使用随机网格进行深度校准

IF 1.5 4区经济学 Q3 BUSINESS, FINANCE

Quantitative Finance Pub Date : 2024-04-12 DOI:10.1080/14697688.2024.2332375

Fabio Baschetti, Giacomo Bormetti, Pietro Rossi

引用次数: 0

摘要

我们提出了一种基于神经网络的随机波动率模型校准方法，该方法结合了 Horvath 等人的开创性网格方法[深度学习波动率：深度学习波动性：一种深度神经网络...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Deep calibration with random grids

We propose a neural network-based approach to calibrating stochastic volatility models, which combines the pioneering grid approach by Horvath et al. [Deep learning volatility: A deep neural networ...

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来源期刊

Quantitative Finance 社会科学-数学跨学科应用

CiteScore

3.20

自引率

7.70%

发文量

102

审稿时长

4-8 weeks

期刊介绍： The frontiers of finance are shifting rapidly, driven in part by the increasing use of quantitative methods in the field. Quantitative Finance welcomes original research articles that reflect the dynamism of this area. The journal provides an interdisciplinary forum for presenting both theoretical and empirical approaches and offers rapid publication of original new work with high standards of quality. The readership is broad, embracing researchers and practitioners across a range of specialisms and within a variety of organizations. All articles should aim to be of interest to this broad readership.