英文标题：
《Most-likely-path in Asian option pricing under local volatility models》
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作者：
Louis-Pierre Arguin, Nien-Lin Liu, and Tai-Ho Wang
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最新提交年份：
2018
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英文摘要：
  This article addresses the problem of approximating the price of options on discrete and continuous arithmetic average of the underlying, i.e. discretely and continuously monitored Asian options, in local volatility models. A path-integral-type expression for option prices is obtained using a Brownian bridge representation for the transition density between consecutive sampling times and a Laplace asymptotic formula. In the limit where the sampling time window approaches zero, the option price is found to be approximated by a constrained variational problem on paths in time-price space. We refer to the optimizing path as the most-likely path (MLP). Approximation for the implied normal volatility follows accordingly. The small-time asymptotics and the existence of the MLP are also recovered rigorously using large deviation theory.
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中文摘要：
本文讨论了在局部波动率模型中，基于基础的离散和连续算术平均值（即离散和连续监测的亚洲期权）近似期权价格的问题。利用连续采样时间间转移密度的布朗桥表示和拉普拉斯渐近公式，得到了期权价格的路径积分型表达式。在采样时间窗接近零的极限下，期权价格被发现近似为时间-价格空间中路径上的约束变分问题。我们将优化路径称为最可能路径（MLP）。隐含正常波动率的近似值如下所示。利用大偏差理论严格地恢复了小时间渐近性和MLP的存在性。
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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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