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, Harrison Waldon



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Next talks


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.


8 October 2024 @1pm BST (Online)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.



Previous talks

14 May 2024 @1pm BST (In person)Roel Oomen (Deutsche Bank)

Hedging of Fixing Exposure

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.