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McMC estimation of multiscale stochastic volatility models with applications

机译:多尺度随机波动率模型的McMC估计及其应用

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In this paper we propose to use Markov chain Monte Carlo methods to estimate the parameters of stochastic volatility models with several factors varying at different time scales. The originality of our approach, in contrast with classical factor models is the identification of two factors driving univariate series at well-separated time scales. This is tested with simulated data as well as foreign exchange data. Furthermore, we exploit the model calibration problem of implied volatility surface by postulating a computational scheme, which consists of McMC estimation and variance reduction techniques in MC/QMC simulations for option evaluation under multi-scale stochastic volatility models. Empirical studies and its extension are discussed.
机译:在本文中,我们建议使用马尔可夫链蒙特卡罗方法来估计随机波动率模型的参数,该模型具有在不同时间尺度上变化的几个因素。与经典因子模型相比,我们方法的独创性是在分离的时间尺度上确定驱动单变量序列的两个因子。这已通过模拟数据以及外汇数据进行了测试。此外,我们通过提出一种计算方案来利用隐含波动率表面的模型校准问题,该方案由MC / QMC模拟中的McMC估计和方差减少技术组成,用于多尺度随机波动率模型下的期权评估。讨论了实证研究及其扩展。

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