Job Market Paper

Do Connections Pay Off in the Bitcoin Market?
SQL and R source code download

Abstract I study the trading behavior of investors in this relatively new and unregulated bitcoin market. By parsing transaction data from the bitcoin blockchain, I search for addresses that are connected based on their trading behavior and identify the bit- coin investor network. I find that, from June 2016 to May 2019, addresses that are connected in the network earn 20.75% higher return than their unconnected peers on average. Furthermore, the return difference also exists among these connected ad- dresses. By dividing the connected addresses into ten groups I find the addresses in the top two groups earn higher returns than the rest connected addresses. Among the addresses inside the top two groups, I find that, compared with degree centrality, higher eigenvector centrality is a more related indicator to higher returns.

Download paper here


Price dispersion in bitcoin exchanges
Published in Economics Letters, September 2020

Abstract Bitcoin is traded in a number of exchanges, and there is a large and time-varying price dispersion among them. We identify the sources of price dispersion using a standard time-varying vector autoregression model with stochastic volatility, and we find that shocks to transaction fees and bitcoin price growth explain on average 20%, and sometimes more than 60%, of the variation of price dispersion.

Download paper here

The Market for Bitcoin Transactions
Published in Journal of Internantional Financial Market, Institution & Money, January 2021

Abstract Transaction fees in the bitcoin system work differently from those in conventional payment systems due to the design of the bitcoin mining algorithm. In particular, transaction fees and transaction volume in the bitcoin system increase whenever the network is congested, and our VAR results confirm that is indeed the case. To account for the empirical findings, we build a model where users and miners together determine transaction fees and transaction volume. Even though the mechanism of fluctuating transaction fees in bitcoin introduces an extra cost of uncertainty to users, a back-of-envelope calculation shows that the cost of using the bitcoin network for transactions is still smaller than the cost of using the current conventional payment system with a fixed transaction fee rate. However, this calculation may underestimate the cost due to the crowding-out effect on small transactions during the congested period.

Download paper here

Working Paper

The Impact of Stay-at-Home Orders on US Output: A Network Perspective
Available on SSRN, April 2020, Submitted

Abstract Under the stay-at-home orders issued by states, economic activities are reduced or put on hold by some states across the U.S. to control the spread of COVID-19. By combining several sources of data, we estimate the output loss due to such restrictions using a network approach. Based on our most conservative estimates, the measures as of April 15, 2020 reduce 26% of total US output per period, and about 43% of which is due to the input-output connections in the production network. Using a SIR model with an inter-state infection network, we also calculate the cost of reducing each infection to be approximately 150,000 dollars during the period of March 19 to April 15, 2020. Simulation results of various hypothetical stay-at-home orders show that the unit cost of infection reduction of the existing order is about 13% higher than the local minimum.

Download paper here