主讲人:金含清 牛津大学教授
主持人:马敬堂 西南财经大学数学学院教授
时间:9月29日15:30 – 16:30
地点:西南财经大学柳林校区通博楼B412
主办单位:数学学院 科研处
主讲人简介:
金含清,博士,牛津大学教授,牛津大学NIE金融大数据实验室主任。主要从事金融统计、金融数学、行为金融学等方面的研究,在Journal of Economic Theory、Mathematical Finance、SIAM Journal on Control and Optimization、Mathematics of Operations Research等Top期刊发表了数十篇高水平的论文,其中有多篇为被高引论文。担任多个金融数学Top期刊的编委。
内容提要:
我们提出了一种创新的数据驱动期权定价方法,该方法完全依赖于历史标的资产价格的数据集。然而数据集来源于真实市场,但期权价格通常表示为风险中性测度下期望折现收益。弥合这一差距促使我们确定一个定价核过程,将期权定价转化为对原始测度期望的评估。我们通过求解一个动态效用优化问题来恢复定价核。利用深度学习技术,我们设计数据驱动的算法来求解。数值实验显示了我们方法的有效性。我们的方法适用于没有期权数据进行校准的新兴衍生品市场的衍生品定价问题。
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.