嘉宾介绍：Dr. Jin (金含清) is an associate professor in the Mathematical Institute at the University of Oxford. He received his MSc in Mathematics from Nankai University in 2001, and the PhD in Financial Engineering from Chinese University of Hong Kong in 2004. He staved in the department as a postdoctoral fellow for two years after graduation, and then worked in the Math department in the National University of Singapore as an assistant professor. In Jan 2008, he moved to the University of Oxford. His research interests include Mathematical Finance, Operation Research, and Stochastic Analysis. Recently his research expanded to Digital Economics and Decentralised Finance.
讲座介绍：We propose an innovative data-driven option pricing methodology that relies exclusively on the dataset of historical underlying asset prices. While the dataset is rooted in the objective world, option prices are commonly expressed as discounted expectations of their terminal payoffs in a risk-neutral world. Bridging this gap motivates us to identify a pricing kernel process, transforming option pricing into evaluation of expectations in the objective world. We recover the pricing kernel by solving a dynamic utility optimization problem whose optional wealth process is the reciprocal of the pricing kernel process, and evaluation expectations in terms of a functional optimization problem. Leveraging deep learning techniques, we design data-driven algorithms to solving over the dataset. Numerical experiments are presented to demonstrate the efficiency of our methodology. Our methodology sheds light on the derivatives pricing in emerging derivatives markets where no option data is available for calibration.
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