Holiday
TSMC台積206 TaTbTc
*This is an advanced course. 課程說明: 計算金融可視為現代金融理論的主要實踐方法,它針對不同的應用問題發展出特定的演算法,其中有些屬於古典,已具備高效率(漸進最佳; asymptotic optimality)的特性;有些則具創新與研發的性質,並與AI/ML高度結合。 本課程兼具金融研究與實務,介紹並定義相關的計算金融問題,探討適當的數值方法(含AI/ML, GPU, Quantum等),再輔以實際財經資料加以驗 證數學模型。 課程主軸包括 Introduction to AI/ML 金融工程 (FE, Financial Engineering) 金融科技 (FT, Financial Technology) 去中心化金融 (DeFi, Decentralized Finance) 量子金融 (Quantum Finance) 初探;相關的數值演算法將會加以介紹。 量子金融 (Quantum Finance) 初探;相關的數值演算法將會加以介紹。
Course keywords: 金融衍生品,定價,避險,投組優化,深度學習,financial derivatives, pricing, hedging, portfolio optimization, deep learning Prerequisites: Linear Algebra or equivalent courses. Basic Python programming. Course Topics include but not limited to: Monte Carlo and (Randomized) Quasi Monte Carlo methods (MCQMC): theory and applications in finance Large Deviation Theory and Its Applications in Finance: short dated option and VIX term structures AI/ML in Finance: Technical Analysis including indicators and chart pattern recognition. Trading strategies. Stock Price Prediction, R32;Modern Portfolio Theory and Black-Litterman Model ETFs as Quantitative Investment passive ETFs such as general and leverage/inversion ETFs, thematic ETFs such as ESG AI Robo Advisor as Fintech’s Investment Management feature engineering, AI prediction models, Robo Advisor Hedging Strategies portfolio insurance, hedging by futures and options Miscellaneous: Risk Management, Digital Assets Tail risk estimation (VaR/CVaR), systemic risk, stock tokenization 參考教科書: G. Strang. Linear Algebra and Learning from Data. Wellesley-Cambridge Press; First Edition (February 28, 2019) ETF 量化投資學,韓傳祥,五南書局, 2022年。(第3版) 計量財務金融-金融科技,韓傳祥,新陸書局, 2018年。 金融隨機計算,韓傳祥,新陸書局, 2012年。 亦會提供上課補充講義以及平台使用 Reference links: URL: http://mx.nthu.edu.tw/~chhan/ (and several online platform therein) Computational Finance: http://my.nthu.edu.tw/~finteck/CFLab.html Blog: https://medium.com/@qffers1 有條件開放在報告撰寫與 program coding 使用生成式AI於課程產出 Conditionally open for term essays and copiloted programming; please specify how generative AI will be used in course output Grading Policy: Homework Assignment: 25 %,Midterm Report: 25%, Final Report: 40%,General Participation: 10%. Extra Credit: 3-5% (each event: 1%)
MON | TUE | WED | THU | FRI | |
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15:30716:20 | |||||
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18:30a19:20 | |||||
19:30b20:20 | |||||
20:30c21:20 |
Average Percentage 80.33
Std. Deviation 14.87
本課程為16週課程
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