政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/124147

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/124147

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政大典藏 > College of Commerce > Department of Money and Banking > Theses > Item 140.119/124147

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/124147

Title:	強化學習應用於美式選擇權評價 Applying Reinforcement Learning to American Option Pricing
Authors:	許琳 Xu, Lin
Contributors:	江彌修 Chiang, Mi-Hsiu 許琳 Xu, Lin
Keywords:	美式選擇權定價強化學習最小平方策略迭代最小平方蒙地卡羅法 American option Pricing Reinforcement learning LSPI FQI LSM
Date:	2019
Issue Date:	2019-07-01 10:48:51 (UTC+8)
Abstract:	本文研究了強化學習應用於美式選擇權定價問題，首先，使用 Li, Szepesvari and Schuurmans 提出之最小平方策略迭代(LSPI)演算法學習美式賣權履約策略並進行定價，將蘋果公司美式股票選擇權之真實市場數據處理後套用於 LSPI 方法，並將 LSPI 方法與 Tsitsiklis and Van Roy提出之FQI方法和傳統最小平方蒙地卡羅法比較定價準確性。其次，使用符合金融市場之分析方式，將賣權分價內外不同情況分析，並進行敏感度分析，觀察強化學習使用之參數對於定價結果之影響。模擬結果表示，LSPI 方法與 FQI 方法總體優於 LSM 方法，強化學習對於愈價內之賣權定價愈準確。本文發現強化學習在商品定價領域仍有很大研究潛力，特別是模擬路徑方式與執行動作多樣性方面值得進一步討論。 In this paper we apply the reinforcement learning method to American options pricing. We mainly consider the least squares policy iteration (LSPI) proposed by Li, Szepesvari and Schuurmans(2009) to learn the exercise policy and pricing method of American put options. We price AAPL American stock option with processed real market data, and compare the accuracy between LSPI, FQI proposed by Tsitsiklis and Van Roy(2001), and the standard least square Monte Carlo method (LSM). In order to investigate the influence of parameters used in LSPI on pricing results, the analysis method in financial market, sensitivity analysis is carried out under different situations which are divided according to whether the put option is in-the-money or out-of-the-money. The simulation result shows that LSPI and FQI are superior to LSM in general, and LSPI is more accurate in pricing deeper in-the-money put option. We also find that the reinforcement learning method still has great research potential in the field of derivatives pricing. In particular, there is a need for further investigation on simulation method of price path or selecting action variety.
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Description:	碩士國立政治大學金融學系 106352047
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0106352047
Data Type:	thesis
DOI:	10.6814/NCCU201900058
Appears in Collections:	[Department of Money and Banking] Theses

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