors’ characteristics - gender, ethnicity and the combination of two. In particular, ethnic similarity positively influence the level of amount invested by both female and male investors belonging to an ethnic minority. Even if female investors tend to prefer male company, their preference changes if a female proponent belonging to an ethnic minority runs the company. From a practical perspective, our findings shed new light on how individual characteristics can be important factor in financing situations. Results allow entrepreneurs and equity crowdfunding platforms to understand better potential investor behaviour and highlights the role of equity crowdfunding as tool for minorities’ financial inclusion and women entrepreneur empowerment.
    Keywords: equity crowdfunding, entrepreneurial finance, ethnicity, gender, similarity effect
    JEL: G02 G11 M13
    Date: 2019–12
  9. By: Elena Argentesi; Paolo Buccirossi; Emilio Calvano; Tomaso Duso; Alessia Marrazzo; Salvatore Nava
    Abstract: This paper presents a broad retrospective evaluation of mergers and merger decisions in the digital sector. We first discuss the most crucial features of digital markets such as network effects, multi-sidedness, big data, and rapid innovation that create important challenges for competition policy. We show that these features have been key determinants of the theories of harm in major merger cases in the past few years. We then analyse the characteristics of almost 300 acquisitions carried out by three major digital companies –Amazon, Facebook, and Google – between 2008 and 2018. We cluster target companies on their area of economic activity and show that they span a wide range of economic sectors. In most cases, their products and services appear to be complementary to those supplied by the acquirers. Moreover, target companies seem to be particularly young, being four-years-old or younger in nearly 60% of cases at the time of the acquisition. Finally, we examine two important merger cases, Facebook/Instagram and Google/Waze, providing a systematic assessment of the theories of harm considered by the UK competition authorities as well as evidence on the evolution of the market after the transactions were approved. We discuss whether the CAs performed complete and careful analyses to foresee the competitive consequences of the investigated mergers and whether a more effective merger control regime can be achieved within the current legal framework.
    Keywords: Digital Markets, Mergers, Network Effects, Big Data, Platforms, Ex-post, Antitrust
    JEL: L4 K21
    Date: 2019
  10. By: Foote, Christopher L. (Federal Reserve Bank of Boston); Loewenstein, Lara (Federal Reserve Bank of Cleveland); Willen, Paul S. (Federal Reserve Bank of Boston)
    Abstract: The application of information technology to finance, or “fintech,” is expected to revolutionize many aspects of borrowing and lending in the future, but technology has been reshaping consumer and mortgage lending for many years. During the 1990s, computerization allowed mortgage lenders to reduce loan-processing times and largely replace human-based assessments of credit risk with default predictions generated by sophisticated empirical models. Debt-to-income ratios at origination add little to the predictive power of these models, so the new automated underwriting systems allowed higher debt-to-income ratios than previous underwriting guidelines would have allowed. In this way, technology brought about an exogenous change in lending standards that was especially relevant for borrowers with low current incomes relative to their expected future incomes—in particular, young college graduates. By contrast, the data suggest that the credit expansion during the 2000s housing boom was an endogenous response to widespread expectations of higher future house prices, as average mortgage sizes rose for borrowers across the entire income distribution.
    Keywords: mortgage underwriting; housing cycle; technological change; credit boom
    JEL: C55 D53 G21 L85 R21 R31
    Date: 2019–11–01
  11. By: Caroline Paunov (OECD); Dominique Guellec (OECD); Nevine El-Mallakh (Université Paris 1 Panthéon-Sorbonne); Sandra Planes-Satorra (OECD); Lukas Nüse (Bertelsmann Foundation)
    Abstract: This paper investigates how digital technologies have shaped the concentration of inventive activity in cities across 30 OECD countries. It finds that patenting is highly concentrated: from 2010 to 2014, 10% of cities accounted for 64% of patent applications to the European Patent Office, with the top five (Tokyo, Seoul, San Francisco, Higashiosaka and Paris) representing 21.8% of applications. The share of the top cities in total patenting increased modestly from 1995 to 2014. Digital technology patent applications are more concentrated in top cities than applications in other technology fields. In the United States, which has led digital technology deployment, the concentration of patent applications in top cities increased more than in Japan and Europe over the two decades. Econometric results confirm that digital technology relates positively to patenting activities in cities and that it benefits top cities, in particular, thereby strengthening the concentration of innovation in these cities.
    Keywords: cities, digital technologies, geography of innovation, innovation, local knowledge spillovers, OECD countries, patenting
    JEL: R12 O31 O34
    Date: 2019–12–16
  12. By: Frosio, Giancarlo (Strasbourg University)
    Abstract: For millennia, Western and Eastern culture shared a common creative paradigm. From Confucian China, across the Hindu Kush with the Indian Mahābhārata, the Bible, the Koran and the Homeric epics, to Platonic mimēsis and Shakespeare’s “borrowed feathers,” our culture was created under a fully open regime of access to pre-existing expressions and re-use. Creativity used to be propelled by the power of imitation. However, modern policies have largely forgotten the cumulative and collaborative nature of creativity. Actually, the last three decades have witnessed an unprecedented expansion of intellectual property rights in sharp contrast with the open and participatory social norms governing creativity in the networked environment. Against this background, this paper discusses the reaction to traditional copyright policy and the emergence of a social movement re-imagining copyright according to a common tradition focusing on re-use, collaboration, access and cumulative creativity. This reaction builds upon copyright’s growing irrelevance in the public mind, especially among younger generations in the digital environment, because of the emergence of new economics of digital content distribution in the Internet. Along the way, the rise of the users, and the demise of traditional gatekeepers, forced a process of reconsideration of copyright’s rationale and welfare incentives. Scholarly and market alternatives to traditional copyright have been plenty, attempting to reconcile pre-modern, modern and post-modern creative paradigms. Building upon this body of research, proposals and practice, this Article will finally try to chart a roadmap for reform that reconnects Eastern and Western creative experience in light of a common past, looking for a shared future.
    Date: 2018–01–15
  13. By: Shy, Oz (Federal Reserve Bank of Atlanta)
    Abstract: The paper investigates the degree to which buyers choose to diversify their use of payment methods for in-person purchases. Some buyers use only one payment instrument. Others combine the use of mostly cash, credit, debit cards, and a few paper checks and prepaid cards. To each survey respondent, I apply three concentration measures over the use of payment instruments. Results show that the degree of consumers' payment concentration exhibits almost no correlation with consumer demographics, payment volume, or aggregate value.
    Keywords: multiple payment methods; consumer payment choice; payment instruments; in-person purchases; concentration measures
    JEL: D9 E42
    Date: 2019–10–01
  14. By: Brecht Neyt; Stijn Baert; Jana Vynckier (-)
    Abstract: Research exploiting data on classic (offline) couple formation has confirmed predictions from evolutionary psychology in a sense that males attach more value to attractiveness and women attach more value to earnings potential. We examine whether these human partner preferences survive in a context of fewer search and social frictions. We do this by means of a field experiment on the mobile dating app Tinder, which takes a central place in contemporary couple formation. Thirty-two fictitious Tinder profiles that randomly differ in job status and job prestige are evaluated by 4,800 other, real users. We find that both males and females do not use job status or job prestige as a determinant of whom to show initial interest in on Tinder. However, we do see evidence that, after this initial phase, males less frequently begin a conversation with females when those females are unemployed but also then do not care about the particular job prestige of employed females.
    Keywords: job prestige, partner preferences, dating apps, online dating, Tinder
    JEL: J12 J16 J13 C93
    Date: 2019–11
  15. By: Christophe Hurlin (LEO - Laboratoire d'Économie d'Orleans - CNRS - Centre National de la Recherche Scientifique - Université de Tours - UO - Université d'Orléans); Christophe Pérignon (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)
    Abstract: In this article, we discuss the contribution of Machine Learning techniques and new data sources (New Data) to credit-risk modelling. Credit scoring was historically one of the first fields of application of Machine Learning techniques. Today, these techniques permit to exploit new sources of data made available by the digitalization of customer relationships and social networks. The combination of the emergence of new methodologies and new data has structurally changed the credit industry and favored the emergence of new players. First, we analyse the incremental contribution of Machine Learning techniques per se. We show that they lead to significant productivity gains but that the forecasting improvement remains modest. Second, we quantify the contribution of the "datadiversity", whether or not these new data are exploited through Machine Learning. It appears that some of these data contain weak signals that significantly improve the quality of the assessment of borrowers' creditworthiness. At the microeconomic level, these new approaches promote financial inclusion and access to credit for the most vulnerable borrowers. However, Machine Learning applied to these data can also lead to severe biases and discrimination.
    Abstract: Dans cet article, nous proposons une réflexion sur l'apport des techniques d'apprentissage automatique (Machine Learning) et des nouvelles sources de données (New Data) pour la modélisation du risque de crédit. Le scoring de crédit fut historiquement l'un des premiers champs d'application des techniques de Machine Learning. Aujourd'hui, ces techniques permettent d'exploiter de « nouvelles » données rendues disponibles par la digitalisation de la relation clientèle et les réseaux sociaux. La conjonction de l'émergence de nouvelles méthodologies et de nouvelles données a ainsi modifié de façon structurelle l'industrie du crédit et favorisé l'émergence de nouveaux acteurs. Premièrement, nous analysons l'apport des algorithmes de Machine Learning à ensemble d'information constant. Nous montrons qu'il existe des gains de productivité liés à ces nouvelles approches mais que les gains de prévision du risque de crédit restent en revanche modestes. Deuxièmement, nous évaluons l'apport de cette « datadiversité », que ces nouvelles données soient exploitées ou non par des techniques de Machine Learning. Il s'avère que certaines de ces données permettent de révéler des signaux faibles qui améliorent sensiblement la qualité de l'évaluation de la solvabilité des emprunteurs. Au niveau microéconomique, ces nouvelles approches favorisent l'inclusion financière et l'accès au crédit des emprunteurs les plus fragiles. Cependant, le Machine Learning appliqué à ces données peut aussi conduire à des biais et à des phénomènes de discrimination.
    Keywords: Machine Learning ML,Credit scoring,New data,Nouvelles données,Scoring de crédit,Apprentissage automatique
    Date: 2019–11–21
  16. By: Lecoutere, Els; Spielman, David J.; Campenhout, Bjorn Van
    Keywords: Teaching/Communication/Extension/Profession, Labor and Human Capital
    Date: 2019–09
  17. By: Antonio Lemus; Cristian Rojas
    Abstract: This paper questions the role that credit unions play in the Chilean financial system, particularly if they allow financial inclusion. For this purpose, using unique statistical information existing at the CMF, the credit and savings behavior of credit unions’ members is studied, with granularity at the individual level. The results indicate that credit unions effectively contribute to financial inclusion, providing financial services mostly to people with low incomes, elderly, women, and inhabitants of small communities, far from the large urban centers.
    Keywords: credit unions, financial inclusion, credit
    JEL: G21 G28 P13
    Date: 2019
  18. By: Jakub Growiec (Department of Quantitative Economics, Warsaw School of Economics, Poland; Rimini Centre for Economic Analysis)
    Abstract: The article proposes a new conceptual framework for capturing production, R&D, and economic growth in aggregative economic models which extend their horizon into the digital era. Two key factors of production are considered: hardware, including physical labor, traditional physical capital and programmable hardware, and software, encompassing human cognitive work and pre-programmed software, including artificial intelligence (AI). Hardware and software are complementary in production whereas their constituent components are mutually substitutable. The framework generalizes, among others, the standard model of production with capital and labor, models with capital–skill complementarity and skill-biased technical change, and unified growth theories embracing also the pre-industrial period. It offers a clear conceptual distinction between mechanization and automation of production. It delivers sharp, empirically testable and economically intuitive predictions for long-run growth, the evolution of factor shares, and the direction of technical change.
    Keywords: production function, R&D equation, technological progress, complementarity, automation, artificial intelligence
    JEL: O30 O40 O41
    Date: 2019–12
  19. By: Junjie Hu; Wolfgang Karl H\"ardle; Weiyu Kuo
    Abstract: Among all the emerging markets, the cryptocurrency market is considered the most controversial and simultaneously the most interesting one. The visibly significant market capitalization of cryptos motivates modern financial instruments such as futures and options. Those will depend on the dynamics, volatility, or even the jumps of cryptos. In this paper, the risk characteristics for Bitcoin are analyzed from a realized volatility dynamics view. The realized variance is estimated with the corrected threshold jump components, realized semi-variance, and signed jumps. Our empirical results show that the BTC is far riskier than any of the other developed financial markets. Up to 68% of the days are identified to be entangled with jumps. However, the discontinuities do not contribute to the variance significantly. The full-sample fitting suggests that future realized variance has a positive relationship with downside risk and a negative relationship with the positive jump. The rolling-window out-of-sample forecasting results reveal that the forecasting horizon plays an important role in choosing forecasting models. For the long horizon risk forecast, explicitly modeling jumps and signed estimators improve forecasting accuracy and give extra utility up to 19 bps annually, while the HAR model without accounting jumps or signed estimators suits the short horizon case best. Lastly, a simple equal-weighted portfolio of BTC not only significantly reduces the size and quantity of jumps but also gives investors higher utility in short horizon case.
    Date: 2019–12
  20. By: Ran Zhuo; Bradley Huffaker; kc claffy; Shane Greenstein
    Abstract: The Internet comprises thousands of independently operated networks, where bilaterally negotiated interconnection agreements determine the flow of data between networks. The European Union’s General Data Protection Regulation (GDPR) imposes strict restrictions on processing and sharing of personal data of EU residents. Both contemporary news reports and simple bilateral bargaining theory predict reduction in data usage at the application layer would negatively impact incentives for negotiating interconnection agreements at the internet layer due to reduced bargaining power of European networks and increased bargaining frictions. Considerable empirical evidence at the application layer confirms this prediction. Using a large sample of interconnection agreements between networks around the world in 2015–2019, we empirically investigate the impact of the GDPR on interconnection behavior of network operators in the European Economic Area (EEA) compared to network operators in non-EEA OECD countries. All evidence estimates precisely zero effects across multiple measures: the number of observed agreements per network, the inferred agreement types, and the number of observed IP-address-level interconnection points per agreement. We also find economically small effects of the GDPR on the entry and the observed number of customers of networks. We conclude that the short-run costs for GDPR are concentrated at the application layer.
    JEL: L00 L51 L86
    Date: 2019–11
  21. By: Michele Tamma (Dept. of Management, Università Ca' Foscari Venice); Stefania Zardini Lacedelli (School of Museum Studies, University of Leicester); Silvia Maria Carolo (Dept. of Management, Università Ca' Foscari Venice)
    Abstract: The impact of digital platforms on different areas of the museum practice has been widely explored in museology. What is less clear is to what extent the adoption of digital platforms is connected to strategic choices and if it leads to organizational transformations. The paper addresses this issue through the case study derived from a project coordinated by the Department of Management, Ca’ Foscari University of Venice at the Civic Museums of Treviso. A qualitative study was implemented to explore the impact of the introduction of new digital practices on how the members of the museums conceive the relationship with audiences, the curatorial function and the predominant museum’s modes. The research outcomes show how the adoption of digital platforms can foster a broad reflection upon the underlying values and beliefs that shape behaviours in museum, but this reflection it is not enough in itself to trigger an organizational transformation.
    Keywords: digital platforms, digital practices, museum strategy, organisational change, museum value framework
    Date: 2019–12
  22. By: Haryadi, Sigit (Institut Teknologi Bandung)
    Abstract: This paper summarizes my papers and books, which reports on studies concerning the performance measurement of SMS and/or Chat services, particularly on mobile networks. In detail, SMS and/or Chat service quality measurements are grouped into two, the first is a measurement that aims to measure the provider's customer satisfaction, performed by the provider for internal purposes, or to report to the regulator, and the second measurement goal is that the provider and/or by the vendor in relation to the quality assurance provided by the vendor to the provider. In detail, the first type of measurement uses a sampling technique with refer to the subjective method, otherwise, the second type performs aggregate measurements with refer to the Signaling Ladder Diagram of the SMS and/or Chat services.
    Date: 2018–03–04
  23. By: Daum, Thomas; Capezzone, Filippo; Birner, Regina
    Keywords: Research and Development/Tech Change/Emerging Technologies
    Date: 2019–09
  24. By: Schnaubelt, Matthias
    Abstract: Machine learning is increasingly applied to time series data, as it constitutes an attractive alternative to forecasts based on traditional time series models. For independent and identically distributed observations, cross-validation is the prevalent scheme for estimating out-of-sample performance in both model selection and assessment. For time series data, however, it is unclear whether forwardvalidation schemes, i.e., schemes that keep the temporal order of observations, should be preferred. In this paper, we perform a comprehensive empirical study of eight common validation schemes. We introduce a study design that perturbs global stationarity by introducing a slow evolution of the underlying data-generating process. Our results demonstrate that, even for relatively small perturbations, commonly used cross-validation schemes often yield estimates with the largest bias and variance, and forward-validation schemes yield better estimates of the out-of-sample error. We provide an interpretation of these results in terms of an additional evolution-induced bias and the sample-size dependent estimation error. Using a large-scale financial data set, we demonstrate the practical significance in a replication study of a statistical arbitrage problem. We conclude with some general guidelines on the selection of suitable validation schemes for time series data.
    Keywords: machine learning,model selection,model validation,time series,cross-validation
    Date: 2019
  25. By: Kyle F. Herkenhoff; Gajendran Raveendranathan
    Abstract: How are the welfare costs from monopoly distributed across U.S. households? We answer this question for the U.S. credit card industry, which is highly concentrated, charges interest rates that are 3.4 to 8.8 percentage points above perfectly competitive pricing, and has repeatedly lost antitrust lawsuits. We depart from existing competitive models by integrating oligopolistic lenders into a heterogeneous agent, defaultable debt framework. Our model accounts for 20 to 50 percent of the spreads observed in the data. Welfare gains from competitive reforms in the 1970s are equivalent to a one-time transfer worth between 0.24 and 1.66 percent of GDP. Along the transition path, 93 percent of individuals are better off. Poor households benefit from increased consumption smoothing, while rich households benefit from higher general equilibrium interest rates on savings. Transitioning from 1970 to 2016 levels of competition yields welfare gains equivalent to a one-time transfer worth between 1.87 and 3.20 percent of GDP. Lastly, homogeneous interest rate caps in 2016 deliver limited welfare gains.
    Keywords: Welfare costs of monopoly; consumer credit; competition; welfare
    JEL: D14 D43 D60 E21 E44 G21
    Date: 2019–12

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.