Automatic adjoint differentiation for special functions involving expectations

IF 0.8 4区 经济学 Q4 BUSINESS, FINANCE Journal of Computational Finance Pub Date : 2023-01-01 DOI:10.21314/jcf.2023.007
José Brito, Andrei Goloubentsev, Evgeny Gonacharov
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引用次数: 1

Abstract

In this paper we explain how to compute gradients of functions of the form G = ½∑mi=1(Eyi - Ci)2, which often appear in the calibration of stochastic models, using automatic adjoint differentiation and parallelization. We expand on the work of Goloubentsev and Lakshtanov and give approaches that are faster and easier to implement. We also provide an implementation of our methods and apply the technique to calibrate European options.
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包含期望的特殊函数的自动伴随微分
本文解释了如何利用自动伴随微分和并行化方法计算随机模型标定中经常出现的形式为G =½∑mi=1(Eyi - Ci)2的函数的梯度。我们对Goloubentsev和Lakshtanov的工作进行了扩展,并给出了更快、更容易实施的方法。我们还提供了我们方法的实现,并应用该技术来校准欧洲期权。
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来源期刊
CiteScore
0.90
自引率
0.00%
发文量
8
期刊介绍: The Journal of Computational Finance is an international peer-reviewed journal dedicated to advancing knowledge in the area of financial mathematics. The journal is focused on the measurement, management and analysis of financial risk, and provides detailed insight into numerical and computational techniques in the pricing, hedging and risk management of financial instruments. The journal welcomes papers dealing with innovative computational techniques in the following areas: Numerical solutions of pricing equations: finite differences, finite elements, and spectral techniques in one and multiple dimensions. Simulation approaches in pricing and risk management: advances in Monte Carlo and quasi-Monte Carlo methodologies; new strategies for market factors simulation. Optimization techniques in hedging and risk management. Fundamental numerical analysis relevant to finance: effect of boundary treatments on accuracy; new discretization of time-series analysis. Developments in free-boundary problems in finance: alternative ways and numerical implications in American option pricing.
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