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非高斯动态波动率模型及其计量是现代金融的重要研究内容.基于Levy-GARCH动态波动率模型,引入了序贯贝叶斯参数学习方法,并进行S&P500指数的跳跃风险溢价估计、欧式期权定价、风险测度评估的实证研究.研究表明,相比傅里叶变换的极大似然估计,序贯贝叶斯参数学习显著改进了各模型的期权定价能力.研究还发现,带跳跃随机模型的风险度量更加准确;跳跃风险溢价明显高于扩散风险的溢价;跳跃强度越大,风险的市场价格越高.
Non-Gaussian dynamic volatility model and its measurement are the important contents of modern finance.Based on the Levy-GARCH dynamic volatility model, the sequential Bayesian parameter learning method is introduced and the jump risk premium of S & P 500 index is estimated. The European option pricing , Risk measure evaluation.The research shows that compared with the maximum likelihood estimation of Fourier transform, sequential Bayesian parameter learning significantly improves the option pricing ability of each model.The study also found that, with jump stochastic model The risk measurement is more accurate; the jump risk premium is obviously higher than the premium of the diffusion risk; the bigger the jump intensity is, the higher the market risk is.