CQF课程

Jason 1年前 ⋅ 348 阅读

Module 1

Building Blocks of Quant Finance

 
This module introduces the rules of applied Itô calculus as a modeling framework. We build tools in both stochastic calculus and martingale theory and look at simple stochastic differential equations and their associated Fokker-Planck and Kolmogorov equations.
本模块介绍应用伊藤微积分作为建模框架的规则。我们在随机微积分和马丁格尔理论中构建工具，并研究简单的随机微分方程及其相关的福克-普朗克和柯尔莫哥罗夫方程。
  • The Random Nature of Prices: Examination of data, unpredictability, the need for probabilistic models, drift and volatility.
价格的随机性：数据检查，不可预测性，对概率模型的需要，偏移和波动率。
  • Probability Preliminaries: Review of discrete and continuous random variables, transition density functions, moments and important distributions, the Central Limit Theorem.
概率初步研究：回顾离散和连续随机变量，转移密度函数，动量和重要分布，中心极限定理。
  • Fokker-Planck and Kolmogorov Equations: similarity solutions.
福克-普朗克和柯尔莫哥罗夫方程：相似性解决方案。
  • Applied Itô Calculus: Discrete-time random walks, continuous Wiener processes via rescaling and passing to the limit, quadratic variation, Itô integrals and Itô’s lemma.
应用伊藤微积分：离散时间随机游走，通过缩放和传递到极限，二次变化的连续维纳过程，伊藤积分和伊藤引理。
  • Simulating and manipulating stochastic differential equations.
模拟和操作随机微分方程。
  • The Binomial Model: Up and down moves, delta hedging and self-financing replication, no arbitrage, a pricing model and risk-neutral probabilities.
二项式模型：向上和向下运动，增量对冲和自筹资金复制，无套利，定价模型和风险中性概率。
  • Discrete Martingales: Probabilistic universe, sample space, filtration and probability measures, conditional expectations, change of measure.
离散型马丁格尔：概率空间，样本空间，过滤和概率测量，条件期望，测量变化。
  • Continuous Martingales: Discrete and continuous time martingales, Markov vs Martingale, Ito integrals and martingales, stochastic processes as martingale and tools of the trade.
连续型马丁格尔: 离散的和连续时间的马丁格尔，马尔科夫 vs 马丁格尔，伊藤积分和马丁格尔，随机过程作为马丁格尔和交易工具。
  • Discrete Time Finance: Binomial Model, risk-neutrality, replication, risk-neutral probabilities - the connection between expectations and option pricing.
离散时间金融序列：二项式模型，风险中立，复制，风险中性概率- 预期与期权定价之间的联系。
 
 
 
Preparatory reading
预备阅读
Paul Wilmott, Paul Wilmott Introduces Quantitative Finance, second edition, 2007, Wiley (Chapters 3,4,5,7)
Further reading
进一步阅读
  • James D. Hamilton, Time Series Analysis, 1994, Princeton University Press
  • John A. Rice, Mathematical Statistics and Data Analysis, 1988, Wadsworth & Brooks/Cole
  • Salih N. Neftci, An Introduction to the Mathematics of Financial Derivatives, 1996, Academic Press (General reference)
 
Lifelong Learning lectures
终身学习讲座课程
  • Linear Algebra - Riaz Ahmad
  • Stochastic Calculus - Riaz Ahmad
  • Differential Equations - Riaz Ahmad
  • Methods for Quant Finance I, II - Riaz Ahmad
  • Martingales - Riaz Ahmad

Module 2

Quantitative Risk and Return

This module deals with the classical portfolio theory of Markowitz, the capital asset pricing model and more recent developments of these theories. We investigate risk and reward, looking at risk management metrics such as VaR.
在这个模块涉及马科维茨经典的投资组合理论，资产定价模型以及这些理论的最新发展。我们评估风险和回报，查看 VaR 等风险管理指标。
 
  • Modern Portfolio Theory: Expected returns, variances and covariances, benefits of diversification, the opportunity set and the efficient frontier, the Sharpe ratio, utility functions and the Black-Litterman Model.
现代投资组合理论(MPT)： 预期回报，方差和协方差，多元化的好处，机会空间和有效前沿，夏普比率，效用函数和布莱克-利特曼模型。
  • Capital Asset Pricing Model: Single-index model, beta, diversification, optimal portfolios, the multi-index model.
资产定价模型(CAP)：单一指数模型，Beta，分散化，最佳投资组合，多指数模型。
  • Portfolio Optimization: Formulation, implementation and use of calculus to solve constrained optimization.
投资组合优化: 公式，实现和使用微积分来解决约束优化。
  • Risk Regulation and Basel III: Definition of capital, evolution of Basel, Basel III and market risk, key provisions.
风险管理和巴塞尔协议 III: 资本的定义，巴塞尔协议、巴塞尔协议 III 的演变和市场风险，关键条款。
  • Collateral and Margins: Expected Exposure (EE), types of collateral, calculation initial and variation margins, minimum transfer amount (MTA).
抵押品和保证金: 风险敞口(EE)，抵押品类型，计算初始条件和变化边距，最低转账金额(MTA)
  • Value at Risk: Profit and loss for simple portfolios, tails of distributions, Monte Carlo simulations and historical simulations, stress testing and worst-case scenarios.
风险价值：简单投资组合的损益，尾部分布，压力测试和最坏情况。
  • Liquidity Asset Liability Management: Gap analysis, liabilities and contingencies, the role of derivatives and nonderivatives in liquidity, Liquidity Coverage Ratio (LCR), Net Stable Funding Rate (NSFR).
流动性资产负债管理：差距分析(缺口分析)，负债和不可预见费用，衍生品和非衍生品在流动性中的作用，流动性覆盖率(LCR)，净稳定资金利率。
  • Volatility Cluster: Concept and evidence.
波动性集群：概念和证明。
  • Properties of Daily and High-Frequency Asset Returns: Average values, standard deviations, five-minute returns contrasted with daily returns, intraday volatility.
每日和高频资产回报的属性：平均值，标准差，五分钟回报和每日回报的鲜明对比，日内波动模型。
  • Volatility Models: The ARCH framework, why ARCH models are popular, the GARCH model, ARCH models, asymmetric ARCH models and econometric methods.
波动率模型: ARCH 框架，为什么 ARCH 模型很受欢迎，GARCH 模型，ARCH 模型，非对称 ARCH 模型和计量经济学方法。
 
Preparatory reading
  • Paul Wilmott, Paul Wilmott Introduces Quantitative Finance, second edition, 2007, Wiley (Chapters 1, 2, 3, 20-22)
  • Stephen J. Taylor, Asset Price Dynamics, Volatility and Predication, 2007, Princeton University Press (Chapters 2, 4, 9-10, 12)
 
Further reading
  • Edwin J. Elton & Martin J. Gruber, Modern Portfolio Theory and Investment Analysis, 1995, Wiley
  • Robert C. Merton, Continuous Time Finance, 1992, Blackwell
  • Nassim Taleb, Dynamic Hedging, 1996, Wiley
  • David G. Luenberger, Investment Science, June 1997, Oxford University Press (Chapters 6 & 7)
  • Jonathon E. Ingersoll, Theory of Financial Decision Making, 1987, Rowman & Littlefield (Chapter 4)
  • Salih .N. Neftci, An Introduction to the Mathematics of Financial Derivatives, 1996, Academic Press (general reference)
  • Ruey S. Tsay, Analysis of Financial Time Series, third edition, 2010, Wiley
  • Attilio Meucci, Risk and Asset Allocation, 2009, Springer Finance
  • Edwin J. Elton, Martin J. Gruber, Stephen J. Brown, William N.. Goetzmann, Modern Portfolio Theory and Investment, ninth edition, 2010, Wiley
 
Lifelong Learning lectures
(Available to Full program and Level II delegates)
  • Fundamentals of Optimization - Riaz Ahmad
  • Investment Lessons from Blackjack and Gambling - Paul Wilmott
  • Symmetric Downside Sharpe Ratio - William Ziemba
  • Beyond Black-Litterman: Views on Generic Markets - Attilio Meucci
  • Financial Modeling using Garch Processes - Kyriakos Chourdakis

Module 3

 
Equities and Currencies
股票和货币
 
The Black-Scholes theory, built on the principles of delta hedging and no arbitrage, has been very successful and fruitful as a theoretical model and in practice. This module explains the theory and results using different kinds of mathematics to make the delegate familiar with techniques in current use.
Black-Scholes理论建立在delta对冲和无套利原则的基础上，作为一种理论模型和实践已经非常成功和富有成效。本模块使用不同类型的数学解释理论和结果，使学员熟悉当前使用的技术。
 
  • The Black-Scholes Model: A stochastic differential equation for an asset price, the delta-hedged portfolio and self-financing replication, no arbitrage, the pricing partial differential equation and simple solutions.
布莱克-斯科尔斯模型：资产价格的随机微分方程，Delta对冲投资组合和自筹资金复制，无套利，定价偏微分方程和简单解。
  • Martingales: The probabilistic mathematics underlying derivatives theory, Girsanov, change of measure and Feynman-Kac.
鞅：衍生理论的概率数学基础，Girsanov，测度变化和Feynman-Kac。
  • Early Exercise: American options, elimination of arbitrage, modifying the binomial method, gradient conditions, formulation as a free-boundary problem.
提前履约: 美式期权，消除套利，修改二项式方法，梯度条件，公式化为自由边界问题。
  • The Greeks: delta, gamma, theta, vega and rho and their uses in hedging.
希腊字母: Delta，Gamma，Theta，Vega和Rho及其在对冲中的用途。
  • Numerical Analysis: Monte Carlo simulation and the explicit finite-difference method.
数值分析：蒙特卡罗模拟和显式有限差分法。
  • Further Numerical Analysis: Crank-Nicolson, and Douglas multi-time level methods, convergence, accuracy and stability.
更深层次的数值分析：Crank Nicolson和Douglas多时间层方法，收敛性、准确性和稳定性。
  • Exotic Options: OTC contracts and their mathematical analysis.
奇异期权：场外交易合约及其数学分析。
  • Derivatives Market Practice: Examination of common practices and historical perspective of option pricing.
衍生品市场实践：审查期权定价的常见做法和历史观点。
  • Advanced Volatility Modeling: Implied vs actual, local volatility surfaces, non-linear pricing equations.
高级波动率建模：隐含与实际的局部波动率表面，非线性定价方程。
 
 
 
 
Preparatory reading
  • Paul Wilmott, Paul Wilmott Introduces Quantitative Finance, second edition, 2007, Wiley (Chapters 6, 8, 27-30)
  • Paul Wilmott, Paul Wilmott on Quantitative Finance, second edition, 2006, Wiley (Chapters 14, 22-29, 37, 45-53, 57, 76-83)
  • Espen G. Haug, Derivatives: Models on Models, 2007, Wiley (Chapter 1 & 2, and on the CD Know Your Weapon 1 & 2)
 
Further reading
  • Nassim Taleb, Dynamic Hedging, 1996, Wiley
  • John C. Hull, Options, Futures and Other Derivatives, fifth edition, 2002, Prentice-Hall
  • K.W. Morton and D.F. Mayers, Numerical Solution of Partial Differential Equations: An Introduction, 1994, Cambridge University Press
  • Gordon .D. Smith, Numerical Solution of Partial Differential Equations, 1985, Oxford University Press
  • Martin Baxter and Andrew Rennie, Financial Calculus: An Introduction to Derivative Pricing, 2001, Cambridge University Press
  • Steven E. Shreve, Stochastic Calculus for Finance II: Continuous – Time Models v.2, 2000, Springer Finance
  • Richard L. Burden and Douglas J. Faires, Numerical Analysis, tenth edition, 2016, Cengage Learning
 
Lifelong Learning lectures
  • Black-Scholes World, Mathematical Methods and Introduction to Numerical Methods - Riaz Ahmad
  • Infinite Variance - Nassim Nicholas Taleb
  • Introduction to Volatility Trading and Variance Swaps - Sebastien Bossu
  • Advanced Equity Models: Pricing, Calibration and Monte Carlo Simulation - Wim Schoutens
  • Discrete Hedging and Transaction Costs - Paul Wilmott
  • Ten Ways to Derive Black-Scholes - Paul Wilmott
  • Volatility Arbitrage and How to Hedge - Paul Wilmott

Module 4

 
Data Science and Machine Learning I
数据科学和机器学习 I
 
This module will introduce the latest techniques used for machine learning in finance. Starting with a comprehensive overview of the topic, the essential mathematical tools followed by a deep dive into the topic of supervised learning , including regression methods, K-Nearest neighbors, Support Vector Machines, Ensemble methods and many more.
本模块将介绍用于金融领域的机器学习最新技术。从对该主题的全面概述开始，基本数学工具，然后深入研究监督学习主题，包括回归方法，k-最近邻，支持向量机，集成方法等等。
 
  • Introduction to Machine Learning: What is Mathematical Modeling, Classic Tools, Principal Techniques, Principal techniques for Machine Learning, Supervised & Unsupervised Learning, Reinforcement Learning.
机器学习介绍: 什么是数学建模，经典工具，主要技术，机器学习的主要技术，监督和无监督学习，强化学习。
 
  • Maths Toolbox: Maximum Likelihood Estimation, Cost/Loss Function, Gradient Descent, Stochastic Gradient Descent, Bias & Variance, Lagrange Multipliers, Principal Component Analysis.
数学工具箱: 最大似然估计，损失函数，梯度下降，随机梯度下降，偏差和方差，拉格朗日乘数，主成分分析。
 
  • Supervised Learning I: Linear Regression, Penalized Regression: lasso, Ridge & Elastic Net, Logistic, Softmax Regression, Decision Trees, Ensemble Models -Bagging & Boosting.
监督学习 I: 线性回归，惩罚性回归: 套索，脊和弹性网，逻辑回归，softmax 逻辑回归，决策树，集成模型-装袋和升压。
 
  • Logistic Regression, Support Vector Machines, Cluster Analysis: BIRCH, hierarchical, K-mean, Expectation maximization, DBSCAN, OPTICS and mean shift Kalman filtering.
逻辑回归，支持向量机，聚类分析: BIRCH 层次聚类算法，分层，K 均值，期望最大化，DBSCAN 基于密度的聚类算法，OPTICS 和 Mean-Shift 和 Kalman 滤波器。
 
  • Machine Learning Lab: Supervised Learning Implementation, Python - Scikit Learn; Support Vector Machines.
机器学习实验: 监督学习练习，Python - Scikit Learn，支持向量机。
 
 
 
 
Further Reading
  • Trevor Hastie et al., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2009 (2nd edition), Springer
  • Martin Odersky et al., Programming in Scala: Updated for Scala 2.12, 2016 (3rd edition), Artima Press
  • Macros Lopez de Prado, Advances in Financial Machine Learning, 2018, Wiley
  • Christopher Bishop, Pattern Recognition and Machine Learning, 2006, Springer
  • Max Kuhn and Kjell Johnson, Applied Predictive Analytics, 2013, Springer

Module 5

 
Data Science and Machine Learning II
数据科学和机器学习 II
 
In this module we will explore several more methods used for machine learning in finance. Starting with unsupervised learning, Deep learning and Neural networks, we will move into natural language processing and reinforcement learning. You will study the theoretical framework, analyze practical case studies exploring how these techniques are used within finance.
在本学习模块中，我们将会探索机器学习用于金融中的更多方法，从无监督学习、深度学习和神经网络开始，我们将进入自然语言处理和强化学习。您将学习理论框架，分析实际案例研究，探索这些技术如何在金融中使用。
 
  • Machine Learning & Predictive Analytics: Regression, regression in high dimensions, support vector machines, dimension reduction: principal component analysis (PCA), kernel PCA, non-negative matrix decomposition.
机器学习和预测分析：归回，高维回归，支持向量机，降维：主成分分析(PCA)，核主成分分析(Kernel PCA)，非负矩阵分解。
 
  • Unsupervised Learning I: K Means Clustering; Self Organizing Maps; Strengths & Weakness of HAC and SOM.
无监督学习 I：K 聚类，自组织映射，HAC 和 SOM(自组织映射)的优势和劣势。
 
  • Unsupervised Learning II: t-SNE; UMAP; Autoencoders.
无监督学习 II: t-SNE 降维学习方法，UMAP 降维算法，Autoencoders 自动编码器神经网络模型。
 
  • Deep Learning & Neural Networks: Structural Building Blocks; Forward & Back Propagation; Multi Output Perceptron; Building Neural Networks.
深度学习和神经网络：结构框架，正向和反向传播，多输出感知机，构建神经网络。
 
  • Neural Network Architectures: Feedforward, Recurrent, Long Short Term Memory, Convolutional, Generative Adversarial.
神经网络架构：正反馈，循环神经网络，长短期记忆，卷积神经，对抗生成网络。
 
  • Natural Language Processing: Pre-processing; Word Vectorizations, Word2Vec; Deep Learning & NLP Tools.
自然语言处理：预处理；词矢量化，Word2Vec 算法模型，深度学习和 NLP 工具。
 
  • Reinforcement Learning: Multi-armed Bandit; Exploration Strategies; Risk Sensitivity.
强化学习: 多臂老虎机(MAB)，探测策略，风险敏感度。
 
  • AI Based Algo Trading Strategies Using Python: Financial data analysis with Python and pandas, application of classification algorithms, vectorized backtesting, risk analysis for algo trading strategies.
使用 Python 的基于 AI 算法的交易策略：使用 Python 和 pandas 进行金融数据分析，分类算法的应用，矢量化回归测试，算法交易策略的风险分析。
 
Further Reading
 
  • William McKinney, Python for Data Analysis, 2013 O’Reilly
  • Foster Provost and Tom Fawcett, Data Science for Business, 2013, O’Reilly
  • Gareth James et al., An Introduction to Statistical Learning, 2013, Springer
  • Yves Hilipisch, Python for Finance, 2014, (2nd edition), O’Reilly

Module 6

 
Fixed Income and Credit
固定收益和信贷
 
In this module we will review the multitude of interest models used within the industry, focusing on the implementation and limitations of each model. You will learn about credit and how credit risk models are used in quant finance, including structural, reduced form as well as copula models.
在本模块中，我们将回顾行业内使用的一些有趣模型，重点关注每种模型的实现和有限性，您将了解信用以及如何在量化金融中使用信用风险模型，包括结构，简化形式以及 copula 模型
 
  • Fixed-Income Products: Fixed and floating rates, bonds, swaps, caps and floors, FRAs and other delta products.
固定收益产品：固定收益率和浮动利率，债券，掉期合约，利率上下限，FRAs 和其他 Delta 产品。
 
  • Yield, Duration and Convexity: Definitions, use and limitations, bootstrapping to build up the yield curve from bonds and swaps.
利率，久期，凸性：定义，使用和限制，从债券和掉期合约中建立收益曲线。
 
  • Curve Stripping: reference rates & basis spreads, OIS discounting and dual-curve stripping, cross-currency basis curve, cost of funds and the credit crisis.
曲线剥离：参考利率和基础利差，OIS 贴现和双曲线剥离，货币基础曲线，资金成本和信贷危机。
 
  • Interpolation Methods: piece wise constant forwards, piece wise linear, cubic splines, smart quadratics, quartics, monontone convex splines.
插值法: 分段恒定向前，分段线性，三次样条曲线，智能二次型，四次曲线，单调凸样条。
 
  • Current Market Practices: Money vs. scrip, holiday calendars, business day rules, and schedule generation, day count fractions.
货币市场实践：货币和代币，假期日历，工作日规则和日交易计划，日计算分数。
 
  • Stochastic Interest Rate Models, one and two factors: Transferring ideas from the equity world, differences from the equity world, popular models, data analysis.
随机利率模型，一个和两个因素: 从股票世界转移观点，不同于股票世界，流行的模型，数据分析。
 
  • Calibration: Fitting the yield curve in simple models, use and abuse.
校准：用简单模型拟合收益率曲线，使用和滥用。
 
  • Heath, Jarrow and Morton Model: Modeling the yield curve. Determining risk factors of yield curve evolution and optimal volatility structure by PCA. Pricing interest rate derivatives by Monte Carlo.
赫斯-伽罗-莫顿模型: 对收益率曲线的建模，PCA 确定收益率曲线演变的风险因素和最佳波动率结构，蒙特卡洛衍生品定价。
 
  • The Libor Market Model: (Also Brace, Gatarek and Musiela). Calibrating the reference volatility structure by fitting to caplet or swaption data.
伦敦银行同业拆借利率市场模型: （还有布雷斯，加塔雷克和穆西埃拉)，通过拟合 caplet 或互换数据来校准参考波动率结构。
 
  • Advanced Monte Carlo Techniques: Low-discrepancy series for numerical quadrature. Use for option pricing, speculation and scenario analysis.
高级蒙特卡罗技术: 数值求积的低差异级数，用于期权定价，投机和情景分析。
 
  • SABR Arbitrage Free SABR Model: Managing volatility risks, smiles, local volatility models, reduction to the effective forward equation, arbitrage free boundary conditions.
SABR 无套利 SABR 模型：管理波动率风险，微笑，局部波动率模型，简化为有效的远期方程，无套利边界条件。
 
  • Credit Risk and Credit Derivatives: Products and uses, credit derivatives, qualitative description of instruments, applications.
信用风险和信用衍生品: 产品和用途，信用衍生品，工具的定性描述，应用。
 
Structural and Intensity models used for credit risk.
用于信用风险的结构和强度模型。
 
  • CDS Pricing, Market Approach: Implied default probability, recovery rate, default time modeling, building a spreadsheet on CDS pricing.
CDS 定价，市场方法：隐含违约概率，回收率，违约时间建模，建立 CDS 定价的电子表格。
 
  • Synthetic CDO Pricing: The default probability distribution, default correlation, tranche sensitivity, pricing spread.
综合 CDO 定价：违约概率分布，违约相关性，部分敏感性，定价价差。
 
  • Implementation: CDO/copula modeling using spreadsheets.
实践：使用电子表格进行 CDO/copula 建模。
 
  • Correlation and State Dependence: correlation, linear correlation, analyzing correlation, sensitivity and state dependence.
相关性和状态依赖：相关性，线性相关，分析相关性，敏感性和状态依赖性。
 
  • Risk of Default: The hazard rate, implied hazard rate, stochastic hazard rate and credit rating, capital structure arbitrage.
违约风险：风险率、隐含危险率、随机危险率和信用评级、资本结构套利。
 
  • Copulas: Pricing basket credit instruments by simulation.
连接函数：通过模拟定价一揽子信贷工具。
 
  • Statistical Methods in Estimating Default Probability: ratings migration and transition matrices and Markov processes.
评估违约概率的统计方法: 分级迁移转移矩阵和马尔可夫过程。
 
  • X-Valuation Adjustment: Background, default probability and exposure, collateral, CVA, regulatory requirements, DVA and FVA, Counterparty Lab in excel, credit default swaps, bootstrapping CDS spreads, interest rate swaps.
X-Valuation 调整：背景，违约概率和风险敞口，抵押品，CVA，监管要求，DVA 和 FVA，对手 Lab in excel，信用违约互换，自举 CDS 价差，利率互换。
 
Preparatory Reading
  • Jon Gregory, The xVA Challenge: Counterparty Credit Risk, Funding, Collateral and Capital, third edition, 2015, Wiley (Chapters 4-7, 10, 12)
  • Paul Wilmott, Paul Wilmott Introduces Quantitative Finance, 2007, Wiley (Chapters 14-19)
  • Paul Wilmott, Paul Wilmott on Quantitative Finance, second edition, 2006, Wiley (Chapters 30-33, 36, 37, 39-42)
  • Peter Jaeckel, Monte Carlo Methods in Finance, 2002, Wiley (Chapters 1-14)
 
Further Reading
  • Avinash K. Dixit and Robert S. Pindyck, Investment Under Uncertainty, 1994, Princeton University Press
  • Darrell Duffie & Kenneth J. Singleton, Credit Risk: Pricing, Measurement, and Management, 2003, Princeton University Press
  • Gunter Loffler and Peter Posche, Credit Risk Modeling using Excel and VBA, 2007, Wiley
  • George Chacko et al., Credit Derivatives: A Primer on Credit Risk, Modeling, and Instruments, 2006, Wharton School Publishing (Chapters 3, 5)
  • Philipp J. Schoenbucher, Credit Derivatives Pricing Models: Model, Pricing and Implementation, 2003, Wiley (Chapters 2, 4, 5)
  • Antulio N. Bomfim, Understanding Credit Derivatives and Related Instruments, 2004, Academic Press (Chapters 15, 16, 17)
  • Nassim Taleb, Dynamic Hedging, 1996, Wiley
  • John C. Hull, Options, Futures and Other Derivatives, fifth edition, 2002, Prentice-Hall
 
 
 
Advanced Electives
 
In this module you choose two from the following online electives to specialize in your area of interest. You will be required to complete a practical project relating to the electives you have chosen.
在本模块中，您可以从以下在线选修课中选择两门，以专攻您感兴趣的领域。你将被要求完成一个与你选择的选修课相关的实践项目。
 
Algorithmic Trading
算法交易
 
The use of algorithms has become an important element of modern-day financial markets, used by both the buy side and sell side. This elective will look into the techniques used by quantitative professionals who work within the area.
算法的使用已经成为现代金融市场的一个重要元素，买方和卖方都在使用。这门选修课将研究在该领域工作的定量专家使用的技术。
 
  • What is Algorithmic Trading
  • Preparing data; Back testing, analysing results and optimisation
  • Build your own algorithm
  • Alternative approaches: Paris trading Options; New Analytics
  • A career in Algorithmic trading
 
Advanced Computational Methods
高级计算方法
 
One key skill for anyone who works within quantitative finance is how to use technology to solve complex mathematical problems. This elective will look into advanced computational techniques for solving and implementing math in an efficient and succinct manner, ensuring that the right techniques are used for the right problems.
 
对于任何从事量化金融工作的人来说，一个关键技能是如何使用技术解决复杂的数学问题。这门选修课将研究先进的计算技术，以高效和简洁的方式解决和实施数学，确保正确的技术用于正确的问题。
 
  • Finite Difference Methods (algebraic approach) and application to BVP
  • Root finding
  • Interpolation
  • Numerical Integration
 
 
 
 
 
Advanced Risk Management
高级风险管理
 
In this elective, we will explore some of the recent developments in Quantitative Risk Management. We take as a point of departure the paradigms on how market risk is conceived and measured, both in the banking industry (Expected Shortfall) and under the new Basel regulatory frameworks (Fundamentals Review of the Trading Book, New Minimum, Capital of Market Risk).
 
在这门选修课中，我们将探讨量化风险管理的一些最新发展。我们以如何在银行业(预期亏空)和新的巴塞尔监管框架(交易账簿基本回顾，新的最小值，市场风险资本)下构思和衡量市场风险的范例为出发点。
 
  • Review of new developments on market risk management and measurement
  • Explore the use of extreme value of theory (EVT)
  • Explore adjoint automatic differentiation
 
Advanced Volatility Modeling
高级波动率模型
 
Volatility and being able to model volatility is a key element to any quant model. This elective will look into the common techniques used to model volatility throughout the industry. It will provide the mathematics and numerical methods for solving problems in stochastic volatility.
 
波动率和能够对波动率进行建模是任何量化模型的关键要素。本选修课将研究用于模拟整个行业的波动率的常用技术。它将提供解决随机波动率问题的数学和数值方法。
 
  • Fourier Transforms
  • Functions of a Complex Variable
  • Stochastic Volatility
  • Jump Diffusion
 
Machine Learning with Python
基于Python的机器学习
 
This elective will focus on Machine Learning and deep learning with Python applied to Finance. We will focus on techniques to retrieve financial data from open data sources.
 
这门选修课将侧重于使用Python在机器学习和深度学习在金融中的应用。我们将重点介绍从开源数据中检索财务数据的技术。
 
  • Using linear OLS regression to predict financial prices & returns
  • Using scikit-learn for machine learning with Python
  • Application to the pricing of the American options by Monte Carlo simulation
  • Applying logistic regression to classification problems
  • Predicting stock market returns as a classification problem
  • Using TensorFlow for deep learning with Python
  • Using deep learning for predicting stock market returns
 
Advanced Portfolio Management
高级投资组合管理
 
As quantitative finance becomes more important in today’s financial markets, many buyside firms are using quantitative techniques to improve their returns and better manage client capital. This elective will look into the latest techniques used by the buy side in order to achieve these goals.
 
随着量化金融在当今的金融市场中变得越来越重要，许多买方公司正在使用量化技术来提高回报并更好地管理客户资本。该选修课将研究买方为实现这些目标而使用的最新技术。
 
  • Perform a dynamic portfolio optimization, using stochastic control
  • Combine views with market data using filtering to determine the necessary parameters
  • Understand the importance of behavioural biases and be able to address them
  • Understand the implementation issues
  • Develop new insights into portfolio risk management
 
Counterparty Credit Risk Modeling
交易对手风险模型
 
Post-global financial crisis, counterparty credit risk and other related risks have become much more pronounced and need to be taken into account during the pricing and modeling stages. This elective will go through all the risks associated with the counterparty and how they are included in any modeling frameworks.
 
后全球金融危机、交易对手信用风险和其他相关风险变得更加明显，需要在定价和建模阶段加以考虑。该选修课将介绍与交易对手相关的所有风险，以及它们如何包含在任何建模框架中。
  • Credit Risk to Credit Derivatives
  • Counterparty Credit Risk: CVA, DVA, FVA
  • Interest Rates for Counterparty Risk – dynamic models and modeling
  • Interest Rate Swap CVA and implementation of dynamic model
 
Behavioural Finance for Quants
量化中的行为经济学
 
Behavioural finance and how human psychology affects our perception of the world, impacts our quantitative models and drives our financial decisions. This elective will equip delegates with tools to identify the key psychological pitfalls, use their mathematical skills to address these pitfalls and build better financial models.
 
行为金融学以及人类心理学如何影响我们对世界的感知，影响我们的定量模型并推动我们的财务决策。该选修课将为学员提供工具，以识别关键的心理陷阱，利用他们的数学技能来解决这些陷阱并建立更好的财务模型。
 
  • S ystem 1 Vs System 2
  • Behavioural Biases; Heuristic processes; Framing effects and Group processes
  • Loss aversion Vs Risk aversion; Loss aversion; SP/A theory
  • Linearity and Nonlinearity
  • Game theory
 
R for Quant Finance
基于R语言的量化金融分析
 
R is a powerful statistical programming language, with numerous tricks up its sleeves making it an ideal environment to code quant finance and data analytics applications.
 
R 是一种强大的统计编程语言，拥有众多技巧，使其成为编写量化金融和数据分析应用程序的理想环境。
 
  • Intro to R and R Studio
  • Navigate and understand packages
  • Understand data structures and data types
  • Plot charts, read and write data files
  • Write your own scripts and code
 
Risk Budgeting
风险预算
 
Rather than solving the risk-return optimization problem as in the classic (Markowitz) approach, risk budgeting focuses on risk and its limits (budgets). This elective will focus on the quant aspects of risk budgeting and how it can be applied to portfolio management.
 
风险预算不是像经典（Markowitz）方法那样解决风险回报优化问题，而是专注于风险及其极限（预算）。本选修课将侧重于风险预算的量化方面以及如何将其应用于投资组合管理。
 
  • Portfolio Construction and Measurement
  • Value at Risk in Portfolio Management
  • Risk Budgeting in Theory
  • Risk Budgeting in Practice
 
Fintech
金融科技
 
Financial technology, also known as fintech, is an economic industry composed of companies that use technology to make financial services more efficient. This elective gives an insight into the financial technology revolution and the disruption, innovation and opportunity therein.
 
金融技术，也称为金融科技，是一个利用技术使金融服务更有效率的公司组成的经济产业。这门选修课让你深入了解金融科技革命带来的变革，创新和机遇。
 
  • Intro to and History of Fintech
  • Fintech – Breaking the Financial Services Value Chain
  • FinTech Hubs
  • Technology – Blockchain; Cryptocurrencies; Big Data 102; AI 102
  • Fintech Solutions
  • The Future of Fintech
 
C++
C++编程
 
Starting with the basics of simple input via keyboard and output to screen, this elective will work through a number of topics, finishing with simple OOP.
 
从简单的键盘输入和屏幕输出开始学习C++的基础知识，该选修课将会涉及许多主题，最后将会以C++面向对象编程的简单示例结束。
  • Getting Started with the C++ Environment – First Program; Data Types; Simple Debugging
  • Control Flow and Formatting – Decision Making; File Management; Formatting Output
  • Functions – Writing User Defined Functions; Headers and Source Files
  • Intro to OOP – Simple Classes and Objects
  • Arrays and Strings

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