COVID-19 Pandemic Increases the Divide Between Cash and Cashless Payment Users in Europe

This paper investigates how the COVID-19 pandemic has changed an important aspect of everyday life, viz. how people make payments. The empirical study is based on a survey of over 5,000 respondents from 22 European countries. It shows that consumers who had been making cashless payments prior to the outbreak of the pandemic have been even more likely to do so since it broke out. On the other hand, the consumers who had mostly been paying in cash have often continued to do so. The divide between those who pay in cash and those who do not, therefore, seems to have widened during the pandemic. It may suggest financial inclusion. Additionally, we found that the probability of more frequent cashless payments as a result of the pandemic differs considerably between countries and therefore indicate the role of country-specific factors

Chapter 10 Challenger bank as a new digital form of providing financial services to retail customers in the EU Internal Market

‘Challenger banks’ are banks or non-banking start-ups, the operations of which are based on digital technologies and which challenge big, traditional banks. It is a new approach to provide financial services, where an agile organization and new technologies are the key success factors. The aim of the study is to explore the operations of challenger banks and their new digital approaches to provide banking and investment services to retail customers as an innovation in the financial market. The case of Revolut is used as a unit of analysis. The study makes an attempt to identify and explore: (i) What product innovations have been implemented by Revolut in regard to the customers’ access to financial markets? (ii) How does Revolut compete with other challenger banks and traditional financial institutions? (iii) What are socio-economic consequences of innovations introduced by challenger banks? (iv) What is the legal formula of Revolut's operations? From the perspective of management theory, the study identifies the mechanisms of developing and implementing innovations in the financial sector, in the context of digital transformation and the changes in legal regulations

Chapter 10 Challenger bank as a new digital form of providing financial services to retail customers in the EU Internal Market

‘Challenger banks’ are banks or non-banking start-ups, the operations of which are based on digital technologies and which challenge big, traditional banks. It is a new approach to provide financial services, where an agile organization and new technologies are the key success factors. The aim of the study is to explore the operations of challenger banks and their new digital approaches to provide banking and investment services to retail customers as an innovation in the financial market. The case of Revolut is used as a unit of analysis. The study makes an attempt to identify and explore: (i) What product innovations have been implemented by Revolut in regard to the customers’ access to financial markets? (ii) How does Revolut compete with other challenger banks and traditional financial institutions? (iii) What are socio-economic consequences of innovations introduced by challenger banks? (iv) What is the legal formula of Revolut's operations? From the perspective of management theory, the study identifies the mechanisms of developing and implementing innovations in the financial sector, in the context of digital transformation and the changes in legal regulations

Forecasting volatility of Bitcoin

Since Bitcoin price is highly volatile, forecasting its volatility is crucial for many applications, such as risk management or hedging. We study which model is the most suitable for forecasting Bitcoin volatility. We consider several GARCH and two heterogeneous autoregressive (HAR) models and compare them. Since we utilize realized variance estimated from high frequency data as a proxy for true volatility, we can draw sharper conclusions than studies which use only daily data. We find that EGARCH and APARCH perform best among the GARCH models. HAR models based on realized variance perform better than GARCH models based on daily data. Superiority of HAR models over GARCH models is strongest for short-term volatility forecasts.publishedVersio

Modelling customers' intentions to use contactless cards

Since their introduction in the USA in 2002, contactless card payment systems have been widely regarded as the pinnacle of current retail banking technology. However, the potential demand and usage of this innovation has hitherto received little attention from the academic community. Ours is one of the first papers that explore the factors that are likely to govern acceptance and intentions to take-up the technology. The analysis utilises the methodological framework of the technology acceptance model (Davis, 1989; Davis et al., 1989) and develops a range of empirical representations. Our results lend support to the TAM conceptualisation and also indicate that some demographic characteristics imprint upon the intentions of potential users

Time Efficiency of Point-Of-Sale Payment Methods: Empirical Results for Cash, Cards, and Mobile Payments

Preprint publikacji https://doi.org/10.1007/978-3-642-40654-6_19We propose a novel approach for the time efficiency study of payment process at Point-Of-Sale (POS). A wide range of payment methods from cash and standard cards to contactless cards, RFID stickers and mobile payments (NFC and remote) was analysed. Transactions were timed by means of digital chronography of video material recorded in the biggest chain of convenience stores in Poland. Our results confirm that cash is a significantly faster payment method than traditional payment card with a magnetic stripe or EMV chip. However, the innovative payment methods, such as contactless cards and NFC mobile payments, are competitive to cash in terms of time efficiency. Contactless cards used in offline mode and without printing paper slips are the first popular electronic payment method in history faster than cash. Our results could be applied to optimise the payment process at POS as well as to develop innovative and efficient payment solutions

Switching from Cash to Cashless Payments during the COVID-19 Pandemic and Beyond

Using a survey of 5,504 respondents from 22 European countries, we examine preferences regarding cash and cashless payments at the point of sale (POS) during the COVID-19 crisis. Consumers favor cashless transactions when they believe that handling cash presents a higher risk of infection. Moreover, the habits they develop during periods of restrictions and lockdowns appear to further diminish their appetite for transacting in cash. Not only do these factors affect the current choice of payment method, but also they influence declared future intentions to move away from cash after the pandemic is over

Forecasting volatility of Bitcoin

Since Bitcoin price is highly volatile, forecasting its volatility is crucial for many applications, such as risk management or hedging. We study which model is the most suitable for forecasting Bitcoin volatility. We consider several GARCH and two heterogeneous autoregressive (HAR) models and compare them. Since we utilize realized variance estimated from high frequency data as a proxy for true volatility, we can draw sharper conclusions than studies which use only daily data. We find that EGARCH and APARCH perform best among the GARCH models. HAR models based on realized variance perform better than GARCH models based on daily data. Superiority of HAR models over GARCH models is strongest for short-term volatility forecasts

Forecasting volatility of Bitcoin

Since Bitcoin price is highly volatile, forecasting its volatility is crucial for many applications, such as risk management or hedging. We study which model is the most suitable for forecasting Bitcoin volatility. We consider several GARCH and two heterogeneous autoregressive (HAR) models and compare them. Since we utilize realized variance estimated from high frequency data as a proxy for true volatility, we can draw sharper conclusions than studies which use only daily data. We find that EGARCH and APARCH perform best among the GARCH models. HAR models based on realized variance perform better than GARCH models based on daily data. Superiority of HAR models over GARCH models is strongest for short-term volatility forecasts