Cryptocurrencies and Fintech
Paper Session
Saturday, Jan. 6, 2024 8:00 AM - 10:00 AM (CST)
- Chair: Linda Schilling, Washington University-St Louis
Who Invests in Crypto? Wealth, Financial Constraints, and Risk Attitudes
Abstract
We provide a first look into the drivers of household cryptocurrency investing. Analyzing consumer transaction data for millions of U.S. households, we find that, except for high-income early adopters, cryptocurrency investors resemble the general population. These investors span all income levels, with most dollars coming from high-income individuals, similar to equity investors. High past crypto returns and personal income shocks lead to increased cryptocurrency investments. Higher household-level inflation expectations also correlate with greater crypto investments, aligning with hedging motives. For most U.S. households, cryptocurrencies are treated like traditional assets.Did Fintech Loans Default More During the COVID-19 Pandemic? Were Fintech Firms “Cream-Skimming” the Best Borrowers?
Abstract
A growing portion of consumer credit has recently been devoted to unsecured personal installment loans. Fintech firms have been active players in this market, with an increasing market share, while the market share of banks has declined. Studies of fintech lending have shown that their digital access and ability to leverage alternative data have increased accessibility in underserved areas, enabled consumers with thin credit files to obtain credit, and provided a lower cost alternative to long-term credit card financing. This paper exams three questions: (1) Do proprietary loan rating systems accurately predict the likelihood of default? (2) Can a proprietary loan rating system, leveraging alternative data, that was developed in a favorable economic period continue to perform well under adverse economic conditions (such as the COVID-19 pandemic)? (3) Have fintechs been “cream skimming,” i.e., underpricing the cost of credit to top-tier customers? This study uses data from LendingClub, one of the largest fintech lenders in the personal loan market. We find that LendingClub’s loan rating system is superior to traditional measures of credit risk when predicting the likelihood of default and that the loan rating system continued to perform well during the pandemic period. Finally, we find no evidence of cream skimming.Do Uncertainty Indices Affect Cryptocurrencies? A Lesson from the Turbulent Times
Abstract
Although cryptocurrencies have already established themselves in the financial markets, the question of what factors influence their price behavior is open. This article aims to examine what type of risk has the greatest impact on the price movements of cryptocurrencies. We focus on the dependency between various uncertainty proxies and major cryptocurrencies as well as the spillover effects. Within the first group we consider several risk indices such as the economic policy uncertainty index, the volatility index, crude oil volatility index, gold volatility index, the geopolitical risk index and others. Among cryptocurrencies we take into account the ten mostly capitalized coins. The dynamic linkages between indices and coins are examined within the conditional correlation approach and the time varying vector autoregressive models. We analyze the volatility spillovers from risk indices to various coins based on the generalized forecast error variance. The data sample starts from April 2018 and ends up in December 2022 thus encompassing several upturns and downturns of the coin market. We find that for pairs of Volatility Index (VIX) and cryptocurrencies or Crude Oil Volatility Index (OVX) and coins the dynamic correlations for returns are different from zero and negative. In remaining cases the correlation between uncertainty indices and cryptocurrencies is insignificant. In the case of volatility spillovers in the static approach there is no transmission from risk indices to coins. In the dynamic approach, uncertainty indices based on the stocks' market implied volatility behave similarly transmitting the spillovers to coins in the pandemic period, while OVX transmits volatility over 2021 to coins. Most spillovers are observed within the risk indices themselves and within cryptocurrencies, but not between the former and the latter. Uncertainty indices do not transfer risk to cryptocurrencies, but they have a short-term impact on their volatility.JEL Classifications
- G2 - Financial Institutions and Services