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Research Interest

My research interest lies in the intersection of monetary theory, crypto economics and decentralized mechanism design.

Yulin Liu, Yuxun Lu, Kartik Nayak, Fan Zhang, Luyao Zhang and Yinhong Zhao

The ACM Conference on Computer and Communications Security

Transaction fee mechanism (TFM) is an essential component of a blockchain protocol that can fundamentally affect transaction fee dynamics, user experience, and security. However, a systematic evaluation of the real-world impact of TFMs is still absent. Using rich data from the Ethereum blockchain, mempool, and exchanges, we study the effect of EIP-1559, one of the first deployed TFMs that depart from the traditional first-price auction paradigm. We conduct a rigorous and comprehensive empirical study to examine its causal effect on blockchain transaction fee dynamics, transaction waiting time, and security. Our results show that EIP-1559 improves the user experience by making fee estimation easier, mitigating intra-block difference of gas price paid, and reducing users' waiting times. However, EIP-1559 has only a small effect on gas fee levels and consensus security. In addition, we found that when Ether's price is more volatile, the waiting time is significantly higher. We also verify that a larger block size increases the presence of siblings. These findings suggest new directions for improving TFM.

Yulin Liu, Luyao Zhang and Yinhong Zhao

Nature: Scientific Data

Harvard Dataverse

Bitcoin is a peer-to-peer electronic payment system that has rapidly grown in popularity in recent years. Usually, the complete history of Bitcoin blockchain data must be queried to acquire variables with economic meaning. This task has recently become increasingly difficult, as there are over 1.6 billion historical transactions on the Bitcoin blockchain. It is thus important to query Bitcoin transaction data in a way that is more efficient and provides economic insights. We apply cohort analysis that interprets Bitcoin blockchain data using methods developed for population data in the social sciences. Specifically, we query and process the Bitcoin transaction input and output data within each daily cohort. This enables us to create datasets and visualizations for some key Bitcoin transaction indicators, including the daily lifespan distributions of spent transaction output (STXO) and the daily age distributions of the cumulative unspent transaction output (UTXO). We provide a computationally feasible approach for characterizing Bitcoin transactions that paves the way for future economic studies of Bitcoin.

Hans Gersbach, Yulin Liu, and Martin Tischhauser

We examine forward guidance when an economy faces negative natural real interest rates and subsequent supply shocks. We introduce two versatile designs: escaping and switching. In the former, the central bank escapes low interest-rate commitment when inflation reaches a self-chosen threshold. In the latter, the central bank can switch from interest-rate forecasts to inflation forecasts any time. Central bankers are scrupulous and face intrinsic (or extrinsic) costs when they deviate from their policy announcements in the future. We show that switching forward guidance is preferable over escaping forward guidance if and only if negative real interest rate shocks are moderate. Furthermore, with the polynomial chaos expansion method and Sobol’ Indices, we identify the decisive parameters and show that our findings are globally robust to parameter uncertainty.

Hans Gersbach, Volker Hahn, and Yulin Liu

We examine “Forward Guidance Contracts,” which penalize central bankers for choosing high interest rates. We integrate those contracts into the New Keynesian Framework and show that they can be used to overcome a liquidity trap. Moreover, although the government takes only a share of the social benefits into account when it has to decide whether to offer the contract, we demonstrate that for plausible parameter values the government will always find it desirable to offer the contract in a liquidity trap but not in normal times. Finally, we show that the optimal duration of such contracts is typically very short.

Yulin Liu and Luyao Zhang

Currently, there are no convincing proxies for the fundamentals of cryptocurrency assets. We propose a new market-to-fundamental ratio, the price-to-utility (PU) ratio, utilizing unique blockchain accounting methods. We then proxy various fundamental-to-market ratios by Bitcoin historical data and find they have little predictive power for short-term bitcoin returns. However, PU ratio effectively predicts long-term bitcoin returns. We verify PU ratio valuation by unsupervised and supervised machine learning. The valuation method informs investment returns and predicts bull markets effectively. Finally, we present an automated trading strategy advised by the PU ratio that outperforms the conventional buy-and-hold and market-timing strategies. We distribute the trading algorithms as open-source software via Python Package Index for future research.

Luyao Zhang, Xinshi Ma and Yulin Liu

Blockchain empowers a decentralized economy by enabling distributed trust in a peer-to-peer network. However, surprisingly, a widely accepted definition or measurement of decentralization is still lacking. We explore a systematization of knowledge (SoK) on blockchain decentralization by reviewing existing studies on various aspects of blockchain decentralization. First, we establish a taxonomy for analyzing blockchain decentralization in the five facets of consensus, network, governance, wealth, and transaction. We find a lack of research on the transaction aspects that closely characterize user behavior. Second, we apply Shannon entropy in information theory to propose a decentralization index for blockchain transactions. We show that our index intuitively measures levels of decentralization in peer-to-peer transactions by simulating blockchain token transfers. Third, we apply our index to empirically analyze the dynamics of DeFi token transfers. Intertemporally, we observe that levels of decentralization converge regardless of the initial levels of decentralization. Comparison of DeFi applications shows that exchange and lending are more decentralized than payment and derivatives. We also discover that a greater return of ether, the native coin of the Ethereum blockchain, predicts a greater decentralization level in stablecoin transfer that includes ether as collateral. Finally, we develop future research directions to explore the interactions between different facets of blockchain decentralization, the design of blockchain mechanisms that achieve sustainable decentralization, and the interplay of decentralization levels and economic factors.

Yulin Liu and Luyao Zhang

Centralized monetary policy, leading to persistent inflation, is often inconsistent, untrustworthy, and unpredictable. Algorithmic stablecoins enabled by blockchain technology are promising in solving this problem. Algorithmic stablecoins utilize a monetary policy that is entirely rule-based. However, there is little understanding of how to optimize the rule. We propose a model that trade-off the price for supply stability. We further study the comparative statics by varying several design features. Finally, we discuss the empirical implications for designing stablecoins by the private sector and Central Bank Digital Currency (CBDC) by the public sector.

Hans Gersbach, Volker Hahn, and Yulin Liu 

We integrate banks and the coexistence of bank and bond financing into an otherwise standard New Keynesian framework. There are two policy-makers: a central banker, who can decide on short-term nominal interest rates, and a macroprudential policy-maker, who can vary aggregate capital requirements. The two policy instruments can be used to stabilize shocks, to moderate bank credit cycles, and to induce a more efficient allocation of resources across sectors. Moreover, we investigate the optimal combination of simple policy rules for interest rates and capital requirements. The optimal policy rules imply that the central bank should focus exclusively on price stability and the macroprudential policy-maker should react exclusively to changes in loan rate premia.

Referee Activities

International Journal of Central Banking, Review of World Economics, Macroeconomic Dynamics, Journal of Economic Dynamics and Control

Invited Talks & Conferences

Duke CS+ & SciEcon Symposium “Decentralized Finance: Cryptocurrency and Blockchain on the Internet Computer,” Aug. 5, 2021 (panelist)
Algorithmic Monetary Policy, Econ 204: Intermediate Macroeconomics by Prof. Luyao Zhang, Duke Kunshan University, Dec. 10, 2020 (invited guest speaker)
WEAI 95th Annual Conference, Denver, U.S.A, Jun. 26–30, 2020 (online presentation)
11th Swiss Winter Conference on Financial Intermediation, 2018 (presenter)
4th RES Symposium of Junior Researchers, Royal Economic Society, 2018 (presenter)
1st RCEA Warsaw Money-Macro-Finance Conference, 2018 (presenter)
China International Conference in Macroeconomics, Tsinghua University, 2018 (presenter)
The Rimini Conference in Economics and Finance 2018 (chair, presenter and discussant)
Research Seminars, Bank of Finland, 2017 (invited talk)
Fed St.Louis-JEDC-SCG-SNB-UniBern Conference, Swiss National Bank, 2017 (participant)
Workshop “Monetary Policy in Non-standard Times”, European Central Bank, 2017 (participant)
Annual Congress, European Economic Association, 2017 (presenter)
Annual Congress, German Economic Association, 2017 (presenter)
Journal of Monetary Economics and Swiss National Bank Conference, 2016 (participant)
9th Swiss Winter Conference on Financial Intermediation, 2016 (presenter)
Annual Congress, European Economic Association, 2016 (presenter)
Annual Congress, German Economic Association, 2016 (presenter)
Annual Congress, Swiss Society of Economics and Statistics, 2016 (presenter)

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