Using Logistic Regression of Machine Learning Method to Evaluate American Options
學年 110
學期 1
出版（發表）日期 2021-08-19
作品名稱 Using Logistic Regression of Machine Learning Method to Evaluate American Options
作品名稱（其他語言）
著者 Lee, Y. H.
單位
出版者
著錄名稱、卷期、頁數 Asian Journal of Economics, Business and Accounting 21(11), p.34-39
摘要 Aims: The main purpose of this study is to understand whether Logistic regression has certain benefits in the evaluation of American options. As far as the Monte Carlo method is concerned, the least square method is traditionally used to evaluate American options, but in fact, Logistic regression is generally quite good in classification performance. Therefore, this study wants to know if Logistic regression can improve the accuracy of evaluation in American options. Study design: The selection of options parameters required in the simulation process mainly considers the average level of actual market conditions in the past few years in terms of dividend yield and risk-free interest rate. The part of the stock price and the strike price mainly considers three different situations: in-the-money, out-of-the-money and at the money. Methodology: This study applied the Logistic regression in Monte Carlo method for the pricing of American. Uses the ability of logistic regression to help determine whether the American option should be exercised early for each stock price path. The validity of the proposed method is supported by some vanilla put cases testing. The parameters used in all cases tested are considered the current state of the market. Conclusion: This study demonstrates the effectiveness of the proposed approach using numerical examples, revealing significant improvements in numerical efficiency and accuracy. Several test cases showed that the relative error of all tests are below 1%.
關鍵字 American options;Logistic regression;option pricing;Monte Carlo
語言 en
ISSN 2456-639X
期刊性質 國外
收錄於
產學合作
通訊作者 Lee, Y. H
審稿制度 是
國別 USA
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121917 )

SDGS 優質教育