英文标题：
《Computing the CEV option pricing formula using the semiclassical
  approximation of path integral》
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作者：
Axel A. Araneda and Marcelo J. Villena
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最新提交年份：
2018
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英文摘要：
  The Constant Elasticity of Variance (CEV) model significantly outperforms the Black-Scholes (BS) model in forecasting both prices and options. Furthermore, the CEV model has a marked advantage in capturing basic empirical regularities such as: heteroscedasticity, the leverage effect, and the volatility smile. In fact, the performance of the CEV model is comparable to most stochastic volatility models, but it is considerable easier to implement and calibrate. Nevertheless, the standard CEV model solution, using the non-central chi-square approach, still presents high computational times, specially when: i) the maturity is small, ii) the volatility is low, or iii) the elasticity of the variance tends to zero. In this paper, a new numerical method for computing the CEV model is developed. This new approach is based on the semiclassical approximation of Feynman\'s path integral. Our simulations show that the method is efficient and accurate compared to the standard CEV solution considering the pricing of European call options.
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中文摘要：
在预测价格和期权方面，恒定方差弹性（CEV）模型显著优于Black-Scholes（BS）模型。此外，CEV模型在捕捉基本经验规律方面具有显著优势，例如：异方差、杠杆效应和波动率微笑。事实上，CEV模型的性能与大多数随机波动率模型相当，但它更容易实现和校准。尽管如此，使用非中心卡方方法的标准CEV模型解仍然具有很高的计算时间，特别是在以下情况下：i）到期日很小，ii）波动率很低，或iii）方差弹性趋于零。本文提出了一种新的计算CEV模型的数值方法。这种新方法基于费曼路径积分的半经典近似。仿真结果表明，与考虑欧式看涨期权定价的标准CEV解相比，该方法有效且准确。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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