Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learning ArXiv ID: 2501.07508 “View on arXiv” Authors: Unknown Abstract This paper applies deep reinforcement learning (DRL) to optimize liquidity provisioning in Uniswap v3, a decentralized finance (DeFi) protocol implementing an automated market maker (AMM) model with concentrated liquidity. We model the liquidity provision task as a Markov Decision Process (MDP) and train an active liquidity provider (LP) agent using the Proximal Policy Optimization (PPO) algorithm. The agent dynamically adjusts liquidity positions by using information about price dynamics to balance fee maximization and impermanent loss mitigation. We use a rolling window approach for training and testing, reflecting realistic market conditions and regime shifts. This study compares the data-driven performance of the DRL-based strategy against common heuristics adopted by small retail LP actors that do not systematically modify their liquidity positions. By promoting more efficient liquidity management, this work aims to make DeFi markets more accessible and inclusive for a broader range of participants. Through a data-driven approach to liquidity management, this work seeks to contribute to the ongoing development of more efficient and user-friendly DeFi markets. ...

Pricing Quanto and Composite Contracts with Local-Correlation Models ArXiv ID: 2501.07200 “View on arXiv” Authors: Unknown Abstract Pricing composite and quanto contracts requires a joint model of both the underlying asset and the exchange rate. In this contribution, we explore the potential of local-correlation models to address the challenges of calibrating synthetic quanto forward contracts and composite options quoted in the market. Specifically, we design on-line calibration procedures for generic local and stochastic volatility models. The paper concludes with a numerical study assessing the calibration performance of these methodologies and comparing them to simpler approximations of the correlation structure. ...

Sequential Portfolio Selection under Latent Side Information-Dependence Structure: Optimality and Universal Learning Algorithms ArXiv ID: 2501.06701 “View on arXiv” Authors: Unknown Abstract This paper investigates the investment problem of constructing an optimal no-short sequential portfolio strategy in a market with a latent dependence structure between asset prices and partly unobservable side information, which is often high-dimensional. The results demonstrate that a dynamic strategy, which forms a portfolio based on perfect knowledge of the dependence structure and full market information over time, may not grow at a higher rate infinitely often than a constant strategy, which remains invariant over time. Specifically, if the market is stationary, implying that the dependence structure is statistically stable, the growth rate of an optimal dynamic strategy, utilizing the maximum capacity of the entire market information, almost surely decays over time into an equilibrium state, asymptotically converging to the growth rate of a constant strategy. Technically, this work reassesses the common belief that a constant strategy only attains the optimal limiting growth rate of dynamic strategies when the market process is identically and independently distributed. By analyzing the dynamic log-optimal portfolio strategy as the optimal benchmark in a stationary market with side information, we show that a random optimal constant strategy almost surely exists, even when a limiting growth rate for the dynamic strategy does not. Consequently, two approaches to learning algorithms for portfolio construction are discussed, demonstrating the safety of removing side information from the learning process while still guaranteeing an asymptotic growth rate comparable to that of the optimal dynamic strategy. ...

A Modern Paradigm for Algorithmic Trading ArXiv ID: 2501.06032 “View on arXiv” Authors: Unknown Abstract We introduce a novel framework for developing fully-automated trading model algorithms. Unlike the traditional approach, which is grounded in analytical complexity favored by most quantitative analysts, we propose a paradigm shift that embraces real-world complexity. This approach leverages key concepts relating to self-organization, emergence, complex systems theory, scaling laws, and utilizes an event-based reframing of time. In closing, we describe an example algorithm that incorporates the outlined elements, called the Delta Engine. ...

Exploratory Randomization for Discrete-Time Linear Exponential Quadratic Gaussian (LEQG) Problem ArXiv ID: 2501.06275 “View on arXiv” Authors: Unknown Abstract We investigate exploratory randomization for an extended linear-exponential-quadratic-Gaussian (LEQG) control problem in discrete time. This extended control problem is related to the structure of risk-sensitive investment management applications. We introduce exploration through a randomization of the control. Next, we apply the duality between free energy and relative entropy to reduce the LEQG problem to an equivalent risk-neutral LQG control problem with an entropy regularization term, see, e.g. Dai Pra et al. (1996), for which we present a solution approach based on Dynamic Programming. Our approach, based on the energy-entropy duality may also be considered as leading to a justification for the use, in the literature, of an entropy regularization when applying a randomized control. ...

Heath-Jarrow-Morton meet lifted Heston in energy markets for joint historical and implied calibration ArXiv ID: 2501.05975 “View on arXiv” Authors: Unknown Abstract In energy markets, joint historical and implied calibration is of paramount importance for practitioners yet notoriously challenging due to the need to align historical correlations of futures contracts with implied volatility smiles from the option market. We address this crucial problem with a parsimonious multiplicative multi-factor Heath-Jarrow-Morton (HJM) model for forward curves, combined with a stochastic volatility factor coming from the Lifted Heston model. We develop a sequential fast calibration procedure leveraging the Kemna-Vorst approximation of futures contracts: (i) historical correlations and the Variance Swap (VS) volatility term structure are captured through Level, Slope, and Curvature factors, (ii) the VS volatility term structure can then be corrected for a perfect match via a fixed-point algorithm, (iii) implied volatility smiles are calibrated using Fourier-based techniques. Our model displays remarkable joint historical and implied calibration fits - to both German power and TTF gas markets - and enables realistic interpolation within the implied volatility hypercube. ...

Off-Policy Evaluation and Counterfactual Methods in Dynamic Auction Environments ArXiv ID: 2501.05278 “View on arXiv” Authors: Unknown Abstract Counterfactual estimators are critical for learning and refining policies using logged data, a process known as Off-Policy Evaluation (OPE). OPE allows researchers to assess new policies without costly experiments, speeding up the evaluation process. Online experimental methods, such as A/B tests, are effective but often slow, thus delaying the policy selection and optimization process. In this work, we explore the application of OPE methods in the context of resource allocation in dynamic auction environments. Given the competitive nature of environments where rapid decision-making is crucial for gaining a competitive edge, the ability to quickly and accurately assess algorithmic performance is essential. By utilizing counterfactual estimators as a preliminary step before conducting A/B tests, we aim to streamline the evaluation process, reduce the time and resources required for experimentation, and enhance confidence in the chosen policies. Our investigation focuses on the feasibility and effectiveness of using these estimators to predict the outcomes of potential resource allocation strategies, evaluate their performance, and facilitate more informed decision-making in policy selection. Motivated by the outcomes of our initial study, we envision an advanced analytics system designed to seamlessly and dynamically assess new resource allocation strategies and policies. ...

The Intraday Bitcoin Response to Tether Minting and Burning Events: Asymmetry, Investor Sentiment, And “Whale Alerts” On Twitter ArXiv ID: 2501.05232 “View on arXiv” Authors: Unknown Abstract Tether Limited has the sole authority to create (mint) and destroy (burn) Tether stablecoins (USDT). This paper investigates Bitcoin’s response to USDT supply change events between 2014 and 2021 and identifies an interesting asymmetry between Bitcoin’s responses to USDT minting and burning events. Bitcoin responds positively to USDT minting events over 5- to 30-minute event windows, but this response begins declining after 60 minutes. State-dependence is also demonstrated, with Bitcoin prices exhibiting a greater increase when the corresponding USDT minting event coincides with positive investor sentiment and is announced to the public by data service provider, Whale Alert, on Twitter. ...

Time-Varying Bidirectional Causal Relationships Between Transaction Fees and Economic Activity of Subsystems Utilizing the Ethereum Blockchain Network ArXiv ID: 2501.05299 “View on arXiv” Authors: Unknown Abstract The Ethereum blockchain network enables transaction processing and smart-contract execution through levies of transaction fees, commonly known as gas fees. This framework mediates economic participation via a market-based mechanism for gas fees, permitting users to offer higher gas fees to expedite pro-cessing. Historically, the ensuing gas fee volatility led to critical disequilibria between supply and demand for block space, presenting stakeholder challenges. This study examines the dynamic causal interplay between transaction fees and economic subsystems leveraging the network. By utilizing data related to unique active wallets and transaction volume of each subsystem and applying time-varying Granger causality analysis, we reveal temporal heterogeneity in causal relationships between economic activity and transaction fees across all subsystems. This includes (a) a bidirectional causal feedback loop between cross-blockchain bridge user activity and transaction fees, which diminishes over time, potentially signaling user migration; (b) a bidirectional relationship between centralized cryptocurrency exchange deposit and withdrawal transaction volume and fees, indicative of increased competition for block space; (c) decentralized exchange volumes causally influence fees, while fees causally influence user activity, although this relationship is weakening, potentially due to the diminished significance of decentralized finance; (d) intermittent causal relationships with maximal extractable value bots; (e) fees causally in-fluence non-fungible token transaction volumes; and (f) a highly significant and growing causal influence of transaction fees on stablecoin activity and transaction volumes highlight its prominence. ...

A mixture transition distribution approach to portfolio optimization ArXiv ID: 2501.04646 “View on arXiv” Authors: Unknown Abstract Understanding the dependencies among financial assets is critical for portfolio optimization. Traditional approaches based on correlation networks often fail to capture the nonlinear and directional relationships that exist in financial markets. In this study, we construct directed and weighted financial networks using the Mixture Transition Distribution (MTD) model, offering a richer representation of asset interdependencies. We apply local assortativity measures–metrics that evaluate how assets connect based on similarities or differences–to guide portfolio selection and allocation. Using data from the Dow Jones 30, Euro Stoxx 50, and FTSE 100 indices constituents, we show that portfolios optimized with network-based assortativity measures consistently outperform the classical mean-variance framework. Notably, modalities in which assets with differing characteristics connect enhance diversification and improve Sharpe ratios. The directed nature of MTD-based networks effectively captures complex relationships, yielding portfolios with superior risk-adjusted returns. Our findings highlight the utility of network-based methodologies in financial decision-making, demonstrating their ability to refine portfolio optimization strategies. This work thus underscores the potential of leveraging advanced financial networks to achieve enhanced performance, offering valuable insights for practitioners and setting a foundation for future research. ...