arXiv - QuantFin - Computational Finance最新文献_第8页

Internet sentiment exacerbates intraday overtrading, evidence from A-Share market 互联网情绪加剧日内过度交易，A 股市场提供的证据

arXiv - QuantFin - Computational Finance Pub Date : 2024-04-18 DOI: arxiv-2404.12001

Peng Yifeng

引用次数: 0

Deep Joint Learning valuation of Bermudan Swaptions 百慕大掉期的深度联合学习估值

arXiv - QuantFin - Computational Finance Pub Date : 2024-04-17 DOI: arxiv-2404.11257

Francisco Gómez Casanova, Álvaro Leitao, Fernando de Lope Contreras, Carlos Vázquez

引用次数: 0

A Comparison of Traditional and Deep Learning Methods for Parameter Estimation of the Ornstein-Uhlenbeck Process 用于奥恩斯坦-乌伦贝克过程参数估计的传统方法与深度学习方法比较

arXiv - QuantFin - Computational Finance Pub Date : 2024-04-17 DOI: arxiv-2404.11526

Jacob Fein-Ashley

引用次数: 0

Learning tensor networks with parameter dependence for Fourier-based option pricing 为基于傅立叶的期权定价学习具有参数依赖性的张量网络

arXiv - QuantFin - Computational Finance Pub Date : 2024-04-17 DOI: arxiv-2405.00701

Rihito Sakurai, Haruto Takahashi, Koichi Miyamoto

引用次数: 0

Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training 通过持续预训练构建领域指定的日语金融大语言模型

arXiv - QuantFin - Computational Finance Pub Date : 2024-04-16 DOI: arxiv-2404.10555

Masanori Hirano, Kentaro Imajo

引用次数: 0

Quantum Mechanics of Human Perception, Behaviour and Decision-Making: A Do-It-Yourself Model Kit for Modelling Optical Illusions and Opinion Formation in Social Networks 人类感知、行为和决策的量子力学》：为社交网络中的光学幻觉和意见形成建模的自助模型套件

arXiv - QuantFin - Computational Finance Pub Date : 2024-04-16 DOI: arxiv-2404.10554

Ivan S. Maksymov

{"title":"Quantum Mechanics of Human Perception, Behaviour and Decision-Making: A Do-It-Yourself Model Kit for Modelling Optical Illusions and Opinion Formation in Social Networks","authors":"Ivan S. Maksymov","doi":"arxiv-2404.10554","DOIUrl":"https://doi.org/arxiv-2404.10554","url":null,"abstract":"On the surface, behavioural science and physics seem to be two disparate\u0000fields of research. However, a closer examination of problems solved by them\u0000reveals that they are uniquely related to one another. Exemplified by the\u0000theories of quantum mind, cognition and decision-making, this unique\u0000relationship serves as the topic of this chapter. Surveying the current\u0000academic journal papers and scholarly monographs, we present an alternative\u0000vision of the role of quantum mechanics in the modern studies of human\u0000perception, behaviour and decision-making. To that end, we mostly aim to answer\u0000the 'how' question, deliberately avoiding complex mathematical concepts but\u0000developing a technically simple computational code that the readers can modify\u0000to design their own quantum-inspired models. We also present several practical\u0000examples of the application of the computation code and outline several\u0000plausible scenarios, where quantum models based on the proposed do-it-yourself\u0000model kit can help understand the differences between the behaviour of\u0000individuals and social groups.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140614836","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

Quantum Risk Analysis of Financial Derivatives 金融衍生品的量子风险分析

arXiv - QuantFin - Computational Finance Pub Date : 2024-04-15 DOI: arxiv-2404.10088

Nikitas Stamatopoulos, B. David Clader, Stefan Woerner, William J. Zeng

{"title":"Quantum Risk Analysis of Financial Derivatives","authors":"Nikitas Stamatopoulos, B. David Clader, Stefan Woerner, William J. Zeng","doi":"arxiv-2404.10088","DOIUrl":"https://doi.org/arxiv-2404.10088","url":null,"abstract":"We introduce two quantum algorithms to compute the Value at Risk (VaR) and\u0000Conditional Value at Risk (CVaR) of financial derivatives using quantum\u0000computers: the first by applying existing ideas from quantum risk analysis to\u0000derivative pricing, and the second based on a novel approach using Quantum\u0000Signal Processing (QSP). Previous work in the literature has shown that quantum\u0000advantage is possible in the context of individual derivative pricing and that\u0000advantage can be leveraged in a straightforward manner in the estimation of the\u0000VaR and CVaR. The algorithms we introduce in this work aim to provide an\u0000additional advantage by encoding the derivative price over multiple market\u0000scenarios in superposition and computing the desired values by applying\u0000appropriate transformations to the quantum system. We perform complexity and\u0000error analysis of both algorithms, and show that while the two algorithms have\u0000the same asymptotic scaling the QSP-based approach requires significantly fewer\u0000quantum resources for the same target accuracy. Additionally, by numerically\u0000simulating both quantum and classical VaR algorithms, we demonstrate that the\u0000quantum algorithm can extract additional advantage from a quantum computer\u0000compared to individual derivative pricing. Specifically, we show that under\u0000certain conditions VaR estimation can lower the latest published estimates of\u0000the logical clock rate required for quantum advantage in derivative pricing by\u0000up to $sim 30$x. In light of these results, we are encouraged that our\u0000formulation of derivative pricing in the QSP framework may be further leveraged\u0000for quantum advantage in other relevant financial applications, and that\u0000quantum computers could be harnessed more efficiently by considering problems\u0000in the financial sector at a higher level.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140614876","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

Experimental Analysis of Deep Hedging Using Artificial Market Simulations for Underlying Asset Simulators 利用人工市场模拟进行深度套期保值的基础资产模拟器实验分析

arXiv - QuantFin - Computational Finance Pub Date : 2024-04-15 DOI: arxiv-2404.09462

Masanori Hirano

{"title":"Experimental Analysis of Deep Hedging Using Artificial Market Simulations for Underlying Asset Simulators","authors":"Masanori Hirano","doi":"arxiv-2404.09462","DOIUrl":"https://doi.org/arxiv-2404.09462","url":null,"abstract":"Derivative hedging and pricing are important and continuously studied topics\u0000in financial markets. Recently, deep hedging has been proposed as a promising\u0000approach that uses deep learning to approximate the optimal hedging strategy\u0000and can handle incomplete markets. However, deep hedging usually requires\u0000underlying asset simulations, and it is challenging to select the best model\u0000for such simulations. This study proposes a new approach using artificial\u0000market simulations for underlying asset simulations in deep hedging. Artificial\u0000market simulations can replicate the stylized facts of financial markets, and\u0000they seem to be a promising approach for deep hedging. We investigate the\u0000effectiveness of the proposed approach by comparing its results with those of\u0000the traditional approach, which uses mathematical finance models such as\u0000Brownian motion and Heston models for underlying asset simulations. The results\u0000show that the proposed approach can achieve almost the same level of\u0000performance as the traditional approach without mathematical finance models.\u0000Finally, we also reveal that the proposed approach has some limitations in\u0000terms of performance under certain conditions.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140566004","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

Enhancing path-integral approximation for non-linear diffusion with neural network 用神经网络增强非线性扩散的路径积分近似法

arXiv - QuantFin - Computational Finance Pub Date : 2024-04-13 DOI: arxiv-2404.08903

Anna Knezevic

引用次数: 0

A backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations 基于后向微分深度学习的高维非线性后向随机微分方程求解算法

arXiv - QuantFin - Computational Finance Pub Date : 2024-04-12 DOI: arxiv-2404.08456

Lorenc Kapllani, Long Teng

{"title":"A backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations","authors":"Lorenc Kapllani, Long Teng","doi":"arxiv-2404.08456","DOIUrl":"https://doi.org/arxiv-2404.08456","url":null,"abstract":"In this work, we propose a novel backward differential deep learning-based\u0000algorithm for solving high-dimensional nonlinear backward stochastic\u0000differential equations (BSDEs), where the deep neural network (DNN) models are\u0000trained not only on the inputs and labels but also the differentials of the\u0000corresponding labels. This is motivated by the fact that differential deep\u0000learning can provide an efficient approximation of the labels and their\u0000derivatives with respect to inputs. The BSDEs are reformulated as differential\u0000deep learning problems by using Malliavin calculus. The Malliavin derivatives\u0000of solution to a BSDE satisfy themselves another BSDE, resulting thus in a\u0000system of BSDEs. Such formulation requires the estimation of the solution, its\u0000gradient, and the Hessian matrix, represented by the triple of processes\u0000$left(Y, Z, Gammaright).$ All the integrals within this system are\u0000discretized by using the Euler-Maruyama method. Subsequently, DNNs are employed\u0000to approximate the triple of these unknown processes. The DNN parameters are\u0000backwardly optimized at each time step by minimizing a differential learning\u0000type loss function, which is defined as a weighted sum of the dynamics of the\u0000discretized BSDE system, with the first term providing the dynamics of the\u0000process $Y$ and the other the process $Z$. An error analysis is carried out to\u0000show the convergence of the proposed algorithm. Various numerical experiments\u0000up to $50$ dimensions are provided to demonstrate the high efficiency. Both\u0000theoretically and numerically, it is demonstrated that our proposed scheme is\u0000more efficient compared to other contemporary deep learning-based\u0000methodologies, especially in the computation of the process $Gamma$.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565999","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