自2008年次貸危機以來，國內金融主管機關對於高風險的投資商品，做了許多規範與限制，以避免當年雷曼兄弟倒閉造成許多投資人鉅額損失的事件再度發生。現今法令下，連結多資產股權之結構型商品(FCN結構型商品)，只能針對高資產且有相關金融知識及投資經驗的專業投資人做銷售，此商品在銀行銷售商品中，屬於風險等級最高的商品。本研究針對FCN結構型商品，進行風險探討。
本研究先說明商品的合約及各項情境，以蒙地卡羅模擬分析法，針對 FCN結構型商品，進行數值分析。分析的標的包含兩資產股權連結、三資產股權連結，以及其他不同發行條件的FCN結構型商品。分析內容包含計算商品合約價值，各情境機率分析，商品風險分析，商品的績效衡量以及數個因子的敏感度分析。其中，敏感度分析是針對連結資產的波動度、相關係數，以及商品的期數做計算。
數值分析結果顯示，無論是兩資產股權連結或是三資產股權連結，波動度越高之連結標的，FCN結構型商品到期時虧損的風險越高，相關係數較高的連結標的，商品到期時虧損的風險略有降低，而商品期數長短與商品到期時虧損的風險關係不明確。比較類夏普指標的數值，若是只想拿固定配息的投資人，可以選擇低波動度的連結標的，或是階梯式觸及出場價的合約，發生轉換股票的機率最低，商品的績效最好。
投資FCN結構型商品需要事先仔細評估風險，並且選擇合適的連結標的。若遇上連結標的走勢大幅下跌的情況，除了領到固定配息，在合約到期之後會承接股票，適合想領取高固定配息並有意願承接股票的專業投資人。

After 2008 financial crisis, the domestic financial authorities have made many restrictions on high-risk investment products to avoid the recurrence of the incident that caused huge losses to many investors when Lehman Brothers collapsed. Under the current law, Fixed Coupon Note (FCN) can only be sold to the professional investors with high assets and relevant financial knowledge and investment experience. This research focuses on the risk of Fixed Coupon Note.
This research describes the contracts and scenarios of FCN first. The numerical analysis is carried out for FCN by Monte Carlo simulation analysis method. The subject of analysis includes two-asset equity link, three-asset equity link, and other FCN products with different issuance conditions. The content of analysis includes calculation of product contract value, probability analysis of each scenario, risk analysis, performance measurement and the sensitivity analysis of several factors. The sensitivity analysis includes the volatilities, correlations of linked assets, and the tenor of products.
The numerical analysis results show that whether it is a two-asset equity link or a three-asset equity link, the higher the volatility of the linked target, the higher the risk of loss when the FCN expires; the linked equity with a higher correlation, the risk of loss is slightly reduced at the product expiry; and the relationship between the tenors and the risk of FCN is not precise. Comparing the values of Sharpe-like indicators, if investors only want to take fixed coupons, they can choose low-volatility linked targets, or contracts with stepped knock-out prices which have the lowest probability of converting stocks.

中文部分
1. 陳達新 (2009)，「財務數學：隨機過程與衍生性金融商品評價」，雙葉書廊。
2. 陳達新、周恆志 (2018)，「財務風險管理 四版：工具、衡量與未來發展」，雙葉書廊。
3. 廖四郎、王昭文 (2017)，「期貨與選擇權：策略型交易與套利實務 第五版修訂」，新陸書局。
4. 薛立言，劉亞秋 (2018)，「債券市場概論 四版」，華泰文化。
5. 陳威光 (2019)，「金融創新與商品個案」，新陸書局。
6. 陳根元 (2003)，「結構型商品交易制度介紹」, 證券暨期貨月刊，第廿一卷，第七期。
7. 熊肇穆 (2003)，「結構型商品簡介」, 證券暨期貨月刊，第廿一卷，第十一期。
8. 丁于芝 (2015)，「結構型商品之評價與分析―以多資產股權連結結構型商品與保息型匯率連結結構型商品為例」，國立中央大學，碩士論文。
9. 蔡祥恩 (2019)，「以Heston模型評價雙資產區間計息結構型商品」，國立台灣科技大學，碩士論文。
10. 黃德中 (2021)，「股權結構型商品之評價與風險分析」，國立中央大學，碩士論文。
11. 廖高論 (2014)，「股權結構式商品之評價與分析」，國立中央大學，碩士論文。
12. 蔡明宏 (2018)，「金融商品評價與風險分析：一籃子資產結構型商品與匯率衍生性商品為例」，國立中央大學，碩士論文。
13. 李惠文 (2021)，「結構型商品獲利影響因素探討」，東吳大學商學院，碩士論文。
英文部分
1. Artzner, P., Delbaen, F., Eber, J. M., & Heath, D. (1999), “Coherent Measures of Risk”, Mathematical Finance, 9, 203-228.
2. Black, F. and Scholes, M. (1973), “The Pricing of Options and Corporate Liabilities”, Journal of Political Economy, 8, 637-654.
3. Boyle, P.P. (1977), “Options; Monte Carlo Simulations”, Journal of Financial Economics, Vol. 4, Issue 3, pp. 323-338.
4. Boyle, P. P., J. Evnine, and S. Gibbs (1989), "Numerical Evaluation of Multivariate Contingent Claims," Review of Financial Studies, 2, 241-50.
5. Boyle, P. P., and Y. K. Tse (1990), "An Algorithm for Computing Values of Options on the Maximum or Minimum of Several Assets," Journal of Financial and Quantitative Analysis, 25,215-27.
6. Contreras, M. , Llanquihuén, A. and Villena, M. (2016), “On the Solution of the Multi-Asset Black-Scholes Model: Correlations, Eigenvalues and Geometry”, Journal of Mathematical Finance, 6, 562-579.
7. Dowd, K. (1999), “A value at risk approach to risk-return analysis”, The Journal of Portfolio Management, 25(4), 60-67.
8. Espen Gaarder Haug (2007), “The Complete Guide to Option Pricing Formulas 2nd Edition”, McGraw-Hill.
9. F. Black and M. Scholes (1973), “The Pricing of Options and Corporate Liabilities”, Journal of Political Economy, Vol. 81, No. 3, pp. 637-654.
10. Heston, Steven L. (1993), “A closed-form solution for options with stochastic volatility with applications to bond and currency options”, The Review of Financial Studies, 6(2), 327-343.
11. Heynen, Ronald C. and Kat, Harry M. (1994) “Partial Barrier Options”, The Journal of Financial Engineering, Volume 3, No. 3.
12. John C. Hull (2021), “Options, Futures, and Other Derivatives, Global Edition 11th Edition”, Pearson.
13. Johnson, H. (1987), “Options on the Maximum or the Minimum of Several Assets”, Journal of Financial and Quantitative Analysis, 22(3), 277-283.
14. Justin London (2004), “Modeling Derivatives in C++ 1st Edition”, John Wiley & Sons, Inc.
15. Justin London (2006), “Modeling Derivatives Applications in Matlab, C++, and Excel”, FT Press.
16. Le Sourd, V. (2007), “Performance measurement for traditional investment”, Financial Analysts Journal, 58(4), 36-52.
17. Longstaff, Francis A. and Eduardo S. Schwartz (2001), “Valuing American options by simulation: a simple least-squares approach”, The Review of Financial Studies, 14(1), 113-147.
18. Merton, R.C. (1973), “Theory of Rational Option Pricing”, Bell Journal of Economics and Management Science, 4, 141-183.
19. Rich, D. R., and D. M. Chance (1993): "An Alternative Approach to the Pricing of Options on Multiple Assets", Journal of Financial Engineering, 2(3), 271-285.
20. Sharpe W. F. (1966), “Mutual Fund Performance”, Journal of Business, January 1966, pp. 119-138.
21. Steven E. Shreve (2004), “Stochastic Calculus for Finance I – The Binomial Asset
Pricing Model”, Springer.
22. Steven E. Shreve (2004), “Stochastic Calculus for Finance II – Continuous-Time Models”, Springer.
23. Stulz, R. (1982), “Options on the minimum or the maximum of two risky assets: Analysis and applications”, Journal of Financial Economics, 10(2), 161-185.