Zobrazeno 1 - 10
of 4 543
pro vyhledávání: '"Quantitative Finance - Computational Finance"'
Autor:
Takahashi, Tomonori, Mizuno, Takayuki
Despite its practical significance, generating realistic synthetic financial time series is challenging due to statistical properties known as stylized facts, such as fat tails, volatility clustering, and seasonality patterns. Various generative mode
Externí odkaz:
http://arxiv.org/abs/2410.18897
This research presents a novel approach to predicting option movements by analyzing residual transactions, which are trades that deviate from standard hedging activities. Unlike traditional methods that primarily focus on open interest and trading vo
Externí odkaz:
http://arxiv.org/abs/2410.16563
Autor:
Shu, Yizhan, Mulvey, John M.
This article explores dynamic factor allocation by analyzing the cyclical performance of factors through regime analysis. The authors focus on a U.S. equity investment universe comprising seven long-only indices representing the market and six style
Externí odkaz:
http://arxiv.org/abs/2410.14841
Autor:
Furuya, Takashi, Kratsios, Anastasis
Forward-backwards stochastic differential equations (FBSDEs) are central in optimal control, game theory, economics, and mathematical finance. Unfortunately, the available FBSDE solvers operate on \textit{individual} FBSDEs, meaning that they cannot
Externí odkaz:
http://arxiv.org/abs/2410.14788
Autor:
Stillman, Namid R., Baggott, Rory
Deep generative models are becoming increasingly used as tools for financial analysis. However, it is unclear how these models will influence financial markets, especially when they infer financial value in a semi-autonomous way. In this work, we exp
Externí odkaz:
http://arxiv.org/abs/2410.14587
Autor:
Lalor, Luca, Swishchuk, Anatoliy
We develop a deep reinforcement learning (RL) framework for an optimal market-making (MM) trading problem, specifically focusing on price processes with semi-Markov and Hawkes Jump-Diffusion dynamics. We begin by discussing the basics of RL and the d
Externí odkaz:
http://arxiv.org/abs/2410.14504
Autor:
Yang, Yuzhe, Zhang, Yifei, Hu, Yan, Guo, Yilin, Gan, Ruoli, He, Yueru, Lei, Mingcong, Zhang, Xiao, Wang, Haining, Xie, Qianqian, Huang, Jimin, Yu, Honghai, Wang, Benyou
This paper introduces the UCFE: User-Centric Financial Expertise benchmark, an innovative framework designed to evaluate the ability of large language models (LLMs) to handle complex real-world financial tasks. UCFE benchmark adopts a hybrid approach
Externí odkaz:
http://arxiv.org/abs/2410.14059
Portfolio construction has been a long-standing topic of research in finance. The computational complexity and the time taken both increase rapidly with the number of investments in the portfolio. It becomes difficult, even impossible for classic com
Externí odkaz:
http://arxiv.org/abs/2410.11997
The volatility fitting is one of the core problems in the equity derivatives business. Through a set of deterministic rules, the degrees of freedom in the implied volatility surface encoding (parametrization, density, diffusion) are defined. Whilst v
Externí odkaz:
http://arxiv.org/abs/2410.11789
In this article, we employ physics-informed residual learning (PIRL) and propose a pricing method for European options under a regime-switching framework, where closed-form solutions are not available. We demonstrate that the proposed approach serves
Externí odkaz:
http://arxiv.org/abs/2410.10474