Hongzhou Chen, Xiaolin Duan, Abdulmotaleb El Saddik, Wei Cai
Harnessing the transparent blockchain user behavior data, we construct the Political Betting Leaning Score (PBLS) to measure political leanings based on betting within Web3 prediction markets. Focusing on Polymarket and starting from the 2024 U.S. Presidential Election, we synthesize behaviors over 15,000 addresses across 4,500 events and 8,500 markets, capturing the intensity and direction of their political leanings by the PBLS. We validate the PBLS through internal consistency checks and external comparisons. We uncover relationships between our PBLS and betting behaviors through over 800 features capturing various behavioral aspects. A case study of the 2022 U.S. Senate election further demonstrates the ability of our measurement while decoding the dynamic interaction between political and profitable motives. Our findings contribute to understanding decision-making in decentralized markets, enhancing the analysis of behaviors within Web3 prediction environments. The insights of this study reveal the potential of blockchain in enabling innovative, multidisciplinary studies and could inform the development of more effective online prediction markets, improve the accuracy of forecast, and help the design and optimization of platform mechanisms. The data and code for the paper are accessible at the following link: https://github.com/anonymous.
{"title":"Political Leanings in Web3 Betting: Decoding the Interplay of Political and Profitable Motives","authors":"Hongzhou Chen, Xiaolin Duan, Abdulmotaleb El Saddik, Wei Cai","doi":"arxiv-2407.14844","DOIUrl":"https://doi.org/arxiv-2407.14844","url":null,"abstract":"Harnessing the transparent blockchain user behavior data, we construct the\u0000Political Betting Leaning Score (PBLS) to measure political leanings based on\u0000betting within Web3 prediction markets. Focusing on Polymarket and starting\u0000from the 2024 U.S. Presidential Election, we synthesize behaviors over 15,000\u0000addresses across 4,500 events and 8,500 markets, capturing the intensity and\u0000direction of their political leanings by the PBLS. We validate the PBLS through\u0000internal consistency checks and external comparisons. We uncover relationships\u0000between our PBLS and betting behaviors through over 800 features capturing\u0000various behavioral aspects. A case study of the 2022 U.S. Senate election\u0000further demonstrates the ability of our measurement while decoding the dynamic\u0000interaction between political and profitable motives. Our findings contribute\u0000to understanding decision-making in decentralized markets, enhancing the\u0000analysis of behaviors within Web3 prediction environments. The insights of this\u0000study reveal the potential of blockchain in enabling innovative,\u0000multidisciplinary studies and could inform the development of more effective\u0000online prediction markets, improve the accuracy of forecast, and help the\u0000design and optimization of platform mechanisms. The data and code for the paper\u0000are accessible at the following link: https://github.com/anonymous.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141773576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Securities lending is an important part of the financial market structure, where agent lenders help long term institutional investors to lend out their securities to short sellers in exchange for a lending fee. Agent lenders within the market seek to optimize revenue by lending out securities at the highest rate possible. Typically, this rate is set by hard-coded business rules or standard supervised machine learning models. These approaches are often difficult to scale and are not adaptive to changing market conditions. Unlike a traditional stock exchange with a centralized limit order book, the securities lending market is organized similarly to an e-commerce marketplace, where agent lenders and borrowers can transact at any agreed price in a bilateral fashion. This similarity suggests that the use of typical methods for addressing dynamic pricing problems in e-commerce could be effective in the securities lending market. We show that existing contextual bandit frameworks can be successfully utilized in the securities lending market. Using offline evaluation on real historical data, we show that the contextual bandit approach can consistently outperform typical approaches by at least 15% in terms of total revenue generated.
{"title":"Dynamic Pricing in Securities Lending Market: Application in Revenue Optimization for an Agent Lender Portfolio","authors":"Jing Xu, Yung Cheng Hsu, William Biscarri","doi":"arxiv-2407.13687","DOIUrl":"https://doi.org/arxiv-2407.13687","url":null,"abstract":"Securities lending is an important part of the financial market structure,\u0000where agent lenders help long term institutional investors to lend out their\u0000securities to short sellers in exchange for a lending fee. Agent lenders within\u0000the market seek to optimize revenue by lending out securities at the highest\u0000rate possible. Typically, this rate is set by hard-coded business rules or\u0000standard supervised machine learning models. These approaches are often\u0000difficult to scale and are not adaptive to changing market conditions. Unlike a\u0000traditional stock exchange with a centralized limit order book, the securities\u0000lending market is organized similarly to an e-commerce marketplace, where agent\u0000lenders and borrowers can transact at any agreed price in a bilateral fashion.\u0000This similarity suggests that the use of typical methods for addressing dynamic\u0000pricing problems in e-commerce could be effective in the securities lending\u0000market. We show that existing contextual bandit frameworks can be successfully\u0000utilized in the securities lending market. Using offline evaluation on real\u0000historical data, we show that the contextual bandit approach can consistently\u0000outperform typical approaches by at least 15% in terms of total revenue\u0000generated.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research presents a comprehensive evaluation of systematic index option-writing strategies, focusing on S&P500 index options. We compare the performance of hedging strategies using the Black-Scholes-Merton (BSM) model and the Variance-Gamma (VG) model, emphasizing varying moneyness levels and different sizing methods based on delta and the VIX Index. The study employs 1-minute data of S&P500 index options and index quotes spanning from 2018 to 2023. The analysis benchmarks hedged strategies against buy-and-hold and naked option-writing strategies, with a focus on risk-adjusted performance metrics including transaction costs. Portfolio delta approximations are derived using implied volatility for the BSM model and market-calibrated parameters for the VG model. Key findings reveal that systematic option-writing strategies can potentially yield superior returns compared to buy-and-hold benchmarks. The BSM model generally provided better hedging outcomes than the VG model, although the VG model showed profitability in certain naked strategies as a tool for position sizing. In terms of rehedging frequency, we found that intraday hedging in 130-minute intervals provided both reliable protection against adverse market movements and a satisfactory returns profile.
{"title":"Construction and Hedging of Equity Index Options Portfolios","authors":"Maciej Wysocki, Robert Ślepaczuk","doi":"arxiv-2407.13908","DOIUrl":"https://doi.org/arxiv-2407.13908","url":null,"abstract":"This research presents a comprehensive evaluation of systematic index\u0000option-writing strategies, focusing on S&P500 index options. We compare the\u0000performance of hedging strategies using the Black-Scholes-Merton (BSM) model\u0000and the Variance-Gamma (VG) model, emphasizing varying moneyness levels and\u0000different sizing methods based on delta and the VIX Index. The study employs\u00001-minute data of S&P500 index options and index quotes spanning from 2018 to\u00002023. The analysis benchmarks hedged strategies against buy-and-hold and naked\u0000option-writing strategies, with a focus on risk-adjusted performance metrics\u0000including transaction costs. Portfolio delta approximations are derived using\u0000implied volatility for the BSM model and market-calibrated parameters for the\u0000VG model. Key findings reveal that systematic option-writing strategies can\u0000potentially yield superior returns compared to buy-and-hold benchmarks. The BSM\u0000model generally provided better hedging outcomes than the VG model, although\u0000the VG model showed profitability in certain naked strategies as a tool for\u0000position sizing. In terms of rehedging frequency, we found that intraday\u0000hedging in 130-minute intervals provided both reliable protection against\u0000adverse market movements and a satisfactory returns profile.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Walter Hernandez Cruz, Jiahua Xu, Paolo Tasca, Carlo Campajola
Fiat-pegged stablecoins are by nature exposed to spillover effects during market turmoil in Traditional Finance (TradFi). We observe a difference in TradFi market shocks impact between various stablecoins, in particular, USD Coin (USDC) and Tether USDT (USDT), the former with a higher reporting frequency and transparency than the latter. We investigate this, using top USDC and USDT liquidity pools in Uniswap, by adapting the Marginal Cost of Immediacy (MCI) measure to Uniswap's Automated Market Maker, and then conducting Difference-in-Differences analysis on MCI and Total Value Locked (TVL) in USD, as well as measuring liquidity concentration across different providers. Results show that the Silicon Valley Bank (SVB) event reduced USDC's TVL dominance over USDT, increased USDT's liquidity cost relative to USDC, and liquidity provision remained concentrated with pool-specific trends. These findings reveal a flight-to-safety behavior and counterintuitive effects of stablecoin transparency: USDC's frequent and detailed disclosures led to swift market reactions, while USDT's opacity and less frequent reporting provided a safety net against immediate impacts.
{"title":"No Questions Asked: Effects of Transparency on Stablecoin Liquidity During the Collapse of Silicon Valley Bank","authors":"Walter Hernandez Cruz, Jiahua Xu, Paolo Tasca, Carlo Campajola","doi":"arxiv-2407.11716","DOIUrl":"https://doi.org/arxiv-2407.11716","url":null,"abstract":"Fiat-pegged stablecoins are by nature exposed to spillover effects during\u0000market turmoil in Traditional Finance (TradFi). We observe a difference in\u0000TradFi market shocks impact between various stablecoins, in particular, USD\u0000Coin (USDC) and Tether USDT (USDT), the former with a higher reporting\u0000frequency and transparency than the latter. We investigate this, using top USDC\u0000and USDT liquidity pools in Uniswap, by adapting the Marginal Cost of Immediacy\u0000(MCI) measure to Uniswap's Automated Market Maker, and then conducting\u0000Difference-in-Differences analysis on MCI and Total Value Locked (TVL) in USD,\u0000as well as measuring liquidity concentration across different providers.\u0000Results show that the Silicon Valley Bank (SVB) event reduced USDC's TVL\u0000dominance over USDT, increased USDT's liquidity cost relative to USDC, and\u0000liquidity provision remained concentrated with pool-specific trends. These\u0000findings reveal a flight-to-safety behavior and counterintuitive effects of\u0000stablecoin transparency: USDC's frequent and detailed disclosures led to swift\u0000market reactions, while USDT's opacity and less frequent reporting provided a\u0000safety net against immediate impacts.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141721325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Álvaro Cartea, Sebastian Jaimungal, Leandro Sánchez-Betancourt
We study the perfect information Nash equilibrium between a broker and her clients -- an informed trader, and an uniformed trader. In our model, the broker trades in the lit exchange where trades have instantaneous and transient price impact with exponential resilience, while both clients trade with the broker. The informed trader and the broker maximise expected wealth subject to inventory penalties, while the uninformed trader is not strategic and sends the broker random buy and sell orders. We characterise the Nash equilibrium of the trading strategies with the solution to a coupled system of forward-backward stochastic differential equations (FBSDEs). We solve this system explicitly and study the effect of information in the trading strategies of the broker and the informed trader.
{"title":"Nash Equilibrium between Brokers and Traders","authors":"Álvaro Cartea, Sebastian Jaimungal, Leandro Sánchez-Betancourt","doi":"arxiv-2407.10561","DOIUrl":"https://doi.org/arxiv-2407.10561","url":null,"abstract":"We study the perfect information Nash equilibrium between a broker and her\u0000clients -- an informed trader, and an uniformed trader. In our model, the\u0000broker trades in the lit exchange where trades have instantaneous and transient\u0000price impact with exponential resilience, while both clients trade with the\u0000broker. The informed trader and the broker maximise expected wealth subject to\u0000inventory penalties, while the uninformed trader is not strategic and sends the\u0000broker random buy and sell orders. We characterise the Nash equilibrium of the\u0000trading strategies with the solution to a coupled system of forward-backward\u0000stochastic differential equations (FBSDEs). We solve this system explicitly and\u0000study the effect of information in the trading strategies of the broker and the\u0000informed trader.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141722388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroeconomics, policy changes, company fundamentals, and global events)? These factors, which frequently influence trading behaviors, are critical elements in the quest for maximizing investors' profits. Our work attempts to solve this problem through large language model based agents. We have developed a multi-agent AI system called StockAgent, driven by LLMs, designed to simulate investors' trading behaviors in response to the real stock market. The StockAgent allows users to evaluate the impact of different external factors on investor trading and to analyze trading behavior and profitability effects. Additionally, StockAgent avoids the test set leakage issue present in existing trading simulation systems based on AI Agents. Specifically, it prevents the model from leveraging prior knowledge it may have acquired related to the test data. We evaluate different LLMs under the framework of StockAgent in a stock trading environment that closely resembles real-world conditions. The experimental results demonstrate the impact of key external factors on stock market trading, including trading behavior and stock price fluctuation rules. This research explores the study of agents' free trading gaps in the context of no prior knowledge related to market data. The patterns identified through StockAgent simulations provide valuable insights for LLM-based investment advice and stock recommendation. The code is available at https://github.com/MingyuJ666/Stockagent.
{"title":"When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments","authors":"Chong Zhang, Xinyi Liu, Mingyu Jin, Zhongmou Zhang, Lingyao Li, Zhengting Wang, Wenyue Hua, Dong Shu, Suiyuan Zhu, Xiaobo Jin, Sujian Li, Mengnan Du, Yongfeng Zhang","doi":"arxiv-2407.18957","DOIUrl":"https://doi.org/arxiv-2407.18957","url":null,"abstract":"Can AI Agents simulate real-world trading environments to investigate the\u0000impact of external factors on stock trading activities (e.g., macroeconomics,\u0000policy changes, company fundamentals, and global events)? These factors, which\u0000frequently influence trading behaviors, are critical elements in the quest for\u0000maximizing investors' profits. Our work attempts to solve this problem through\u0000large language model based agents. We have developed a multi-agent AI system\u0000called StockAgent, driven by LLMs, designed to simulate investors' trading\u0000behaviors in response to the real stock market. The StockAgent allows users to\u0000evaluate the impact of different external factors on investor trading and to\u0000analyze trading behavior and profitability effects. Additionally, StockAgent\u0000avoids the test set leakage issue present in existing trading simulation\u0000systems based on AI Agents. Specifically, it prevents the model from leveraging\u0000prior knowledge it may have acquired related to the test data. We evaluate\u0000different LLMs under the framework of StockAgent in a stock trading environment\u0000that closely resembles real-world conditions. The experimental results\u0000demonstrate the impact of key external factors on stock market trading,\u0000including trading behavior and stock price fluctuation rules. This research\u0000explores the study of agents' free trading gaps in the context of no prior\u0000knowledge related to market data. The patterns identified through StockAgent\u0000simulations provide valuable insights for LLM-based investment advice and stock\u0000recommendation. The code is available at\u0000https://github.com/MingyuJ666/Stockagent.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on geometrical considerations, we propose a new oscillator for technical market analysis, the tube oscillator. This oscillator measures the trending behavior of a fixed market instrument based on its past history. It is shown in an empirical analysis of the German DAX and the Forex EUR/USD exchange rate that a simple trading strategy based on this oscillator and fixed threshold leads to consistent positive monthly returns of average magnitude of 2% or more. The oscillator is derived from a broader understanding of the geometric behavior of prices throughout a fixed period, which we term financial market geometry. The remarkable profit results of the presented technique show that 1) prices of financial market instruments have a strong underlying deterministic component which can be detected and quantified with a matching approach and 2) financial market geometry is capable of providing such detectors.
{"title":"Financial market geometry: The tube oscillator","authors":"Dragoljub Katic, Stefan Richter","doi":"arxiv-2407.08036","DOIUrl":"https://doi.org/arxiv-2407.08036","url":null,"abstract":"Based on geometrical considerations, we propose a new oscillator for\u0000technical market analysis, the tube oscillator. This oscillator measures the\u0000trending behavior of a fixed market instrument based on its past history. It is\u0000shown in an empirical analysis of the German DAX and the Forex EUR/USD exchange\u0000rate that a simple trading strategy based on this oscillator and fixed\u0000threshold leads to consistent positive monthly returns of average magnitude of\u00002% or more. The oscillator is derived from a broader understanding of the\u0000geometric behavior of prices throughout a fixed period, which we term financial\u0000market geometry. The remarkable profit results of the presented technique show\u0000that 1) prices of financial market instruments have a strong underlying\u0000deterministic component which can be detected and quantified with a matching\u0000approach and 2) financial market geometry is capable of providing such\u0000detectors.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141612926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study evaluates the performance of 41 machine learning models, including 21 classifiers and 20 regressors, in predicting Bitcoin prices for algorithmic trading. By examining these models under various market conditions, we highlight their accuracy, robustness, and adaptability to the volatile cryptocurrency market. Our comprehensive analysis reveals the strengths and limitations of each model, providing critical insights for developing effective trading strategies. We employ both machine learning metrics (e.g., Mean Absolute Error, Root Mean Squared Error) and trading metrics (e.g., Profit and Loss percentage, Sharpe Ratio) to assess model performance. Our evaluation includes backtesting on historical data, forward testing on recent unseen data, and real-world trading scenarios, ensuring the robustness and practical applicability of our models. Key findings demonstrate that certain models, such as Random Forest and Stochastic Gradient Descent, outperform others in terms of profit and risk management. These insights offer valuable guidance for traders and researchers aiming to leverage machine learning for cryptocurrency trading.
{"title":"A Comprehensive Analysis of Machine Learning Models for Algorithmic Trading of Bitcoin","authors":"Abdul Jabbar, Syed Qaisar Jalil","doi":"arxiv-2407.18334","DOIUrl":"https://doi.org/arxiv-2407.18334","url":null,"abstract":"This study evaluates the performance of 41 machine learning models, including\u000021 classifiers and 20 regressors, in predicting Bitcoin prices for algorithmic\u0000trading. By examining these models under various market conditions, we\u0000highlight their accuracy, robustness, and adaptability to the volatile\u0000cryptocurrency market. Our comprehensive analysis reveals the strengths and\u0000limitations of each model, providing critical insights for developing effective\u0000trading strategies. We employ both machine learning metrics (e.g., Mean\u0000Absolute Error, Root Mean Squared Error) and trading metrics (e.g., Profit and\u0000Loss percentage, Sharpe Ratio) to assess model performance. Our evaluation\u0000includes backtesting on historical data, forward testing on recent unseen data,\u0000and real-world trading scenarios, ensuring the robustness and practical\u0000applicability of our models. Key findings demonstrate that certain models, such\u0000as Random Forest and Stochastic Gradient Descent, outperform others in terms of\u0000profit and risk management. These insights offer valuable guidance for traders\u0000and researchers aiming to leverage machine learning for cryptocurrency trading.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We develop static and dynamic approaches for hedging of the impermanent loss (IL) of liquidity provision (LP) staked at Decentralised Exchanges (DEXes) which employ Uniswap V2 and V3 protocols. We provide detailed definitions and formulas for computing the IL to unify different definitions occurring in the existing literature. We show that the IL can be seen a contingent claim with a non-linear payoff for a fixed maturity date. Thus, we introduce the contingent claim termed as IL protection claim which delivers the negative of IL payoff at the maturity date. We apply arbitrage-based methods for valuation and risk management of this claim. First, we develop the static model-independent replication method for the valuation of IL protection claim using traded European vanilla call and put options. We extend and generalize an existing method to show that the IL protection claim can be hedged perfectly with options if there is a liquid options market. Second, we develop the dynamic model-based approach for the valuation and hedging of IL protection claims under a risk-neutral measure. We derive analytic valuation formulas using a wide class of price dynamics for which the characteristic function is available under the risk-neutral measure. As base cases, we derive analytic valuation formulas for IL protection claim under the Black-Scholes-Merton model and the log-normal stochastic volatility model. We finally discuss estimation of risk-reward of LP staking using our results.
我们开发了静态和动态方法，用于对冲采用 Uniswap V2 和 V3 协议的去中心化交易所（DEXes）中流动性供应（LP）的无常损失（IL）。我们提供了计算 IL 的详细定义和公式，以统一现有文献中出现的不同定义。我们证明，IL 可以看作是一个固定到期日非线性报酬的或有债权。因此，我们引入了被称为 IL 保护债权的或有债权，它在到期日提供 IL 报酬的负值。我们采用基于套利的方法对该债权进行估值和风险管理。首先，我们开发了独立于模型的静态复制方法，利用交易的欧洲虚值看涨和看跌期权对 IL 保护索赔进行估值。我们对现有方法进行了扩展和概括，证明如果存在一个流动期权市场，则可以用期权对冲 IL 保障债权。其次，我们开发了基于动态模型的方法，用于在风险中性度量下对 IL 保障债权进行估值和对冲。我们利用风险中性度量下可获得特征函数的各类价格动态推导出分析估值公式。作为基础案例，我们推导了布莱克-斯科尔斯-默顿模型和逻辑正态随机波动模型下 IL 保障索赔的分析估值公式。最后，我们讨论了利用我们的结果对 LP 押注的风险回报进行估计的问题。
{"title":"Unified Approach for Hedging Impermanent Loss of Liquidity Provision","authors":"Alexander Lipton, Vladimir Lucic, Artur Sepp","doi":"arxiv-2407.05146","DOIUrl":"https://doi.org/arxiv-2407.05146","url":null,"abstract":"We develop static and dynamic approaches for hedging of the impermanent loss\u0000(IL) of liquidity provision (LP) staked at Decentralised Exchanges (DEXes)\u0000which employ Uniswap V2 and V3 protocols. We provide detailed definitions and\u0000formulas for computing the IL to unify different definitions occurring in the\u0000existing literature. We show that the IL can be seen a contingent claim with a\u0000non-linear payoff for a fixed maturity date. Thus, we introduce the contingent\u0000claim termed as IL protection claim which delivers the negative of IL payoff at\u0000the maturity date. We apply arbitrage-based methods for valuation and risk\u0000management of this claim. First, we develop the static model-independent\u0000replication method for the valuation of IL protection claim using traded\u0000European vanilla call and put options. We extend and generalize an existing\u0000method to show that the IL protection claim can be hedged perfectly with\u0000options if there is a liquid options market. Second, we develop the dynamic\u0000model-based approach for the valuation and hedging of IL protection claims\u0000under a risk-neutral measure. We derive analytic valuation formulas using a\u0000wide class of price dynamics for which the characteristic function is available\u0000under the risk-neutral measure. As base cases, we derive analytic valuation\u0000formulas for IL protection claim under the Black-Scholes-Merton model and the\u0000log-normal stochastic volatility model. We finally discuss estimation of\u0000risk-reward of LP staking using our results.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141577408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider a central trading desk which aggregates the inflow of clients' orders with unobserved toxicity, i.e. persistent adverse directionality. The desk chooses either to internalise the inflow or externalise it to the market in a cost effective manner. In this model, externalising the order flow creates both price impact costs and an additional market feedback reaction for the inflow of trades. The desk's objective is to maximise the daily trading P&L subject to end of the day inventory penalization. We formulate this setting as a partially observable stochastic control problem and solve it in two steps. First, we derive the filtered dynamics of the inventory and toxicity, projected to the observed filtration, which turns the stochastic control problem into a fully observed problem. Then we use a variational approach in order to derive the unique optimal trading strategy. We illustrate our results for various scenarios in which the desk is facing momentum and mean-reverting toxicity. Our implementation shows that the P&L performance gap between the partially observable problem and the full information case are of order $0.01%$ in all tested scenarios.
{"title":"Unwinding Toxic Flow with Partial Information","authors":"Alexander Barzykin, Robert Boyce, Eyal Neuman","doi":"arxiv-2407.04510","DOIUrl":"https://doi.org/arxiv-2407.04510","url":null,"abstract":"We consider a central trading desk which aggregates the inflow of clients'\u0000orders with unobserved toxicity, i.e. persistent adverse directionality. The\u0000desk chooses either to internalise the inflow or externalise it to the market\u0000in a cost effective manner. In this model, externalising the order flow creates\u0000both price impact costs and an additional market feedback reaction for the\u0000inflow of trades. The desk's objective is to maximise the daily trading P&L\u0000subject to end of the day inventory penalization. We formulate this setting as\u0000a partially observable stochastic control problem and solve it in two steps.\u0000First, we derive the filtered dynamics of the inventory and toxicity, projected\u0000to the observed filtration, which turns the stochastic control problem into a\u0000fully observed problem. Then we use a variational approach in order to derive\u0000the unique optimal trading strategy. We illustrate our results for various\u0000scenarios in which the desk is facing momentum and mean-reverting toxicity. Our\u0000implementation shows that the P&L performance gap between the partially\u0000observable problem and the full information case are of order $0.01%$ in all\u0000tested scenarios.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"366 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141569070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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