Digital Finance

Most existing layer-1 blockchain protocols are based on proofs of work and proofs of stake. Those protocols are used to determine which miners or validators will be entitled to update the distributed ledger and earn the fees associated with the transactions in the mined blocks. More research is needed to determine which protocol would ensure the long term sustainability of blockchain and what is the best mechanism design for this protocol. See Proof-of-Work Cryptocurrencies: Does Mining Technology Undermine Decentralization?.

Modern blockchains enable the use of smart contracts to implement a broad range of financial services. Those services include borrowing/lending, derivative trading, insurance, and decentralized exchanges. Despite the potential gains of such systems, the adoption of DeFi (decentralized finance) exchanges will present a number of challenges and the need for guardrails. One example is the creation of arbitrage losses for investors trading in blockchain-based DEXs. See The Adoption of Blockchain-based Decentralized Exchanges.

A fundamental problem of second-generation blockchains, which support a broad range of financial services well and beyond cryptocurrency payments, is information leakage. This problem arises due to the public observability of transactions, which can be frontrun by malicious arbitrageurs while pending in the mempool. Frontrunning attacks not only lead to financial losses for traders of the DeFi ecosystem, but also congest the network and decrease the aggregate value of blockchain stakeholders. At the same time, frontrunning opportunities may benefit validators, who extract value from high transaction-fee bidding frontrunners. This is the so-called validator extractable value problem. The adoption of private communication channels depends upon the incentives provided to the users of the DeFi ecosystem. See The Evolution of Blockchain: From Public to Private Mempools.

How would informed traders trade if they have to publicly compete for execution? Decentralized exchanges (DEXs) provide an ideal laboratory to answer this question, as they require traders to publicly submit fees to prioritize their orders. We provide empirical evidence that informed traders do not bid low fees to hide their trading intentions. Rather, they publicly bid high fees to signal their information, even if private bidding is an option. Using a unique dataset of Ethereum mempool orders, we show that informed traders do so by employing a “jump bidding” strategy, where they place high initial bids to deter potential competitors.

We study Just-in-time (JIT) liquidity provision within blockchain-based decentralized exchanges (DEXs). Unlike passive liquidity providers (LPs) who deposit assets into liquidity pools before observing order flows, JIT LPs take a more active approach: they scan pending orders in blockchains' public mempools to swiftly provide liquidity for swap orders of their choice, only to withdraw their liquidity immediately after the swap is executed. Our game-theoretic analysis uncovers a paradoxical scenario: the presence of a JIT LP, rather than enhancing pool depth as expected, can inadvertently reduce it. A central reason behind the paradox is the adverse selection problem that passive LPs face due to the presence of informed arbitrageurs. The ability of JIT LPs to monitor the mempool prior to providing liquidity gives them a second-mover advantage that mitigates their adverse selection costs and potentially crowds out passive LPs, particularly when order flows are not highly elastic to changes in pool liquidity. These effects may lead to an overall reduction of pool depth and to an increased execution risk for traders. To alleviate the detrimental effects of JIT liquidity, we propose a two-tiered fee structure for passive and JIT LPs which transfers fees from the latter to the former. We show that this structure can prevent crowding out and improve welfare.

We analyze the run risk of USD-backed stablecoins. Stablecoin issuers aim to keep the stablecoin price at $1 by holding a portfolio of US dollar assets like bank deposits, Treasuries, and corporate bonds while promising to exchange stablecoins for $1 in cash with arbitrageurs. We show that asset illiquidity coupled with fixed redemption values reinstates panic runs among investors that only trade stablecoins in secondary markets with flexible prices. Importantly, run risk is exacerbated by more efficient arbitrage, implying a tradeoff between price stability and run risk. This is why stablecoin issuers only authorize a concentrated set of arbitrageurs despite the cost to price stability. Our findings are based on a model calibrated with a novel dataset on stablecoin arbitrage and trading activity. Our model predicts economically significant run risk for Tether (USDT) due to its liquidity transformation. But run risk is also sizeable for Circle (USDC) due to its less concentrated arbitrage. Finally, we show that issuing dividends to investors would effectively reduce run risk at both USDT and USDC, which points to a potential benefit of regulating stablecoins as securities.

We consider the impact of trading fees on the profits of arbitrageurs trading against an automated marker marker (AMM) or, equivalently, on the adverse selection incurred by liquidity providers due to arbitrage. We extend the model of Milionis et al. [2022] for a general class of two asset AMMs to both introduce fees and discrete Poisson block generation times. In our setting, we are able to compute the expected instantaneous rate of arbitrage profit in closed form. When the fees are low, in the fast block asymptotic regime, the impact of fees takes a particularly simple form: fees simply scale down arbitrage profits by the fraction of time that an arriving arbitrageur finds a profitable trade.

On centralized exchanges (CEXs) running a limit order book (LOB), market makers provide liquidity by actively submitting and re-pricing their quotes. Constant function market makers (CFMMs), the dominant mechanisms for decentralized exchanges (DEXs) on the blockchain, allow a new form of passive liquidity provision: liquidity providers (LPs) contribute assets to CFMM reserves that are subsequently available for trade with liquidity takers, at quoted prices that are algorithmically set. How is the return of providing liquidity on CFMMs determined? See Automated Market Making and Loss-Versus-Rebalancing.

Demand for blockchains such as Bitcoin and Ethereum is far larger than supply, necessitating a mechanism that selects a subset of transactions to include “on-chain” from the pool of all pending transactions. The idiosyncrasies of public blockchains require rethinking mechanism design from first principles, and in particular new notions of incentive-compatibility. Such blockchain-aware mechanism design played an important role in the evolution of Ethereum’s transaction fee mechanism, and in particular the adoption of EIP-1559. See Transaction Fee Mechanism Design.

In many settings, blockchain technology offers a decentralized alternative to a centralized service. However, decentralization often comes at an enormous efficiency cost; for example, chains like Bitcoin and Ethereum are very slow and expensive to operate when compared to their centralized counterparts. Understanding the economics of decentralization protocols—in other words, how prices and fees are set, how service is allocated, how infrastructure is provisioned, and related questions—is a core component in minimizing this efficiency cost. See Monopoly without a Monopolist: An Economic Analysis of the Bitcoin Payment System.

Compared with existing payment systems, Bitcoin’s throughput is low. Designed to address Bitcoin’s scalability challenge, the Lightning Network (LN) is a protocol allowing two parties to secure bitcoin payments and escrow holdings between them. In a lightning channel, each party commits collateral towards future payments to the counterparty and payments are cryptographically secured updates of collaterals. The network of channels increases transaction speed and reduces blockchain congestion. See Lightning Network Economics: Channels.

The growing adoption of smart contracts and blockchains brings new security risks that can lead to huge monetary losses. Billions of dollars worth of crypto assets have been stolen due to program errors and security vulnerabilities in smart contracts and blockchain systems. More research is needed to provide the correctness and security guarantees for blockchain programs according to their specifications with a reasonable effort, and more importantly, such guarantees should be machine-checkable without the need to trust any third party. See SciviK: A Versatile Framework for Specifying and Verifying Smart Contracts.

Permissioned blockchains are being used in number of contexts, including supply chains and other related industries. There is, at present, the lack of a framework for understanding the incentives of participants of a permissioned blockchain. Is adoption of blockchain socially beneficial and will such adoption arise in equilibrium? Our research has found that blockchain unequivocally benefits consumers, but that gains for the manufacturing sector are competed away. See Economics of Permissioned Blockchain Adoption.

Blockchain, and distributed ledger technology (DLT) more generally, have the potential for radically changing how securities markets operate, which would in turn dramatically alter how these markets should or should not be regulated. Interviews with about 100 persons who play prominent roles making these markets work or regulating them reveal a wide range of views on how DLT should or will affect the markets and their structure. While many highlighted the scope for dramatic cost reductions, others expressed skepticism, while still others questioned appetites for making DLT-based changes. See Distributed Ledger Technology and the Securities Markets of the Future. 

With so much investor-salient data coming in the form of text, our research aims at applying natural language processing techniques to large data sets to extract information. Our research has studied how equity, credit, rate, and currency markets respond to and influence news, earnings calls, and central bank communications. We have built models to investigate micro- and macro-efficiency of financial markets and the dynamics of information production. In future work, we plan to study how soft information (such as that obtainable from text corpora) can improve our understanding of price formation and the evolution of crypto and blockchain ecosystems. See Investor Information Choice with Macro and Micro Information and How News and Its Context Drive Risk and Returns Around the World.