Real-Time Derivative Pricing and Hedging with Consistent Metamodels

Guangxin Jiang (), L. Jeff Hong () and Haihui Shen ()
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Guangxin Jiang: School of Management, Harbin Institute of Technology, Harbin 150001, China
L. Jeff Hong: School of Management and School of Data Science, Fudan University, Shanghai 200433, China
Haihui Shen: Sino-US Global Logistics Institute, Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China

INFORMS Journal on Computing, 2024, vol. 36, issue 5, 1168-1189

Abstract: In derivative pricing and hedging, the consistency between the price and Greek surfaces (i.e., the Greek surfaces can be obtained by differentiating the price surface) is important in stabilizing the balance sheet and reducing the hedging cost. To build consistent surfaces of the price and Greeks for real-time decisions, we propose to use the gradient-enhanced stochastic kriging method, based on the data collected through extensive simulation experiments conducted when the market is closed. In addition to the naturally guaranteed consistency, we prove that the constructed price and Greek surfaces are more accurate than those constructed separately using stochastic kriging. Besides the consistency between the price and Greeks, we show that the partial differential equation relation between the price and Greeks, implied by the famous Feynman-Kac formula, can also be used to further improve the accuracy of the constructed surfaces. The numerical studies show that our proposed metamodeling methods work well for derivative pricing and hedging.

Keywords: simulation analytics; metamodeling; stochastic kriging; derivative pricing (search for similar items in EconPapers)
Date: 2024
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