论文标题
随时间变化的稳定过程变化
Tempered Stable Processes with Time Varying Exponential Tails
论文作者
论文摘要
在本文中，我们介绍了一个具有随机指数尾巴的新时间序列模型。该模型是基于正常的恢复稳定分布构建的，具有随时间变化的参数。该模型捕获了随机指数的尾巴，从而在期权定价中产生波动率的微笑效果和波动性项结构。此外，该模型描述了波动性的随时间变化。我们通过应用模型来分析标准普尔500指数返回数据，从经验上显示随机偏度和随机峰度。我们介绍了标准普尔500选项价格的模型参数校准的Monte-Carlo模拟技术。我们可以看到，随机指数的尾巴使模型更好地通过校准分析市场期权价格。
In this paper, we introduce a new time series model having a stochastic exponential tail. This model is constructed based on the Normal Tempered Stable distribution with a time-varying parameter. The model captures the stochastic exponential tail, which generates the volatility smile effect and volatility term structure in option pricing. Moreover, the model describes the time-varying volatility of volatility. We empirically show the stochastic skewness and stochastic kurtosis by applying the model to analyze S&P 500 index return data. We present the Monte-Carlo simulation technique for the parameter calibration of the model for the S&P 500 option prices. We can see that the stochastic exponential tail makes the model better to analyze the market option prices by the calibration.
