arXiv - QuantFin - Risk Management最新文献

DeFi Arbitrage in Hedged Liquidity Tokens 对冲流动性代币中的 DeFi 套利

arXiv - QuantFin - Risk Management Pub Date : 2024-09-17 DOI: arxiv-2409.11339

Maxim Bichuch, Zachary Feinstein

引用次数: 0

Decomposition Pipeline for Large-Scale Portfolio Optimization with Applications to Near-Term Quantum Computing 应用于近期量子计算的大规模组合优化分解管道

arXiv - QuantFin - Risk Management Pub Date : 2024-09-16 DOI: arxiv-2409.10301

Atithi Acharya, Romina Yalovetzky, Pierre Minssen, Shouvanik Chakrabarti, Ruslan Shaydulin, Rudy Raymond, Yue Sun, Dylan Herman, Ruben S. Andrist, Grant Salton, Martin J. A. Schuetz, Helmut G. Katzgraber, Marco Pistoia

{"title":"Decomposition Pipeline for Large-Scale Portfolio Optimization with Applications to Near-Term Quantum Computing","authors":"Atithi Acharya, Romina Yalovetzky, Pierre Minssen, Shouvanik Chakrabarti, Ruslan Shaydulin, Rudy Raymond, Yue Sun, Dylan Herman, Ruben S. Andrist, Grant Salton, Martin J. A. Schuetz, Helmut G. Katzgraber, Marco Pistoia","doi":"arxiv-2409.10301","DOIUrl":"https://doi.org/arxiv-2409.10301","url":null,"abstract":"Industrially relevant constrained optimization problems, such as portfolio\u0000optimization and portfolio rebalancing, are often intractable or difficult to\u0000solve exactly. In this work, we propose and benchmark a decomposition pipeline\u0000targeting portfolio optimization and rebalancing problems with constraints. The\u0000pipeline decomposes the optimization problem into constrained subproblems,\u0000which are then solved separately and aggregated to give a final result. Our\u0000pipeline includes three main components: preprocessing of correlation matrices\u0000based on random matrix theory, modified spectral clustering based on Newman's\u0000algorithm, and risk rebalancing. Our empirical results show that our pipeline\u0000consistently decomposes real-world portfolio optimization problems into\u0000subproblems with a size reduction of approximately 80%. Since subproblems are\u0000then solved independently, our pipeline drastically reduces the total\u0000computation time for state-of-the-art solvers. Moreover, by decomposing large\u0000problems into several smaller subproblems, the pipeline enables the use of\u0000near-term quantum devices as solvers, providing a path toward practical utility\u0000of quantum computers in portfolio optimization.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265653","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}

引用次数: 0

Research and Design of a Financial Intelligent Risk Control Platform Based on Big Data Analysis and Deep Machine Learning 基于大数据分析和深度机器学习的金融智能风险控制平台研究与设计

arXiv - QuantFin - Risk Management Pub Date : 2024-09-16 DOI: arxiv-2409.10331

Shuochen Bi, Yufan Lian, Ziyue Wang

{"title":"Research and Design of a Financial Intelligent Risk Control Platform Based on Big Data Analysis and Deep Machine Learning","authors":"Shuochen Bi, Yufan Lian, Ziyue Wang","doi":"arxiv-2409.10331","DOIUrl":"https://doi.org/arxiv-2409.10331","url":null,"abstract":"In the financial field of the United States, the application of big data\u0000technology has become one of the important means for financial institutions to\u0000enhance competitiveness and reduce risks. The core objective of this article is\u0000to explore how to fully utilize big data technology to achieve complete\u0000integration of internal and external data of financial institutions, and create\u0000an efficient and reliable platform for big data collection, storage, and\u0000analysis. With the continuous expansion and innovation of financial business,\u0000traditional risk management models are no longer able to meet the increasingly\u0000complex market demands. This article adopts big data mining and real-time\u0000streaming data processing technology to monitor, analyze, and alert various\u0000business data. Through statistical analysis of historical data and precise\u0000mining of customer transaction behavior and relationships, potential risks can\u0000be more accurately identified and timely responses can be made. This article\u0000designs and implements a financial big data intelligent risk control platform.\u0000This platform not only achieves effective integration, storage, and analysis of\u0000internal and external data of financial institutions, but also intelligently\u0000displays customer characteristics and their related relationships, as well as\u0000intelligent supervision of various risk information","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"97 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265654","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}

引用次数: 0

Credit Spreads' Term Structure: Stochastic Modeling with CIR++ Intensity 信用利差的期限结构：利用 CIR++ 强度进行随机建模

arXiv - QuantFin - Risk Management Pub Date : 2024-09-13 DOI: arxiv-2409.09179

Mohamed Ben Alaya, Ahmed Kebaier, Djibril Sarr

{"title":"Credit Spreads' Term Structure: Stochastic Modeling with CIR++ Intensity","authors":"Mohamed Ben Alaya, Ahmed Kebaier, Djibril Sarr","doi":"arxiv-2409.09179","DOIUrl":"https://doi.org/arxiv-2409.09179","url":null,"abstract":"This paper introduces a novel stochastic model for credit spreads. The\u0000stochastic approach leverages the diffusion of default intensities via a CIR++\u0000model and is formulated within a risk-neutral probability space. Our research\u0000primarily addresses two gaps in the literature. The first is the lack of credit\u0000spread models founded on a stochastic basis that enables continuous modeling,\u0000as many existing models rely on factorial assumptions. The second is the\u0000limited availability of models that directly yield a term structure of credit\u0000spreads. An intermediate result of our model is the provision of a term\u0000structure for the prices of defaultable bonds. We present the model alongside\u0000an innovative, practical, and conservative calibration approach that minimizes\u0000the error between historical and theoretical volatilities of default\u0000intensities. We demonstrate the robustness of both the model and its\u0000calibration process by comparing its behavior to historical credit spread\u0000values. Our findings indicate that the model not only produces realistic credit\u0000spread term structure curves but also exhibits consistent diffusion over time.\u0000Additionally, the model accurately fits the initial term structure of implied\u0000survival probabilities and provides an analytical expression for the credit\u0000spread of any given maturity at any future time.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265656","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}

引用次数: 0

Claims processing and costs under capacity constraints 能力限制下的索赔处理和成本

arXiv - QuantFin - Risk Management Pub Date : 2024-09-11 DOI: arxiv-2409.09091

Filip Lindskog, Mario V. Wüthrich

引用次数: 0

NLP-Powered Repository and Search Engine for Academic Papers: A Case Study on Cyber Risk Literature with CyLit NLP 驱动的学术论文库和搜索引擎：使用 CyLit 的网络风险文献案例研究

arXiv - QuantFin - Risk Management Pub Date : 2024-09-10 DOI: arxiv-2409.06226

Linfeng Zhang, Changyue Hu, Zhiyu Quan

{"title":"NLP-Powered Repository and Search Engine for Academic Papers: A Case Study on Cyber Risk Literature with CyLit","authors":"Linfeng Zhang, Changyue Hu, Zhiyu Quan","doi":"arxiv-2409.06226","DOIUrl":"https://doi.org/arxiv-2409.06226","url":null,"abstract":"As the body of academic literature continues to grow, researchers face\u0000increasing difficulties in effectively searching for relevant resources.\u0000Existing databases and search engines often fall short of providing a\u0000comprehensive and contextually relevant collection of academic literature. To\u0000address this issue, we propose a novel framework that leverages Natural\u0000Language Processing (NLP) techniques. This framework automates the retrieval,\u0000summarization, and clustering of academic literature within a specific research\u0000domain. To demonstrate the effectiveness of our approach, we introduce CyLit,\u0000an NLP-powered repository specifically designed for the cyber risk literature.\u0000CyLit empowers researchers by providing access to context-specific resources\u0000and enabling the tracking of trends in the dynamic and rapidly evolving field\u0000of cyber risk. Through the automatic processing of large volumes of data, our\u0000NLP-powered solution significantly enhances the efficiency and specificity of\u0000academic literature searches. We compare the literature categorization results\u0000of CyLit to those presented in survey papers or generated by ChatGPT,\u0000highlighting the distinctive insights this tool provides into cyber risk\u0000research literature. Using NLP techniques, we aim to revolutionize the way\u0000researchers discover, analyze, and utilize academic resources, ultimately\u0000fostering advancements in various domains of knowledge.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191286","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}

引用次数: 0

Risk measures on incomplete markets: a new non-solid paradigm 不完全市场的风险度量：一种新的非稳固范式

arXiv - QuantFin - Risk Management Pub Date : 2024-09-08 DOI: arxiv-2409.05194

Vasily Melnikov

引用次数: 0

Pareto-Optimal Peer-to-Peer Risk Sharing with Robust Distortion Risk Measures 采用稳健失真风险度量的帕累托最优点对点风险分担技术

arXiv - QuantFin - Risk Management Pub Date : 2024-09-08 DOI: arxiv-2409.05103

Mario Ghossoub, Michael B. Zhu, Wing Fung Chong

引用次数: 0

Quantifying Seasonal Weather Risk in Indian Markets: Stochastic Model for Risk-Averse State-Specific Temperature Derivative Pricing 量化印度市场的季节性天气风险：风险规避型特定州气温衍生品定价的随机模型

arXiv - QuantFin - Risk Management Pub Date : 2024-09-06 DOI: arxiv-2409.04541

Soumil Hooda, Shubham Sharma, Kunal Bansal

{"title":"Quantifying Seasonal Weather Risk in Indian Markets: Stochastic Model for Risk-Averse State-Specific Temperature Derivative Pricing","authors":"Soumil Hooda, Shubham Sharma, Kunal Bansal","doi":"arxiv-2409.04541","DOIUrl":"https://doi.org/arxiv-2409.04541","url":null,"abstract":"This technical report presents a stochastic framework for pricing temperature\u0000derivatives in Indian markets accounting for both monsoon and winter seasons.\u0000Utilising historical temperature and electricity consumption data from 12\u0000Indian states we develop a model based on a modified mean-reverting\u0000Ornstein-Uhlenbeck process and employ Monte Carlo simulations for pricing. Our\u0000analysis reveals significant variations in option pricing across states with\u0000monsoon call options ranging from 10.78 USD to 182.82 USD and winter put\u0000options from 48.65 USD to 194.99 USD. The introduction of a risk aversion\u0000parameter shows substantial impacts on pricing leading to an increase of up to\u0000416 percentage in option prices for certain states. Sensitivity analyses\u0000indicate that option prices are more responsive to changes in volatility than\u0000to mean reversion rates. Additionally extreme weather scenarios can shift\u0000option prices by up to 409 percentage during heatwaves and decrease by 60\u0000percentage during cold waves. These findings emphasise the importance of\u0000state-specific and season-specific approaches in temperature derivative pricing\u0000highlighting the need for tailored risk management strategies in India's\u0000diverse climate.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191289","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}

引用次数: 0

Pricing and hedging of decentralised lending contracts 分散借贷合同的定价和套期保值

arXiv - QuantFin - Risk Management Pub Date : 2024-09-06 DOI: arxiv-2409.04233

Lukasz Szpruch, Marc Sabaté Vidales, Tanut Treetanthiploet, Yufei Zhang

{"title":"Pricing and hedging of decentralised lending contracts","authors":"Lukasz Szpruch, Marc Sabaté Vidales, Tanut Treetanthiploet, Yufei Zhang","doi":"arxiv-2409.04233","DOIUrl":"https://doi.org/arxiv-2409.04233","url":null,"abstract":"We study the loan contracts offered by decentralised loan protocols (DLPs)\u0000through the lens of financial derivatives. DLPs, which effectively are\u0000clearinghouses, facilitate transactions between option buyers (i.e. borrowers)\u0000and option sellers (i.e. lenders). The loan-to-value at which the contract is\u0000initiated determines the option premium borrowers pay for entering the\u0000contract, and this can be deduced from the non-arbitrage pricing theory. We\u0000show that when there are no market frictions, and there is no spread between\u0000lending and borrowing rates, it is optimal to never enter the lending contract. Next, by accounting for the spread between rates and transactional costs, we\u0000develop a deep neural network-based algorithm for learning trading strategies\u0000on the external markets that allow us to replicate the payoff of the lending\u0000contracts that are not necessarily optimally exercised. This allows hedge the\u0000risk lenders carry by issuing options sold to the borrowers, which can\u0000complement (or even replace) the liquidations mechanism used to protect\u0000lenders' capital. Our approach can also be used to exploit (statistical)\u0000arbitrage opportunities that may arise when DLP allow users to enter lending\u0000contracts with loan-to-value, which is not appropriately calibrated to market\u0000conditions or/and when different markets price risk differently. We present\u0000thorough simulation experiments using historical data and simulations to\u0000validate our approach.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191288","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}

引用次数: 0