差别

这里会显示出您选择的修订版和当前版本之间的差别。

--- mycourse:quantitative_risk_management [2022/05/24 09:17] – kk
+++ mycourse:quantitative_risk_management [2024/04/23 13:53] (当前版本) – [考试] kk
@@ 行 9: / 行 9: @@
 ===== 教学计划 =====
-| 序号 | 主题 | 时间 | 讲义 |
+| 序号 | 主题 | 时间 | 讲义 | slides | 练习 |
-| 1 | Introduction | wk1 (wk10) | [[http://kktim.cn/teaching/mlinfin/MLinFin-L01-Introduction.html|L01]]|
+| 1 | Introduction | wk5 (wk14) | [[http://kktim.cn/teaching/qfrm/QFRM-L01-Introduction.html|L01]] | [[http://kktim.cn/teaching/qfrm/QFRM-L01-Introduction-slides.html|L01]]| x |
-| 2 | Linear Regression | wk2 (wk11) | [[http://kktim.cn/teaching/mlinfin/MLinFin-L02-Linear-Regression.html|L02]] |
+| 2 | Risk Models and Market Risk | wk6 (wk15) | [[http://kktim.cn/teaching/qfrm/QFRM-L02-Risk-Models-and-Market-Risk.html|L02]] | [[http://kktim.cn/teaching/qfrm/QFRM-L02-Risk-Models-and-Market-Risk-slides.html|L02]] |  [[http://kktim.cn/teaching/qfrm/L01_market_risk_management.slides.html|ex01]] |
-| 3 | Dimension Reduction | wk3 (wk12) | [[http://kktim.cn/teaching/mlinfin/MLinFin-L03-Dimensionality-Reducion.html|L03]] |
+| 3 | Risk Models and Market Risk | wk6 (wk15) | [[http://kktim.cn/teaching/qfrm/QFRM-L03-Volatility-and-Mortgage-Backed-Securities-Risk.html|L03]] | [[http://kktim.cn/teaching/qfrm/QFRM-L03-Volatility-and-Mortgage-Backed-Securities-Risk-slides.html|L03]] | x |
-| 4 | Trees, Forest, and Boosting | wk4 (wk13) | [[http://kktim.cn/teaching/mlinfin/MLinFin-L04-Trees-Forests-Bagging-and-Boosting.html|L04]] |
+| 4 | Credit Risk Management | wk7-8 (wk16-17) | [[http://kktim.cn/teaching/qfrm/QFRM-L04-Credit-Risk-Management.html|L04]] | [[http://kktim.cn/teaching/qfrm/QFRM-L04-Credit-Risk-Management-slides.html|L04]] | [[http://kktim.cn/teaching/qfrm/L02_Credit_Risk_Management.slides.html|ex02]] |
-| 5 | Deep Neural Networks | wk5-6 (wk14-15) |  |
-| 6 | MDP & Reinforcement Learning | wk7 (wk16) |  |
-| 7 | Course Project Presentation | wk8 (wk17) | x |
-**Jupyter Notebooks** for the course are accessible via the [[https://eyun.baidu.com/s/3ggfohk3|cloud service]]. Pls with "t7me" as the password.
 ===== 教材与参考书 =====
-  - Murphy K P. Probabilistic machine learning: an introduction[M]. MIT press, 2022.
+  - Jorion P. Financial Risk Manager Handbook: FRM Part I/Part II[M]. John Wiley & Sons, 2010.
-  - Dixon M F, Halperin I, Bilokon P. Machine learning in Finance[M]. Springer International Publishing, 2020.
+  - Jorion P. Value at Risk: The New Benchmark for Managing Financial Risk[M]. McGraw Hill, 2006.
-  - Nagel S. Machine learning in asset pricing[M]. Princeton University Press, 2021.
+  - Hull J. Risk management and financial institutions, 5th Edition[M]. John Wiley & Sons, 2018.
-  - de Prado M M L. Machine learning for asset managers[M]. Cambridge University Press, 2020.
+  - McNeil A J, Frey R, Embrechts P. Quantitative risk management: concepts, techniques and tools-revised edition[M]. Princeton university press, 2015.
-  - Ashwin Rao, Tikhon Jelvis. Foundations of Reinforcement Learning with Applications in Finance[M]. Stanford University, 2022.
+===== 考试 =====
-===== 主要参考文献 =====
+  * 考试时间：第9周周4（4月25日）上午9:00-11:00
+  * 地点：文添楼314
+  * 形式：开卷、笔试、不能使用联网的计算机和手机、平板等终端
+  * 题型：简答、计算、论述
- [1] Athey S. The impact of machine learning on economics[J]. The economics of artificial intelligence: An agenda, 2018: 507-547.
- [2] Athey S, Imbens G W. Machine learning methods that economists should know about[J]. Annual Review of Economics, 2019, 11: 685-725.
- [3] Mullainathan S, Spiess J. Machine learning: an applied econometric approach[J]. Journal of Economic Perspectives, 2017, 31(2): 87-106.
+===== 主要参考文献 =====
- [4] Cohen, Samuel N. and Snow, Derek and Szpruch, Lukasz, Black-Box Model Risk in Finance (February 9, 2021). Available at SSRN: https://ssrn.com/abstract=3782412 or http://dx.doi.org/10.2139/ssrn.3782412
- [5] Goldstein I, Spatt C S, Ye M. Big data in finance[J]. The Review of Financial Studies, 2021, 34(7): 3213-3225.
- [6] Erel I, Stern L H, Tan C, et al. Selecting directors using machine learning[J]. The Review of Financial Studies, 2021, 34(7): 3226-3264.
- [7] Li K, Mai F, Shen R, et al. Measuring corporate culture using machine learning[J]. The Review of Financial Studies, 2021, 34(7): 3265-3315.
- [8] Amel-Zadeh, Amir and Calliess, Jan-Peter and Kaiser, Daniel and Roberts, Stephen, Machine Learning-Based Financial Statement Analysis (November 25, 2020). Available at SSRN: https://ssrn.com/abstract=3520684 or http://dx.doi.org/10.2139/ssrn.3520684
- [9] Gu S, Kelly B, Xiu D. Empirical asset pricing via machine learning[J]. The Review of Financial Studies, 2020, 33(5): 2223-2273.
- [10] Giglio, Stefano and Kelly, Bryan T. and Xiu, Dacheng, Factor Models, Machine Learning, and Asset Pricing (October 15, 2021). Available at SSRN: https://ssrn.com/abstract=3943284 or http://dx.doi.org/10.2139/ssrn.3943284
- [11] Gu S, Kelly B, Xiu D. Autoencoder asset pricing models[J]. Journal of Econometrics, 2021, 222(1): 429-450.
- [12] Kelly B T, Pruitt S, Su Y. Characteristics are covariances: A unified model of risk and return[J]. Journal of Financial Economics, 2019, 134(3): 501-524.
- [13] Kozak S, Nagel S, Santosh S. Shrinking the cross-section[J]. Journal of Financial Economics, 2020, 135(2): 271-292.
- [14] Tobek O, Hronec M. Does it pay to follow anomalies research? machine learning approach with international evidence[J]. Journal of Financial Markets, 2021, 56: 100588.
- [15] Baba Yara, Fahiz and Boyer, Brian H. and Davis, Carter, The Factor Model Failure Puzzle (November 19, 2021). Available at SSRN: https://ssrn.com/abstract=3967588 or http://dx.doi.org/10.2139/ssrn.3967588
- [16] Chen, Luyang and Pelger, Markus and Zhu, Jason, Deep Learning in Asset Pricing (April 4, 2019). Available at SSRN: https://ssrn.com/abstract=3350138 or http://dx.doi.org/10.2139/ssrn.3350138
- [17] Bryzgalova, Svetlana and Pelger, Markus and Zhu, Jason, Forest Through the Trees: Building Cross-Sections of Stock Returns (September 25, 2020). Available at SSRN: https://ssrn.com/abstract=3493458 or http://dx.doi.org/10.2139/ssrn.3493458
- [18] Giglio S, Liao Y, Xiu D. Thousands of alpha tests[J]. The Review of Financial Studies, 2021, 34(7): 3456-3496.
- [19] Duarte V, Fonseca J, Goodman A S, et al. Simple Allocation Rules and Optimal Portfolio Choice Over the Lifecycle[R]. National Bureau of Economic Research, 2021.
- [20] Jiang, Jingwen and Kelly, Bryan T. and Xiu, Dacheng, (Re-)Imag(in)ing Price Trends (December 1, 2020). Chicago Booth Research Paper No. 21-01, Available at SSRN: https://ssrn.com/abstract=3756587 or http://dx.doi.org/10.2139/ssrn.3756587
- [21] Ait-Sahalia Y, Xiu D. Using principal component analysis to estimate a high dimensional factor model with high-frequency data[J]. Journal of Econometrics, 2017, 201(2): 384-399.
- [22] Aït-Sahalia Y, Xiu D. Principal component analysis of high-frequency data[J]. Journal of the American Statistical Association, 2019, 114(525): 287-303.
-===== 考核方式 =====
+**[[:literature_search|查找文献的方法]]**
-  - 考核方式：课程项目
+  - Longerstaey J, Spencer M. Riskmetricstm—technical document[J]. Morgan Guaranty Trust Company of New York: New York, 1996, 51: 54.
-  - 课程项目形式：（全部或部分）复制经典论文
+  - Mina J, Xiao J Y. Return to RiskMetrics: the evolution of a standard[J]. RiskMetrics Group, 2001, 1: 1-11.
-  - DDL：15-Jun-2022, 20:00
+  - Morgan J P. Creditmetrics-technical document[J]. JP Morgan, New York, 1997.
-  - 提交内容：课程报告+PPT+项目展示视频
-  - 提交方式：百度网盘（提前30天提供搜集二维码） {{:mycourse:mlinfin-report-2022.jpg?400|}}
-  - 奖励：主动参加17周课堂展示的起评分以100分计（其他人起评分按95计算）