The seminar
The seminar is either held in person at the Oxford-Man Institute or broadcasted online via Zoom; see details of each talk below.
Organisers: Álvaro Cartea, Patrick Chang, Fayçal Drissi, Christopher Hyland, Shifan Yu
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Next talks
19 November 2026 @1pm BST (In Person) – Jiahua Wu (Imperial College London)
Title: Pricing AI as a Service (Jiahua Wu, Ilan Lobel, Yanwei Sun)
Abstract: We study AI as a Service, in which a monopolistic provider sells AI capabilities that perform tasks for downstream buyers. Buyers privately differ in both their value from the service and the probability that the AI resolves their task. The seller designs a menu of capabilities and outcome-contingent prices and controls how true task outcomes are mapped into reported outcomes; buyers may dispute and verify the reported outcome at a cost. We first compare two pricing formats commonly used in practice: usage-based pricing (UBP), under which the buyer pays per use regardless of the outcome, and pay-for-success pricing (PFS), under which the buyer pays only when the deployed AI reports the task as successful. When the AI-reported outcome fully aligns with the buyer’s task outcome, PFS becomes more attractive as per-success values become less heterogeneous and higher per-success values coincide with higher success probabilities. In a customer-service application, where an AI front end resolves requests before they join the human service queue, PFS is favored when AI capability and human service are complements, and UBP is favored when they are substitutes. Allowing the seller to choose the billable success metric can further increase PFS profit. When buyer verification is sufficiently costly, a single success payment combined with an appropriately chosen metric can reproduce the fully aligned limited-liability two-price benchmark, so optimal PFS weakly dominates UBP. Our main result shows that this misalignment motive disappears in the unrestricted contract space: separate success and failure prices make full outcome alignment without loss of optimality and eliminate dispute waste. We characterize the optimal mechanism, which reduces to a convex program under mild conditions: the lowest- and highest-success-probability types receive first-best allocations, while interior types may be distorted upward or downward according to curvature virtual types. These results extend to settings with more than two outcomes.
Bio: Jiahua Wu is an Associate Professor of Operations at Imperial Business School, Imperial College London. His research examines how pricing, information, and other incentives shape performance in markets and operations. Recent projects focus on technology-enabled markets, including online platforms and AI-enabled services.
24 November 2026 @1pm BST (In Person) – Daniel Ferreira (LSE)
Title: Artificial Intelligence in the Boardroom (Daniel Ferreira and Jin li)
Abstract: We analyze how the use of artificial intelligence affects the monitoring and advisory relationship between CEOs and corporate boards. AI can serve as a private advisor to CEOs, partially substituting for board advice, and thus reducing CEOs’ incentives to share information with directors. In equilibrium, firms respond by reducing board independence to restore information flows. Lower monitoring intensity decreases CEO turnover, making CEOs more entrenched. Lower dismissal risk allows firms to reduce CEO compensation. We show that AI adoption by the board mitigates some of the negative aspects of the CEO’s AI use, but cannot restore efficient levels of board monitoring. Our analysis suggests that the adoption of AI in the boardroom may have unintended consequences for corporate governance.
Bio: Daniel Ferreira is Professor of Finance at LSE and Fellow of the European Corporate Governance Institute. He is also a department editor of Management Science, a council member of the Society for Financial Studies, a research fellow at CEPR, and an associate editor of The Economic Journal. He served as Head of the Department of Finance from 2020 to 2023. Daniel’s research lies at the intersection of corporate finance and organisational economics, with a particular focus on corporate governance. He has been at LSE since 2006 and holds a PhD in Economics from The University of Chicago.
Previous talks
5 May 2026 @2pm BST (In Person) – Harjoat Bhamra (Imperial College London)
Title: Leverage Dynamics and Learning about Economic Crises (joint with Artur Anschukov and Lars-Alexander Kuehn)
Abstract: Models of learning about economic crises generate risk premia that rise at the onset of a crisis but then fall as belief uncertainty fades. Yet, empirical risk premia remain elevated during crises. We resolve this tension with leverage dynamics generated by the impact of learning on optimal capital structure decisions within a representative agent consumption-based model. Optimal leverage creates a feedback effect: learning increases risk premia, thereby depressing prices and further raising leverage. We structurally estimate the model and show it closely matches the joint dynamics of consumption, equity risk premia, credit risk, and leverage, especially during crises.
Bio: Harjoat Bhamra is a Professor of Finance at Imperial College Business School. Harjoat’s research focuses on dynamic models on corporate investment, capital structure, macro-finance, and monetary policy. He has published in leading journals such as American Economic Review, Review of Financial Studies, Journal of Economic Theory, and Journal of Monetary Economics. He is an Associate Editor for Management Science. Harjoat earned his PhD at London Business School. Website: https://www.harjoatbhamra.com/
30 April 2026 @2pm BST (In Person) – Yacine Aït-Sahalia (Princeton University)
3 March 2026 @11am GMT (In Person) – Nancy Xu (Boston College)
Forecasting International Stock Market Variances
Abstract: We first examine 320 different forecasting models for international monthly stock return volatilities, using high frequency realized variances and the implied option variance as the predictor variables. We evaluate linear and non-linear models, and logarithmic transformed and weighted least squares estimation approaches. A logarithmically transformed Corsi (2009) model combined with the option implied variance (“lm4 log”) is robustly, across countries and time, among the best forecasting models. This conclusion survives when estimating a multitude of alternative models, including panel and MIDAS models, models embedding quarticity, leverage, downside, and jump risk. While not always the very best, the “lm4 log” model always generates forecasts extremely highly correlated with the very best model for a particular sample.
Bio: Nancy R. Xu is an Assistant Professor of Finance at the Boston College (Carroll School of Management), a Research Affiliate of Centre for Economic Policy Research (CEPR), and a Visiting Scholar at the Federal Reserve Bank of Boston. Her research lies at the intersection of asset pricing, macro-finance, and political economy, with a recent focus on how fiscal institutions, policy uncertainty, and political frictions shape risk premia and capital allocation. She has also served as a research consultant to a European Central Bank and previously interned at the Federal Reserve Bank of New York. She will join University of Cambridge Judge Business School as an Associate Professor in Summer 2026.
24 February 2026 @4pm GMT (In Person) – Pedro Bordalo (Saïd Business School, University of Oxford)
Memory and Expectations
Abstract: We address the joint hypothesis problem in cross-sectional asset pricing by using measured analyst expectations of earnings growth. We construct a firm-level measure of Expectations Based Returns (EBRs) that uses analyst forecast errors and revisions and shuts down any cross-sectional differences in required returns. We obtain three results. First, variation in EBRs accounts for a large chunk of cross-sectional return spreads in value, investment, size, and momentum factors. Second, time variation in these spreads is predictable from that in EBRs, holding constant scaled price variables (as proxies for time varying required returns). Third, firm characteristics often seen as capturing risk premia predict disappointment of expectations and low EBRs. Overall, return spreads typically attributed to exotic risk factors are explained by predictable movements in non-rational expectations of firms’ earnings growth.
18 February 2026 @4pm GMT (In Person) – Martin Herdegen (University of Warwick)
Portfolio Optimisation under Transaction Costs with Recursive Preferences
Abstract: We study the optimal investment-consumption problem for an agent whose preferences are governed by Epstein-Zin stochastic differential utility and who invests in a constant-parameter Black-Scholes-Merton market. We assume that purchases and sales of the risky asset are subject to proportional transaction costs. We fully characterise all parameter combinations for which the problem is well posed (which may depend on the level of transaction costs). We also provide a full verification argument that relies on no additional technical assumptions and uses primary methods only. Even in the special case of power utility, our arguments are significantly simpler and more elegant than the results in the extant literature. A novel key idea is to parametrise consumption and the value function in terms of the shadow fraction of wealth. The talk is based on joint work with David Hobson and Alex Tse.
13 October 2025 @4pm BST (In Person) – Nan Chen (The Chinese University of Hong Kong)
Optimal Bonding Function in Decentralized Exchanges
Abstract: Decentralized exchanges operate on automated market markers to provide liquidity in a passive way, subject to both stale price arbitrage and trading reverse arbitrage. In this paper, we combine calculus of variation and convex optimization techniques to investigate the optimal design problem for bonding functions, a centerpiece of DEX mechanism, in the presence of these two types of arbitrages. Economic implications of our results are also discussed.
joint work with Agostino Capponi (Columbia), Ling Qin (Shanghai Tech University), and Zhou Yang (South China Normal University)
Bio: Professor Chen Nan graduated from the Department of Probability and Statistics at Peking University in 1998, and he received his M.Sc. degree in Probability and Statistics in 2001 at Peking University, his M.Phil. and Ph.D. degrees in 2006 at Columbia University, USA. He joined the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong in 2006. He served as associate editor for Operations Research Letters from 20072008. He is now an associate editor of International Review of Finance, Digital Finance and has chaired/been a member of the program committees of several international conferences on quantitative finance and Monte Carlo simulation.
Prof. Chen now serves as director of the Bachelor of Engineering Program in Financial Technology at CUHK. The program is the first of its kind in Hong Kong to offer comprehensive undergraduate education in FinTech. He is also director of Master of Science Program in Financial Engineering at CUHK Shenzhen.
1 May 2025 @4pm BST (In Person) – Winston Dou (Wharton School, University of Pennsylvania)
The Cost of Intermediary Market Power for Distressed Borrowers
Abstract: In the loan markets for distressed corporate borrowers, a few specialized lenders finance a large fraction of loans. Ultra-high yield spreads prevail even after removing the credit- and liquidity-risk component. Borrowers are in desperate need of financing but face limited funding options, while specialized lenders have repeated syndication relations with restrained participation. We develop and estimate a dynamic game-theoretic model, accounting for strategic competition, endogenous collusion capacity, endogenous participation, and latent heterogeneity. Lender market power accounts for 74 – 92% of the risk-adjusted yield spreads, with a significant fraction attributable to collusion. Smaller borrowers are more susceptible to lender market power. Importantly, both specialized lenders and distressed borrowers would be worse off if collusion is completely prohibited, suggesting that vigorous antitrust policies can be efficiency retarding.
Bio: Professor Winston (Wei) Dou is an Assistant Professor of Finance and the Golub Faculty Scholar at the Wharton School, University of Pennsylvania. He is a financial economist specializing in asset pricing, capital markets, industrial organization (IO), macroeconomics, and econometrics. His research primarily focuses on asset pricing and capital markets, integrating concepts, insights, modeling, and empirical tools from IO, macroeconomics, and econometrics. Professor Dou is dedicated to developing innovative theoretical models and establishing strong connections between these models and data, with a particular emphasis on model robustness.
3 March 2025 @1pm GMT (In Person) – Markus K. Brunnermeier (Princeton University)
Optimal (Un)Conventional Monetary Policy
Abstract: We study the optimal joint interest rate and central bank balance sheet policies in a macro model with financial sector, sticky prices, aggregate and idiosyncratic risk. Minimizing the output gap requires the central bank to condition its interest rate policy on past QE. Previous central bank balance sheet expansions require more aggressive interest rate policy going forward. Risk and consumption allocation efficiency calls for a preparatory balance sheet policy that mediates the redistributive role of subsequent interest rate moves. These two objectives jointly pin down optimal interest rate and balance sheet policies.
Bio: Markus K. Brunnermeier is the Edwards S. Sanford Professor in the economics department at Princeton University and director of Princeton’s Bendheim Center for Finance. His research focuses on international financial markets and the macroeconomy with special emphasis on bubbles, liquidity, financial and monetary price stability, and digital money. In 2020, at the outbreak of Covid, he established a webinar series.
11 February 2025 @1pm GMT (In Person) – Emilio Barucci (Politecnico di Milano)
The Adoption of Central Bank Digital Currency
Abstract: We investigate the adoption of Central Bank Digital Currency (CBDC) in a partial equilibrium setting, focusing on four key features: remuneration, incentives, interoperability, and architecture. Agents are interested in adopting CBDC because they have a preference for digital payments, and the Central Bank may incentivize adoption through subsidies and platform development, potentially in collaboration with the private sector. Our results demonstrate that both the remuneration scheme and monetary incentives significantly influence the adoption rate and transaction volume. Calibrating the model for the Digital Euro case study, we show that the target of 60\% of the population using CBDC can be achieved, provided that platform productivity is substantially enhanced. While this target is feasible within a one layer architecture, it becomes significantly more challenging in a two layers setting. CBDC is likely to win the adoption battle against stablecoins but is likely to lose it against debit cards.
Bio: Emilio Barucci is a Professor of mathematical finance at the Department of Mathematics of Politecnico di Milano. He is head of QFinLab and of the Quantitative Finance group. He holds a degree in Economics from the University of Florence. His research interests include: banking system, financial markets, privatizations, corporate governance, macroeconomics, quantitative finance, asset management, fintech. He authored over seventy publications and ten volumes. He is the director of the Quantitative Finance and of the International Master in Fintech both at GSOM.
28 October 2024 @3pm BST (In Person) – Aleksey Kolokolov (University of Manchester)
BUMVU Estimators
Abstract: We provide necessary and sufficient conditions for an (Unbiased) Block estimator to have Uniformly Minimum Variance. Our theory parallels the theory of UMVU estimation, the main novel insight being the focus on the covariance among blocks. We use this theory to derive lower variance bounds for block estimators of functionals of high-frequency volatility when the block size is fixed. We further show the relevance of the new theory for the classical problem of estimation of homoskedastic nonparametric regressions with varying mean. Finally, we introduce a new test for the presence of drift in financial data which exploits the precision of BUMVU estimators. The test shows abundant presence of drift in financial data.
Bio: Aleksey Kolokolov is an Assistant Professor in Finance at Alliance Manchester Business School, University of Manchester. He holds a PhD in Econometrics and Empirical Economics at the University of Rome “Tor Vergata”, Italy. His research focuses on advanced statistical methods for analysing high-frequency financial data, with a particular emphasis on modelling jumps, flash crashes, price staleness, and forecasting. His work has been published in leading journals in finance and econometrics, including the Journal of Financial Economics, Management Science, and Journal of Econometrics, etc.
8 October 2024 @1pm BST (In Person) – Andrea Roncoroni (Essec Business School)
Operational Resilience Anew
Abstract: Extreme events, such as pandemics or geopolitical crises, may lead to unexpected and sudden disruptions in the global supply chain. This reality may necessitate prompt and significant adjustments in physical operations (e.g., reshoring, nearshoring). Empirical evidence indicates that while many companies recognize this necessity, far fewer actually implement or are capable of doing so. In such contexts, academic research provides a range of operational tools to foster operational resilience. We propose a novel purely financial instrument to enable firms to enhance their operational resilience. Our approach is normative in nature. We develop, analyze, and evaluate it using two integrated risk management (IRM) models. One model is a stylized newsvendor model featuring a risk-averse decision maker contending with an unreliable supplier, where reliability diminishes following a supply chain disruption. We derive our IRM policy and empirically assess its effectiveness in bolstering operational resilience. The other model is a multinational capacity allocation model encompassing both reliable and unreliable suppliers. We calibrate it using historical gas market data and evaluate the resulting recommendations in light of the European gas supply chain disruption subsequent to the Russian attack on Ukraine. Our findings reveal that: i. (Operational contraction) A reduction in reliability prompts a decrease in optimal ordering/allocation; ii. (Financially-driven resilience) IRM can mitigate this decrease compared to mere operational management, and: iii. (All-weather feature) Traditional single-claim hedging offers uneven mitigation across reliability reductions compared to utilizing a combined custom hedge. In summary, our analysis indicates that the presence of an optimal IRM strategy mitigates the need for relocation, thereby offering managers a new avenue to strengthen operational resilience.
Bio: Andrea Roncoroni is a Professor of Financial Engineering at ESSEC Business School (Paris), Visiting Fellow at Bocconi University (Milan), and Director of the Energy and Commodity Finance (ECOMFIN) Research Center.
His research work deals with stochastic modeling, risk management, and asset allocation, with a focus on nonfinancial firms and commodity markets.
11 June 2024 @1pm BST (In person) – Basil Williams (Imperial College Business School)
Model Secrecy and Stress Tests
Abstract: Should regulators reveal the models they use to stress-test banks? In our setting, revealing leads to gaming, but secrecy can induce banks to underinvest in socially desirable assets for fear of failing the test. We show that although the regulator can solve this underinvestment problem by making the test easier, some disclosure may still be optimal (e.g., if banks have high appetite for risk or if capital shortfalls are not very costly). Cutoff rules are optimal within monotone disclosure rules, but more generally optimal disclosure is single-peaked. We discuss policy implications and offer applications beyond stress tests.
Bio: Basil is an Assistant Professor in the Department of Finance at Imperial College Business School. He studies finance theory, with a focus on problems involving asymmetric information. His papers have examined banking, market microstructure, organizational economics, and incentive contracting.
14 May 2024 @1pm BST (In person) – Roel Oomen (Deutsche Bank)
Abstract: FX fixings are an indispensable and widely used reference rate in a market that trades continuously without an official close. Yet, a dealer’s handling of fix transactions is a much debated topic. Especially when exposure to the fix is large relative to available market liquidity and hedging may extend to the pre-fix window, an inherent conflict of interest can arise between dealer and client. In this paper we use a model with permanent and transient market impact to characterise a dealer’s optimal strategy to hedge fixing exposure. We show that smaller fix exposures are fully hedged over the calculation window, but that larger fix transactions are optimally hedged over a longer horizon that includes the pre-fix window. A client’s all-in transaction costs can be lowered by pre-fix hedging when transient impact decays sufficiently quickly and dominates permanent impact.
Bio: Roel is the head of QRD Lab for sales and trading at Deutsche Bank. He started his industry career as a quant in electronic cash equity trading in 2006, and subsequently held various roles in electronic FX spot trading, including co-head of the business. Roel holds a PhD in econometrics, is a senior research fellow at the London School of Economics, and has published widely on the econometric analysis of high frequency data and FX trading.
24 April 2024 @4pm BST (Online) – Joel Hasbrouck (New York University)
An Economic Model of a Decentralized Exchange with Concentrated Liquidity
Abstract: We provide an economic model of a decentralized exchange (DEX) that allows investors to concentrate liquidity within exogenously specified price intervals (e.g., Uniswap V3). We demonstrate that providing liquidity for a risky vs. risk-free asset pair within any price interval is analogous to investing in a portfolio composed of the risky asset and the risk-free asset subject to a further cost due to arbitrage trading against the DEX. The associated portfolio weights evolve dynamically such that the risky asset portfolio weight declines from unity to zero as the risky asset price increases. Moreover, DEX liquidity provision is always a sub-optimal investment in the absence of trading fees. In turn, we show that any level of positive trading fees supports a unique level of equilibrium liquidity provision for each DEX price interval. We provide a simple approximation of the equilibrium liquidity provision for each interval which can be useful for empirical work. We also demonstrate that liquidity provision to an interval provides a return approximately equivalent to a covered call trading strategy.
Bio: Joel Hasbrouck is the Kenneth G. Langone Professor of Business Administration and a Professor of Finance at the Stern School of Business, New York University. He specializes in market microstructure: the analysis, design and regulation of trading mechanisms for securities. He is the author of Empirical Market Microstructure (Oxford Press) and numerous articles. He is an Associate Editor at the Journal of Financial Econometrics, an Advisory Editor at the Journal of Financial Markets, and a Fellow of the Society of Financial Econometrics.
22 February 2024 @1pm [In person at OMI] – Bruno Biais (HEC)
Private vs public currency
Abstract: We analyze dynamic capital allocation and risk sharing between a principal and many agents, who privately observe their output.The state variables of the mechanism design problem are aggregate capital and the distribution of continuation utilities across agents.This gives rise to a Bellman equation in an infinite dimensional space, which we solve with mean-field techniques.We fully characterize the optimal mechanism and show that the level of risk agents must be exposed to for incentive reasons is decreasing in their initial outside utility.We extend classical welfare theorems by showing that any incentive-constrained optimal allocation can be implemented as an equilibrium allocation, with appropriate money issuance and wealth taxation by the principal.
Bio: Bruno Biais holds a Ph.D. in finance from HEC Paris, received the Paris Bourse dissertation award and the CNRS bronze medal. He taught at Toulouse, Carnegie Mellon, Oxford, and LSE. His research focuses on finance, contract theory and experimental economics. His latest research publications cover the topics of Blockchain and equilibrium Bitcoin pricing. In early 2020, Professor Bruno Biais was awarded an ERC Advanced Grant for his research project, entitled “Welfare, Incentives, Dynamics, and Equilibrium”.
24 January 2024 – Olga Klein (Warwick Business School)
Blockchain scaling and liquidity concentration on decentralized exchanges
Abstract: Liquidity providers (LPs) on decentralized exchanges (DEXs) can protect themselves from adverse selection risk by updating their positions more frequently. However, repositioning is costly, because LPs have to pay gas fees for each update. We analyze the causal relation between repositioning and liquidity concentration around the market price, using the entry of a blockchain scaling solution, Polygon, as our instrument. Polygon’s lower gas fees allow LPs to update more frequently than on Ethereum. Our results demonstrate that higher repositioning intensity and precision lead to greater liquidity concentration, which benefits small trades by reducing their slippage.
Bio: Olga is an Assistant Professor of Finance and a research fellow at the Gillmore Center of Financial Technology at Warwick Business School. Olga’s research interests are in the areas of market microstructure, algorithmic and high-frequency trading, liquidity, market efficiency and fintech.
08 November 2023 – Guillermo Angeris (Bain Capital Crypto)
The Geometry of Constant Function Market Makers
Constant function market makers (CFMMs) are the most popular type of decentralized trading venue for cryptocurrency tokens. In this paper, we give a very general geometric framework (or ‘axioms’) which encompass and generalize many of the known results for CFMMs in the literature, without requiring strong conditions such as differentiability or homogeneity. One particular consequence of this framework is that every CFMM has a (unique) canonical trading function that is nondecreasing, concave, and homogeneous, showing that many results known only for homogeneous trading functions are actually fully general. We also show that CFMMs satisfy a number of intuitive and geometric composition rules, and give a new proof, via conic duality, of the equivalence of the portfolio value function and the trading function. Many results are extended to the general setting where the CFMM is not assumed to be path-independent, but only one trade is allowed. Finally, we show that all ‘path-independent’ CFMMs have a simple geometric description that does not depend on any notion of a ‘trading history’.
Bio: Head of research at Bain Capital Crypto. During his Ph.D. (2019-2022) Guille worked on inverse design in the Nanoscale and Quantum Photonics lab with Prof. Jelena Vučković and Prof. Stephen Boyd. His thesis, Heuristics and bounds for photonic design, focused mostly on some applications of optimization to photonics. He also did his bachelor’s and master’s at Stanford, also in EE.
14 June 2023 – Andreas Park (University of Toronto)
Learning from DeFi: Would Automated Market Makers Improve Equity Trading?
We investigate the potential for automated market makers (AMMs) to be economically viable in and improve traditional financial markets. AMMs are a new type of trading institution that have emerged in the world of crypto-assets and process a significant portion of global crypto trading volume. The current trend of tokenizing assets, the legitimization of crypto-token issuance via the EU’s MiCA regulation, and the push by the S.E.C. to change the trading of retail orders presents an opportunity to consider AMMs for traditional markets. Our approach is to determine the parameters that would allow liquidity providers to profitably contribute to an AMM and calculate, based on U.S. equity trading data, if liquidity demanders would benefit from using the AMM for these parameters. Our analysis suggests that properly designed AMMs could save U.S. investors about 30% of annual transaction costs. The source for these savings is twofold: AMMs allow better risk sharing for liquidity providers and they use locked-up capital that otherwise sits idly at brokerages. The introduction of AMMs in traditional markets could particularly improve the liquidity and trading cost issues faced by small firms, allowing them to attract more investors and capital.
Bio: Andreas Park is a Professor of Finance at the University of Toronto, appointed to the Rotman School of Management and the Department of Management at UTM. He currently serves as the Research Director at the FinHub, Rotman’s Financial Innovation Lab, he is the co-founder of LedgerHub, the University of Toronto’s blockchain research lab.
17 May 2023 – Andrei Lyashenko (QRM, Illinois Institute of Technology)
Modeling Yield Curves with Factor HJM
We introduce a novel risk-neutral interest rate modeling framework based on the factor modeling approach widely used to model yield curves in real-world applications. The new modeling framework combines the simplicity, intuitiveness, and computational efficiency of the factor modeling approach with the no-arbitrage rigor of pricing term structure models. Its constructive nature makes it a convenient practical tool for model development and brings clarity and intuition to the yield curve modeling process.
Bio: Andrei Lyashenko is the head of Market Risk and Pricing Models at the Quantitative Risk Management (QRM), Inc. in Chicago. His team is responsible for research, implementation and support of pricing and risk models across multiple asset classes. In November 2019, he was awarded the prestigious Quant of the Year award, jointly with Fabio Mercurio from Bloomberg, L.P., for their Risk Magazine paper on modeling backward-looking rates.
03 May 2023 – Claudio Tessone (University of Zurich)
From self-organisation to rug pulls in UniSwap
UniSwap is the largest decentralised exchange (DEX) in terms of market capitalisation, and is widely recognised for introducing an Automated Market Maker (AMM) mechanism that provides liquidity and eliminates counterparty risk. However, UniSwap also poses several threats by itself due to its lack of regulation and operations scrutiny. Because of its decentralised nature, anyone can take two tokens with arbitrary supplies and pair them to form a liquidity pool (LP) on UniSwap; this LP allows the creator and other users to buy, sell or provide liquidity of any amount of tokens. These characteristics sometimes can lead to deceive and harm innocent users. In this research, we focus on extit{Rug-pull} attack and its effect on the liquidity of LPs and the whole Uniswap market, we detect Rug-pull events by retrieving and replaying each mint, swap, and burn event, and calculating the change ratio of the reserve of the primary tokens include WETH, USDT, USDC and DAI. Our results show that despite UniSwap being the largest and most popular DeFi platform, it still exhibits several anomalies in terms of liquidity, price, and attacks, including numerous LPs with extremely low total value locked (TVL) and tokens with extremely high or low prices, Rug-pull is a prevalent attack on Uniswap which not only directly causes many LP’s price and liquidity anomalies but also has a significant impact on the stability and connectivity of the liquidity pool network. We also analyse the striking statistical regularities of the Liquidity network in UniSwap, a novel construct that allows to foresee the existence of simple mechanisms that drive the growth and sustainability of this decentralised marketplace.
Bio: Claudio J. Tessone is Professor of Blockchain and Distributed Ledger Technologies at the Informatics Department, University of Zurich. He is co-founder and Chairman of the UZH Blockchain Center. He holds a PhD in Physics (on complex systems) and an Habilitation on Complex socio-economic systems from ETH Zurich.
26 April 2023 – Bhaskar Krishnamachari (USC Viterbi School of Engineering)
Dynamic Curves: Enhancing Decentralized Autonomous Exchanges
Cryptocurrency exchanges based on curve-based automated market makers have been a major development in decentralized finance (DeFi) because they enable efficient, secure, low-friction conversions between different cryptocurrencies. This research talk will explore the potential of dynamic AMM curves, optimal trading on such dynamic AMM curves, and the use of reinforcement learning to enhance AMMs. We will describe how dynamic curves enable autonomous exchanges to maintain a liquidity pool that continuously adjusts to the market price, and discuss how optimal trading policies can be derived for such a dynamic AMM. We will also discuss how a reinforcement learning agent can be used to optimize fees on automated market maker protocols.
Bio: Bhaskar Krishnamachari is a Professor of Electrical and Computer Engineering at the USC Viterbi School of Engineering. His research spans the design and evaluation of algorithms, protocols, and applications for wireless networks, distributed systems, and the internet of things.
15 February 2023 – Andre Rzym (Man Group)
Crypto, is the Alpha worth the Pain?
We describe our experience of, and observations on, the cryptocurrency space from the perspective of a regulated investment manager. We discuss the immaturity of the space, consider the impact of high volatility, and describe how the product development process is very different to other asset classes.
Bio: Partner and Portfolio Manager, Man AHL.
18 January 2023 – Agostino Capponi (Columbia University)
The Information Content of Blockchain Fees
Trading at decentralized exchanges (DEXs) requires traders to bid blockchain fees to determine the execution priority of their orders. We employ a structural vector-autoregressive (structural VAR) model to provide evidence that DEX trades with high fees not only reveal more private information, but also respond more to public price innovations on centralized exchanges (CEXs), contributing to price discovery. Using a unique dataset of Ethereum mempool orders, we further demonstrate that high fees do not result from traders competing with each other on private or public information. Rather, our analysis lends support to the hypothesis that they bid high fees to reduce the execution risk of their orders due to blockchain congestion.
Bio: Here
07 December 2022 – Francisco Marmolejo Cossio (Harvard University)
Differential Liquidity Provision in Uniswap V3 and Implications for Contract Design
Decentralized exchanges (DEXs) facilitate trading assets on-chain without the need of a trusted third party. Amongst these, constant function market maker DEXs such as Uniswap handle the most volume of trades between ERC-20 tokens. In Uniswap v3, liquidity providers (LPs) are given the option to differentially allocate liquidity to be used for trades that occur within specific price intervals. We formalize the profit and loss that LPs can earn in simplified trade dynamics and are able to compute optimal liquidity allocations for liquidity providers who hold fixed price trajectory beliefs. We use this tool to shed light on the design question regarding how v3 contracts should partition price space for permissible liquidity allocations. Our results suggest that a richer space of potential partitions can simultaneously benefit both LPs and traders.
Bio: Here
30 November 2022 – Ronnie Sircar (Princeton University)
Cryptocurrencies, Mining & Mean Field Games
We present a mean field game model to study the question of how centralization of reward and computational power occur in Bitcoin-like cryptocurrencies. Miners compete against each other for mining rewards by increasing their computational power. This leads to a novel mean field game of jump intensity control, which we solve explicitly for miners maximizing exponential utility, and handle numerically in the case of miners with power utilities. We show that the heterogeneity of their initial wealth distribution leads to greater imbalance of the reward distribution, or a “rich get richer” effect. This concentration phenomenon is aggravated by a higher bitcoin mining reward, and reduced by competition. Additionally, an advantaged miner with cost advantages such as access to cheaper electricity, contributes a significant amount of computational power in equilibrium, unaffected by competition from less efficient miners. Hence, cost efficiency can also result in the type of centralization seen among miners of cryptocurrencies.
Bio: RONNIE SIRCAR is the Eugene Higgins Professor of Operations Research and Financial Engineering (ORFE) at Princeton University, and is affiliated with the Bendheim Center for Finance, the Program in Applied and Computational Mathematics, and the Andlinger Center for Energy and the Environment. He received his doctorate from Stanford University, and taught for three years at the University of Michigan in the Department of Mathematics.
23 November 2022 – Fayçal Drissi (University of Oxford – Oxford-Man Institute)
Decentralised Finance and Automated Market Making: Predictable Loss and Optimal Liquidity Provision
Constant product markets with concentrated liquidity (CL) are the most popular type of automated market makers. In this paper, we characterise the continuous-time wealth dynamics of strategic LPs who dynamically adjust their range of liquidity provision in CL pools. Their wealth results from fee income and the value of their holdings in the pool. Next, we derive a self-financing and closed-form optimal liquidity provision strategy where the width of the LP’s liquidity range is determined by the profitability of the pool (provision fees minus gas fees), the predictable losses (PL) of the LP’s position, and concentration risk. Concentration risk refers to the decrease in fee revenue if the marginal exchange rate (akin to the midprice in a limit order book) in the pool exits the LP’s range of liquidity. When the marginal rate is driven by a stochastic drift, we show how to optimally skew the range of liquidity to increase fee revenue and profit from the expected changes in the marginal rate. Finally, we use Uniswap v3 data to show that, on average, LPs have traded at a significant loss, and to show that the out-of-sample performance of our strategy is superior to the historical performance of LPs in the pool we consider.
Bio: Fayçal Drissi is a postdoctoral researcher at the Oxford-Man Institute, University of Oxford. His research focuses on effectively combining elements of Mathematics and Machine Learning for financial decision problems in both traditional finance and decentralized finance, with a focus on optimal execution and market making. He holds a Ph.D. in Mathematics from Université Paris 1 Panthéon-Sorbonne. Prior to his doctoral studies, he worked for five years in the hedge fund industry, engaging in research and development related to derivatives pricing and systematic trading.