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A comparison of biased simulation schemes for stochastic volatility models

机译:随机波动率模型的有偏仿真方案比较

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摘要

Using an Euler discretization to simulate a mean-reverting CEV process gives rise to the problem that while the process itself is guaranteed to be nonnegative, the discretization is not. Although an exact and efficient simulation algorithm exists for this process, at present this is not the case for the CEV-SV stochastic volatility model, with the Heston model as a special case, where the variance is modelled as a mean-reverting CEV process. Consequently, when using an Euler discretization, one must carefully think about how to fix negative variances. Our contribution is threefold. Firstly, we unify all Euler fixes into a single general framework. Secondly, we introduce the new full truncation scheme, tailored to minimize the positive bias found when pricing European options. Thirdly and finally, we numerically compare all Euler fixes to recent quasi-second order schemes of Kahl and Jäckel, and Ninomiya and Victoir, as well as to the exact scheme of Broadie and Kaya. The choice of fix is found to be extremely important. The full truncation scheme outperforms all considered biased schemes in terms of bias and root-mean-squared error.
机译:使用Euler离散化来模拟均值回复CEV过程会产生一个问题,即虽然过程本身被保证是非负的,但离散化不是。尽管针对此过程存在精确且有效的仿真算法,但对于CEV-SV随机波动率模型而言,目前情况并非如此,其中Heston模型为特例,其中方差建模为均值回复CEV过程。因此,在使用欧拉离散时,必须仔细考虑如何修正负方差。我们的贡献是三倍。首先,我们将所有Euler修补程序统一为一个通用框架。其次,我们引入了新的完全截断方案,该方案旨在将对欧洲期权定价时发现的正偏差最小化。第三,最后,我们在数值上将所有欧拉定理与Kahl和Jäckel,Ninomiya和Victoir的最近准二阶方案以及Broadie和Kaya的精确方案进行比较。发现修复程序的选择非常重要。就偏差和均方根误差而言,完整的截断方案优于所有考虑的偏差方案。

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