英文标题：
《Option Pricing via Multi-path Autoregressive Monte Carlo Approach》
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作者：
Wei-Cheng Chen, Wei-Ho Chung
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最新提交年份：
2019
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英文摘要：
The pricing of financial derivatives, which requires massive calculations and close-to-real-time operations under many trading and arbitrage scenarios, were largely infeasible in the past. However, with the advancement of modern computing, the efficiency has substantially improved. In this work, we propose and design a multi-path option pricing approach via autoregression (AR) process and Monte Carlo Simulations (MCS). Our approach learns and incorporates the price characteristics into AR process, and re-generates the price paths for options. We apply our approach to price weekly options underlying Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and compare the results with prior practiced models, e.g., Black-Scholes-Merton and Binomial Tree. The results show that our approach is comparable with prior practiced models.
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中文摘要：
在许多交易和套利情景下，金融衍生品的定价需要大量计算和接近实时的操作，在过去基本上是不可行的。然而，随着现代计算技术的进步，效率已经大大提高。在这项工作中，我们提出并设计了一种基于自回归（AR）过程和蒙特卡罗模拟（MCS）的多路径期权定价方法。我们的方法学习价格特征并将其纳入AR过程，并重新生成期权的价格路径。我们将我们的方法应用于台湾证券交易所资本化加权股票指数（TAIEX）的周期权定价，并将结果与之前的实践模型，如Black-Scholes-Merton和二叉树进行比较。结果表明，我们的方法与以前的实践模型具有可比性。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Pricing of Securities 证券定价
分类描述：Valuation and hedging of financial securities, their derivatives, and structured products
金融证券及其衍生产品和结构化产品的估值和套期保值
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