讲座题目：Closed-form Implied Volatility Surfaces for Stochastic Volatility Models

讲座时间：2017年5月22日（周一）下午3：30

主讲人：李辰旭

讲座地点：金融学院二楼报告厅

主讲人简介

李辰旭博士，北京大学光华管理学院副教授，博士生导师，主要从事金融计量和金融工程等专题研究。多项研究成果已成功发表在Annals of Statistics，Journal of Econometrics， Mathematics of Operations Research，Mathematical Finance等重要学术期刊,多次获国家自然科学基金资助。其研究成果于2015年荣获全国第七届高等学校科学研究优秀成果奖（人文社会科学）。作为研究的实践，参与金融机构的衍生品定价与量化交易模型的开发和改进。在北京大学光华管理学院他讲授金融中的数学方法，随机分析与应用，管理学中的统计方法，商务统计分析，数据分析与统计决策等课程。2004年获中国科学技术大学数学学士学位，2010年获美国哥伦比亚大学博士学位。他兴趣广泛，对文化艺术特别是钢琴演奏及钢琴艺术鉴赏和研究拥有诚挚的热爱。

讲座简介

Abstract: In this paper, we propose and implement a methodology for explicitly disentangling the classical and long-standing empirical puzzle that which stochastic volatility model best fits the implied volatility data, instead of resorting to standard econometric procedures. As an illustrative example, S&P 500 index's implied volatility surface is investigated through two tasks. First, by exploring and setting some representative stylized facts on the implied volatility surface as criterion, we conduct comparative and empirical analysis on dissecting prevalent stochastic volatility models and consequently pinpointing their advantages and disadvantages. Second, rather than exhaustively assessing more possible models by exploring more stylized facts, as an alternative resort, we ultimately construct novel data-driven implied stochastic volatility models directly based on observed market shape characteristics of the implied volatility surface. To accomplish these two tasks, we develop an indispensable tool -- a closed-form bivariate-expansion approximation of shape characteristics of implied volatility surface under general stochastic volatility models, which provides crucial convenience and flexibility in revealing the explicit relations between stochastic and implied volatilities, as demonstrated by an example on examining impact of different model parameters on the shape of the implied volatility surface.