中文摘要：

摘要：为研究股市无穷跳跃和连续扩散行为特征，提出了一类能够捕捉无穷跳和扩散之间交互影响的动态跳-扩散双因子交叉回馈模型．借助L6vy过程条件特征函数、局部风险中性关系和贝叶斯学习技术，给出了动态跳-扩散随机过程的期权定价方法，并进行标准普尔500指数欧式期权标准化合约的实证研究，对比了有限跳-扩散及无穷跳一扩散模型定价差异．研究结果表明：以VG为基础的无穷跳-扩散全面优于Merton的有限跳-扩散双因子模型：跳-扩散交叉回馈模型具有最小的期权定价误差；跳跃行为相比扩散波动具有更高的持续性、更强的杠杆作用和更高的风险市场价格．

英文摘要：

In order to study the behaviors of infinite jumps and diffusions in stock markets, this paper presents a dynamic double-factor-cross-feedback jump-diffusion process that captures the interaction between jumps and diffusions. Using the conditional characteristic function of Levy process, local risk-neutral valuation re- lationship and sequential Bayesian learning technology, this paper develops a generalized risk-neutral pricing method for the dynamic jump-diffusion model, empirically studies the standardized European options on S＆P 500 index, and gives a comprehensive comparison of the pricing accuracy between the finite activity jump- diffusion model and infinite activity jump-diffusion model. Compared with the diffusion volatility, the infinite activity jump-diffusion model （VG-JD） performs better than the finite jump-diffusion model （MJ-JD）. The cross-feedback model always performs the best with the lowest errors in option valuation. It also finds that, the jumps have a higher persistence, stronger leverage effect and a higher market price.