金融商品不斷地推陳出新，對投資者而言，如何去篩選信賴性之結構型商品成為重要議題，對發行劵商，如何在新產品上市之前，設定合理的避險比率也成為重要課題。本文研究使用蒙地卡羅模擬法，分析結構型商品的績效與增進美式選擇權定價時之精確度。Longstaff and Schwartz (2001) 最小平方蒙地卡羅模擬法廣泛應用至複雜之衍生性商品。然而，最佳之迴歸模型不易尋求，包括基底函數之種類，與基底函數次方項之選用。本論文首先將冪次多項式與最佳履約邊界值結合，成為修正後最佳履約決策。結果顯示，若基底函數取二次多項式，有修正之最佳履約決策可減少10% RMSE。第二部份個案分析，單資產以美式價外重設型認購權證進行模擬，本文首先利用兩階段模擬技巧，分別對避險比率、觸及價內機率與溢價之分析。多資產個案以結構型商品-高受益票劵(ELN)為例，為了觀察價格變化趨勢，本論文藉由變動相關係數的個數、變動幅度與不同損益方式進行分析。希望藉由此類分析，能提供投資者與發行劵商在決策上更多更有用之資訊。Due to constant changes in global financial products, how to select a reliable structured note is important to investors. How to set the reasonable hedge ratio before product kickoff is also important to issuers. The purpose of this research is to analyze the performance of structured notes and to improve the accuracy for pricing American options via Monte Carlo simulation. The least-squares Monte Carlo approach proposed by Longstaff and Schwartz (2001) claimed to price American options with complex derivatives. However, it seems difficult to apply this approach in choosing the optimal regression settings, including different basis functions and the degree of these basis functions. This paper first combines the power polynomials with optimal exercise boundary as modified optimal exercise rule. The results in the single asset imply that the modified rule with optimal exercise boundary can decrease nearly 10% RMSE when using the square degree of power polynomials. The second part of this paper is case study. In order to find the reset probability for the call warrant, the two Monte Carlo simulation systems are used in this research. For the final ELN case, we analyze the tendency of price when changing the number and the amplitude of correlation factors together with different payoffs. With these results, this paper aims to bring contributions to issuers and investors.
