Research

Abstract: Fintech firms are often viewed as competing with banks. Instead, more recently, there has been growth in partnership and collaboration between fintech firms and banks. These partnerships have allowed banks to access more information on consumers through data aggregation, artificial intelligence/machine learning (AI/ML), and other tools. We explore the demographics of consumers targeted by banks that have entered into such partnerships. Specifically, we test whether banks are more likely to extend credit offers (by mail) and/or credit originations to consumers who would have otherwise been deemed high risk either because of low credit scores or lack of credit scores altogether. Our analysis uses data on credit offers based on a survey conducted by Mintel, as well as data on credit originations based on the Federal Reserve’s Y-14M reports. Additionally, we analyze a unique data set of partnerships between fintech firms and banks compiled by CB Insights to identify the relevant partnerships. Our results indicate that banks are more likely to offer credit cards and personal loans to the credit invisible and below-prime consumers — and are also more likely to grant larger credit limits to those consumers — after the partnership period. Similarly, we find that fintech partnerships result in banks being more likely to originate mortgage loans to nonprime homebuyers and that they increase the mortgage loan amounts that banks grant to nonprime buyers as well. Overall, we find that these partnerships could help to move us toward a more inclusive financial system.

Abstract: Cryptocurrency markets are often characterized by market manipulation or, at the very least, by a sharp distinction between large and sophisticated investors and small retail investors. While traditional assets often see a divergence in the success of institutional traders and retail traders, we find an even more pronounced difference regarding the holders of Ethereum (ETH), the second-largest cryptocurrency by volume. We see a significant difference in how large holders of ETH behave compared with smaller holders of ETH relative to price movements and the volatility of  the cryptocurrency. We find that large ETH holders tend to increase their ETH holdings prior to a price increase, while small ETH holders tend to reduce their ETH holdings prior to a price increase. In other words, ETH returns tend to move in the direction that benefits crypto “whales” while reducing returns (or increasing loss) to “minnows.” Additionally, we find that the volatility of ETH returns seems to be driven by small retail investors rather than by the crypto whales. 

Abstract: There exists an increasing number of methods of estimating stochastic volatility from price returns within the financial econometrics literature. Combining results from different models has proved to be beneficial in forecasting across fields in economics. In this paper we synthesize the work done in forecast combination with estimation methods for integrated volatility in high frequency financial data, as well as using machine learning methods to to estimate optimal volatility. We test for the utility and accuracy of our combined estimates by applying the Volatility Feedback Effect, which highlights the negative relation between volatility and returns, as well as in Monte-Carlo simulated data. The efficacy of combining estimators is compared by using economic criteria in the form of trading strategy profits from a strategy utilizing the negative relation outlined from the Volatility Feedback Effect. We find that combining methods of integrated volatility yields positive results for the stock returns analyzed. In particular, we observe a few things of interest. Volatility calculated at the 5-minute level is not always optimal for computing volatility, at least for the trading strategy used in this paper. The more useful volatility estimator, as well as frequency at which its estimated, varies amongst the stocks analyzed; there is no one-size-fits-all volatility estimation method. Combination volatility estimation is not always superior to the individual estimation methods, it does tend to produce a more useful estimate of volatility than the vast majority of other estimators, making it a consistently solid volatility estimation choice. Finally, there exists additional utility in estimating volatility via OLS and LASSO regression methods, implying machine learning algorithms may yet find a place in the volatility literature.

Abstract: Though the use of cryptocurrencies as means of transaction may never occur, cryptocurrencies remain a largely traded digital asset. Part of the reason for the lack of adoption as a method of payment is the large volatility digital assets tend to experience. Cryptocurrency volatility is by no means homogenous, and has the potential to vary across exchanges.