基于马尔可夫切换随机波动率模型的期权定价

Yiying Cheng
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引用次数: 0

摘要

近年来,针对金融时间序列的马尔可夫切换随机波动率(MSSV)模型的研究取得了很大进展。一些研究考虑了不同的MSSV规格,并证明了与流行的广义自回归异方差(GARCH)模型相比,对波动率的预测能力更强。然而,它们在期权定价中的应用仍然有限,部分原因是缺乏方便的集成MSSV波动率估计的封闭式期权定价公式。我们开发了这样一个封闭形式的期权定价公式和相应的对冲策略,适用于一类广泛的MSSV模型。然后,我们给出了两个最流行的MSSV模型的应用示例:马尔可夫切换多重分形(MSM)和组件驱动状态切换(CDRS)模型。我们的研究结果表明,这些模型在一天前的期权价格预测中表现良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Option Pricing with Markov Switching Stochastic Volatility Models
Recently, there has been much progress in developing Markov switching stochastic volatility (MSSV) models for financial time series. Several studies consider various MSSV specifications and document superior forecasting power for volatility compared to the popular generalized autoregressive heteroscedasticity (GARCH) models. However, their application to option pricing remains limited, partially due to the lack of convenient closed-form option pricing formulas which integrate MSSV volatility estimates. We develop such a closed-form option pricing formula and the corresponding hedging strategy for a broad class of MSSV models. We then present an example of application to two of the most popular MSSV models: Markov switching multifractal (MSM) and component-driven regime switching (CDRS) models. Our results establish that these models perform well in one-day-ahead forecasts of option prices.
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