发帖
雷达卡
英文文献：Option Pricing using Realized Volatility-利用已实现波动率的期权定价
英文文献作者：Lars Stentoft
英文文献摘要：
In the present paper we suggest to model Realized Volatility, an estimate of daily volatility based on high frequency data, as an Inverse Gaussian distributed variable with time varying mean, and we examine the joint properties of Realized Volatility and asset returns. We derive the appropriate dynamics to be used for option pricing purposes in this framework, and we show that our model explains some of the mispricings found when using traditional option pricing models based on interdaily data. We then show explicitly that a Generalized Autoregressive Conditional Heteroskedastic model with Normal Inverse Gaussian distributed innovations is the corresponding benchmark model when only daily data is used. Finally, we perform an empirical analysis using stock options for three large American companies, and we show that in all cases our model performs significantly better than the corresponding benchmark model estimated on return data alone. Hence the paper provides evidence on the value of using high frequency data for option pricing purposes.
摘要本文将已实现波动率(基于高频数据的日波动率估计)建模为具有时变均值的逆高斯分布变量，并考察了已实现波动率与资产收益的联合性质。在这个框架中，我们推导出用于期权定价目的的适当的动态，并且我们表明，我们的模型解释了基于日间数据使用传统期权定价模型时发现的一些错误定价。当只使用日数据时，一个具有正态反高斯分布创新点的广义自回归条件异方差模型是相应的基准模型。最后，我们对三家大型美国公司的股票期权进行了实证分析，结果表明，在所有情况下，我们的模型都显著优于仅根据收益数据估计的基准模型。因此，本文为使用高频数据进行期权定价的价值提供了证据。
京公网安备 11010802022788号
