英文标题：
《Physics and Derivatives: Effective-Potential Path-Integral
Approximations of Arrow-Debreu Densities》
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作者：
Luca Capriotti and Ruggero Vaia
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最新提交年份：
2019
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英文摘要：
We show how effective-potential path-integrals methods, stemming on a simple and nice idea originally due to Feynman and successfully employed in Physics for a variety of quantum thermodynamics applications, can be used to develop an accurate and easy-to-compute semi-analytical approximation of transition probabilities and Arrow-Debreu densities for arbitrary diffusions. We illustrate the accuracy of the method by presenting results for the Black-Karasinski and the GARCH linear models, for which the proposed approximation provides remarkably accurate results, even in regimes of high volatility, and for multi-year time horizons. The accuracy and the computational efficiency of the proposed approximation makes it a viable alternative to fully numerical schemes for a variety of derivatives pricing applications.
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中文摘要：
我们展示了有效的势路积分方法，该方法源于费曼最初提出的一个简单而好的想法，并成功地应用于各种量子热力学应用的物理中，可以用来开发一个精确且易于计算的任意扩散的跃迁概率和Arrow-Debreu密度的半解析近似。我们通过给出Black-Karasinski和GARCH线性模型的结果来说明该方法的准确性，对于这些模型，所提出的近似方法提供了非常精确的结果，即使是在高波动率的情况下，也可以提供多年时间范围的结果。该近似的准确性和计算效率使其成为各种衍生品定价应用中完全数值格式的可行替代方案。
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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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