arXiv - QuantFin - Computational Finance最新文献_第3页

Existence, uniqueness and positivity of solutions to the Guyon-Lekeufack path-dependent volatility model with general kernels 具有一般内核的居雍-勒库法克路径依赖波动模型解的存在性、唯一性和实在性

arXiv - QuantFin - Computational Finance Pub Date : 2024-08-05 DOI: arxiv-2408.02477

Hervé AndrèsCERMICS, Benjamin JourdainCERMICS, MATHRISK

{"title":"Existence, uniqueness and positivity of solutions to the Guyon-Lekeufack path-dependent volatility model with general kernels","authors":"Hervé AndrèsCERMICS, Benjamin JourdainCERMICS, MATHRISK","doi":"arxiv-2408.02477","DOIUrl":"https://doi.org/arxiv-2408.02477","url":null,"abstract":"We show the existence and uniqueness of a continuous solution to a\u0000path-dependent volatility model introduced by Guyon and Lekeufack (2023) to\u0000model the price of an equity index and its spot volatility. The considered\u0000model for the trend and activity features can be written as a Stochastic\u0000Volterra Equation (SVE) with non-convolutional and non-bounded kernels as well\u0000as non-Lipschitz coefficients. We first prove the existence and uniqueness of a\u0000solution to the SVE under integrability and regularity assumptions on the two\u0000kernels and under a condition on the second kernel weighting the past squared\u0000returns which ensures that the activity feature is bounded from below by a\u0000positive constant. Then, assuming in addition that the kernel weighting the\u0000past returns is of exponential type and that an inequality relating the\u0000logarithmic derivatives of the two kernels with respect to their second\u0000variables is satisfied, we show the positivity of the volatility process which\u0000is obtained as a non-linear function of the SVE's solution. We show numerically\u0000that the choice of an exponential kernel for the kernel weighting the past\u0000returns has little impact on the quality of model calibration compared to other\u0000choices and the inequality involving the logarithmic derivatives is satisfied\u0000by the calibrated kernels. These results extend those of Nutz and Valdevenito\u0000(2023).","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932411","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

KAN based Autoencoders for Factor Models 基于 KAN 的因子模型自动编码器

arXiv - QuantFin - Computational Finance Pub Date : 2024-08-04 DOI: arxiv-2408.02694

Tianqi Wang, Shubham Singh

引用次数: 0

CVA Sensitivities, Hedging and Risk CVA 敏感性、套期保值和风险

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-26 DOI: arxiv-2407.18583

Stéphane CrépeyUFR Mathématiques UPCité, Botao LiLPSM, Hoang NguyenIES, LPSM, Bouazza Saadeddine

引用次数: 0

High order approximations of the log-Heston process semigroup 对数-赫斯顿过程半群的高阶近似值

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-24 DOI: arxiv-2407.17151

Aurélien Alfonsi, Edoardo Lombardo

引用次数: 0

Alleviating Non-identifiability: a High-fidelity Calibration Objective for Financial Market Simulation with Multivariate Time Series Data 缓解不可识别性：利用多变量时间序列数据进行金融市场模拟的高保真校准目标

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-23 DOI: arxiv-2407.16566

Chenkai Wang, Junji Ren, Ke Tang, Peng Yang

{"title":"Alleviating Non-identifiability: a High-fidelity Calibration Objective for Financial Market Simulation with Multivariate Time Series Data","authors":"Chenkai Wang, Junji Ren, Ke Tang, Peng Yang","doi":"arxiv-2407.16566","DOIUrl":"https://doi.org/arxiv-2407.16566","url":null,"abstract":"The non-identifiability issue has been frequently reported in the social\u0000simulation works, where different parameters of an agent-based simulation model\u0000yield indistinguishable simulated time series data under certain discrepancy\u0000metrics. This issue largely undermines the simulation fidelity yet lacks\u0000dedicated investigations. This paper theoretically analyzes that incorporating\u0000multiple time series data features in the model calibration phase can alleviate\u0000the non-identifiability exponentially with the increasing number of features.\u0000To implement this theoretical finding, a maximization-based aggregation\u0000function is applied to existing discrepancy metrics to form a new calibration\u0000objective function. For verification, the financial market simulation, a\u0000typical and complex social simulation task, is considered. Empirical studies on\u0000both synthetic and real market data witness the significant improvements in\u0000alleviating the non-identifiability with much higher simulation fidelity of the\u0000chosen agent-based simulation model. Importantly, as a model-agnostic method,\u0000it achieves the first successful simulation of the high-frequency market at\u0000seconds level. Hence, this work is expected to provide not only a rigorous\u0000understanding of non-identifiability in social simulation, but a high-fidelity\u0000calibration objective function for financial market simulations.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771986","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

Calibrating the Heston Model with Deep Differential Networks 用深度差分网络校准赫斯顿模型

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-22 DOI: arxiv-2407.15536

Chen Zhang, Giovanni Amici, Marco Morandotti

引用次数: 0

Explainable AI in Request-for-Quote 询价中的可解释人工智能

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-21 DOI: arxiv-2407.15038

Qiqin Zhou

引用次数: 0

Towards A Post-Quantum Cryptography in Blockchain I: Basic Review on Theoretical Cryptography and Quantum Information Theory 迈向区块链中的后量子密码学 I：理论密码学和量子信息论基本回顾

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-19 DOI: arxiv-2407.18966

Tatsuru Kikuchi

引用次数: 0

Leveraging Machine Learning for High-Dimensional Option Pricing within the Uncertain Volatility Model 在不确定波动率模型中利用机器学习进行高维期权定价

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-18 DOI: arxiv-2407.13213

Ludovic Goudenege, Andrea Molent, Antonino Zanette

{"title":"Leveraging Machine Learning for High-Dimensional Option Pricing within the Uncertain Volatility Model","authors":"Ludovic Goudenege, Andrea Molent, Antonino Zanette","doi":"arxiv-2407.13213","DOIUrl":"https://doi.org/arxiv-2407.13213","url":null,"abstract":"This paper explores the application of Machine Learning techniques for\u0000pricing high-dimensional options within the framework of the Uncertain\u0000Volatility Model (UVM). The UVM is a robust framework that accounts for the\u0000inherent unpredictability of market volatility by setting upper and lower\u0000bounds on volatility and the correlation among underlying assets. By leveraging\u0000historical data and extreme values of estimated volatilities and correlations,\u0000the model establishes a confidence interval for future volatility and\u0000correlations, thus providing a more realistic approach to option pricing. By\u0000integrating advanced Machine Learning algorithms, we aim to enhance the\u0000accuracy and efficiency of option pricing under the UVM, especially when the\u0000option price depends on a large number of variables, such as in basket or\u0000path-dependent options. Our approach evolves backward in time, dynamically\u0000selecting at each time step the most expensive volatility and correlation for\u0000each market state. Specifically, it identifies the particular values of\u0000volatility and correlation that maximize the expected option value at the next\u0000time step. This is achieved through the use of Gaussian Process regression, the\u0000computation of expectations via a single step of a multidimensional tree and\u0000the Sequential Quadratic Programming optimization algorithm. The numerical\u0000results demonstrate that the proposed approach can significantly improve the\u0000precision of option pricing and risk management strategies compared with\u0000methods already in the literature, particularly in high-dimensional contexts.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141742102","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

Temporal Representation Learning for Stock Similarities and Its Applications in Investment Management 股票相似性的时态表征学习及其在投资管理中的应用

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-18 DOI: arxiv-2407.13751

Yoontae Hwang, Stefan Zohren, Yongjae Lee

{"title":"Temporal Representation Learning for Stock Similarities and Its Applications in Investment Management","authors":"Yoontae Hwang, Stefan Zohren, Yongjae Lee","doi":"arxiv-2407.13751","DOIUrl":"https://doi.org/arxiv-2407.13751","url":null,"abstract":"In the era of rapid globalization and digitalization, accurate identification\u0000of similar stocks has become increasingly challenging due to the non-stationary\u0000nature of financial markets and the ambiguity in conventional regional and\u0000sector classifications. To address these challenges, we examine SimStock, a\u0000novel temporal self-supervised learning framework that combines techniques from\u0000self-supervised learning (SSL) and temporal domain generalization to learn\u0000robust and informative representations of financial time series data. The\u0000primary focus of our study is to understand the similarities between stocks\u0000from a broader perspective, considering the complex dynamics of the global\u0000financial landscape. We conduct extensive experiments on four real-world\u0000datasets with thousands of stocks and demonstrate the effectiveness of SimStock\u0000in finding similar stocks, outperforming existing methods. The practical\u0000utility of SimStock is showcased through its application to various investment\u0000strategies, such as pairs trading, index tracking, and portfolio optimization,\u0000where it leads to superior performance compared to conventional methods. Our\u0000findings empirically examine the potential of data-driven approach to enhance\u0000investment decision-making and risk management practices by leveraging the\u0000power of temporal self-supervised learning in the face of the ever-changing\u0000global financial landscape.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141742134","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