查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

114-2 357295 財務數值方法 1 3.0 3 ★ 劉亮志資財所 17 0 　

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
114-2	357295	財務數值方法	1	3.0	3	★	劉亮志	資財所	17	0

教學大綱與進度：

教師姓名劉亮志　教師諮商時間(Office Hours)

Email lcliu@ntut.edu.tw

最後更新時間 2026-02-24 10:12:20

課程大綱本課程將延續財務工程導論的內容，將持續在衍生性商品評價、避險以及風險控管等問題上繼續深入探討；本學期將把焦點放在股票衍生性商品與利率衍生性商品的評價與避險上，並利用C(或C++)和Matlab兩種工具來執行樹狀結構與蒙地卡羅法以評價選擇權與公司債的價值。 This course will still focus on the methods for bonds and derivatives pricing and hedging, such as the evaluation of stock and interest rate options. For implementation purposes associated with the concepts introduced, such as the tree structure, two programming languages for computation will be provided: C (or C++ ) and Matlab.

課程進度 Week 1 : Course Introduction and Preliminary Week 2 : Fundamental Economic Features of Stock Options - 1 Week 3 : Fundamental Economic Features of Stock Options - 2 Week 4 : Functions and Their Applications - 1 Week 5 : Functions and Their Applications - 2 Week 6 : Introduction to Fundamental Option Pricing Theory - 1 Week 7 : Introduction to Fundamental Option Pricing Theory - 2 Week 8 : Introduction to Fundamental Option Pricing Theory - 3 Week 9 : Midterm Review and Exercise Week 10 : Tree Method and Extension of Option Pricing Theory - 1 Week 11 : Tree Method and Extension of Option Pricing Theory - 2 Week 12 : Tree Method and Extension of Option Pricing Theory - 3 Week 13 : Tree Method and Extension of Option Pricing Theory - 4 Week 14 : Monte Carlo Simulation and Option Pricing - 1 Week 15 : Monte Carlo Simulation and Option Pricing - 2 Week 16 : Monte Carlo Simulation and Option Pricing - 3 Week 17 : Take-Home Final Exam Week 18 : Course Review

評量方式與標準 Exercise: 50% Midterm Exercise: 10% Take-Home Final Exam: 40%

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：否
Use of self-made materials

課程諮詢管道 Tuesday 10:00~12:00 at Technical Building 433

延伸教學與資源 ● 參與式創新教學 (Participatory & Innovative Teaching)
強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。
Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification.

課程對應SDGs指標 SDG4：優質教育（Quality Education）

課程是否導入AI ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）

備註

教師姓名	劉亮志　教師諮商時間(Office Hours)
Email	lcliu@ntut.edu.tw
最後更新時間	2026-02-24 10:12:20
課程大綱	本課程將延續財務工程導論的內容，將持續在衍生性商品評價、避險以及風險控管等問題上繼續深入探討；本學期將把焦點放在股票衍生性商品與利率衍生性商品的評價與避險上，並利用C(或C++)和Matlab兩種工具來執行樹狀結構與蒙地卡羅法以評價選擇權與公司債的價值。 This course will still focus on the methods for bonds and derivatives pricing and hedging, such as the evaluation of stock and interest rate options. For implementation purposes associated with the concepts introduced, such as the tree structure, two programming languages for computation will be provided: C (or C++ ) and Matlab.
課程進度	Week 1 : Course Introduction and Preliminary Week 2 : Fundamental Economic Features of Stock Options - 1 Week 3 : Fundamental Economic Features of Stock Options - 2 Week 4 : Functions and Their Applications - 1 Week 5 : Functions and Their Applications - 2 Week 6 : Introduction to Fundamental Option Pricing Theory - 1 Week 7 : Introduction to Fundamental Option Pricing Theory - 2 Week 8 : Introduction to Fundamental Option Pricing Theory - 3 Week 9 : Midterm Review and Exercise Week 10 : Tree Method and Extension of Option Pricing Theory - 1 Week 11 : Tree Method and Extension of Option Pricing Theory - 2 Week 12 : Tree Method and Extension of Option Pricing Theory - 3 Week 13 : Tree Method and Extension of Option Pricing Theory - 4 Week 14 : Monte Carlo Simulation and Option Pricing - 1 Week 15 : Monte Carlo Simulation and Option Pricing - 2 Week 16 : Monte Carlo Simulation and Option Pricing - 3 Week 17 : Take-Home Final Exam Week 18 : Course Review
評量方式與標準	Exercise: 50% Midterm Exercise: 10% Take-Home Final Exam: 40%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：否 Use of self-made materials
課程諮詢管道	Tuesday 10:00~12:00 at Technical Building 433
延伸教學與資源	● 參與式創新教學 (Participatory & Innovative Teaching) 強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。 Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification.
課程對應SDGs指標	SDG4：優質教育（Quality Education）
課程是否導入AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
備註