Option Pricing and Neural Networks in Artificial Intelligence (옵션 가격 결정과 신경망)

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Seki Kim (Sungkyunkwan University)

In the era of artificial intelligence, the use of neural networks to estimate the value of options for financial assets and instruments is no longer the exclusive domain of a few experts. Many researchers are now using it for the robustness of option pricing. In this talk, various methods with Mathematica are being attempted, including applying neural network models to financial mathematical option models such as Black-Scholes and Merton Jump-Diffusion models. Additionally, these values obtained could be analyzed and compared with measurements based on existing financial models.

인공지능 시대에 금융 자산과 상품에 대한 옵션의 가치를 계산하기 위한 신경망의 사용은 더이상 몇몇 전문가 들의 것은 아니다. 지금은 많은 연구자 들이 이를 이용하여 안정적인 금융 상품의 가치를 측정하고 있다. 여기서 금융 수학적 옵션 모델인 블랙-쇼즈, 머튼 점프 확산 모델 등에 신경망 모델을 적용하여 옵션의 가치를 측정하며 이를 위한 Mathematica의 실질적 사용이 포함된 여러 방법 들을 시도한다. 또 기존의 모델에 따른 가치 측정 값과 비교하여 분석하고자 한다.

About the Speaker

Seki Kim is a Professor Emeritus of Mathematics at Sungkyunkwan University. He completed his Ph.D in Mathematics at the University of Iowa in 1995. After a postdoctral fellowship and instructorship at Seoul National University, he joined the Faculty of Mathematics at Sungkyunkwan University in 1997. His research interest is on Mathematical Finance and its Computational Methods.