CQF课程简介

CQF课程简介

原文地址
CQF主页

CQF是 Certificate in Quantitative Finance (量化金融认证)的缩写，偏金融工程方向。

与CFA/FRM等认证相比，CQF侧重用随机模型分析金融现象，对数学有比较高要求，特别是随机分析。

CQF采用Project（培训+认证）模式，全程6个月（可以延期，两年内完成即可），中间有三次Exam，课程结束前有一个Project。

CQF 2020年的报名费是Full Program 约7000美金，如果分开报Level I和Level II，各自约4000美金。

通过CQF之后水平大约介于金融工程本科生和研究生之间。但是要注意CQF是速成班，通过CQF认证并不能使你达到非常高的水平，这只是学习的开始。CQF提供了终身学习课程（lifelong Learning），通过CQF认证之后可以访问这些涵盖范围相当广泛并且比较前沿的学习资料。

Overview 概述

The CQF is split into three essential phases: Preparation (optional primers), the CQF Qualification (modules and electives) and Lifelong Learning (continuous education). You can start your CQF journey in either January or June with two flexible study options - the Full Program or Level I and Level II separately.

CQF分为三个阶段：准备阶段（可选的前导课），CQF认证（模块和选修课）和终身学习（持续教育）。CQF课程每年一月或者六月开始，有两种选择：Full Program或者Level I/II分开学习（译注：课程内容上，Full Program = Level I + Level II，报Full Program比分开报Level I + Level II便宜一些）。

1. Preparation: Optional primers 前导课阶段

Start your journey by taking our optional primers in Mathematics, Python Programming and Finance. Our primers along with the one-to-one faculty support we provide, will prepare you for the rigors of the core CQF program, and are immediately available to you upon completing the enrollment process.

可选的前导课包括数学、Python编程和金融基础（译注：主要是金融市场），为CQF program做准备。基础课程提供一对一指导，完成注册流程之后立即可以开始学习前导课。

2. CQF Qualification: Modules and electives 认证阶段

The CQF qualification is comprised of six modules and two advanced electives (selected from 14), which along with three exams and a final project, need to be completed to gain the CQF qualification. Each module covers a different aspect of quant finance and consists of lectures, workshops, optional exercises and online discussions.

CQF认证包括六个模块和两个高阶选修课（总共14个选修课可供选择），通过三次Exam和一次Final project之后，可以获得CQF认证。每个模块覆盖量化金融的一个复杂的方向，由讲课、研讨会、可选的练习以及在线讨论组成

3. Lifelong Learning: Continuous education 终身学习

With more than 900 hours of lectures included in the program at no additional cost, Lifelong Learning gives delegates unlimited access to our continuously-updated library of lectures on every conceivable finance topic.

超过900小时的课程，不需要支付额外费用。终身学习阶段可以不受限制地访问一个持续更新的涵盖所有金融主题的课程库。

Primers - The foundation for success 基础课

Choose any or all of our optional primers to prepare yourself for the next stage of your CQF journey.

选择任意一个或者所有基础课，为下阶段（CQF认证）做好准备。

If your math, finance or programming knowledge is a little rusty, our unique primers are just the thing to help you polish your skills and bring you up to speed ahead of the CQF modules. The great majority of our delegates take this opportunity to refresh their knowledge and value the benefits it brings. The primers are available following the payment of your deposit.

如果数学、金融或者编程知识有些生疏，通过基础课掌握这些知识，为下一阶段学习CQF模块做准备。

Mathematics Primer 数学基础

The CQF program begins with the Mathematics Primer, 12 hours of intensive training covering all the math preliminaries you’ll need to know. The primer has been carefully designed to help you feel at home with the level of math involved in the core program.

数学基础包括12小时的高强度训练，涵盖需要掌握的所有数学基础。基础课程的内容经过精心设计，以便覆盖核心课程所需要的数学知识。

• Calculus 微积分

  • Functions and Limits 函数和极限
  • Differentiation and Integration 微分和积分
  • Complex Numbers 复数
  • Functions of Several Variables 多元函数

• Differential Equations: 微分方程

  • First Order Equations 一阶方程
  • Second and Higher Order Equations 二阶和高阶方程

• Linear Algebra: 线性代数

  • Matrices and Vectors 矩阵和向量
  • Systems of Linear Equations 线性方程系统
  • Eigenvalues and Eigenvectors 特征值和特征向量

• Probability: 概率论

  • Probability Distribution Function 概率分布函数
  • Cumulative Distribution 累积分布
  • Expectation Algebra 期望值
  • Key Discrete and Continuous Distributions Including the Normal Distribution 常见离散和连续分布
  • Central Limit Theorem 中心极限定理

• Statistics: 统计

  • General Summary Statistics 一般统计
  • Maximum Likelihood Estimator 最大似然估计
  • Regression and Correlation 回归和相关性

Python Primer Python基础

The Python Primer introduces Scientific Computing in Python. Ideal if you’re new to coding in this setting, this primer includes eight hours of training and will present the essentials of Python language in a scientific framework. Enabling you to begin writing numerical code, you’ll start with screen output and learn to write simple programs for computational purposes.

Python基础包括用Python进行科学计算，面向没有编程基础的学员。课程共计8小时，从零起步学习数字编码，屏幕输出，编写简单的科学计算程序。

The primer covers:

• Python syntax: variables; basic data types; control flow; functions, file input/output
  python语法：变量，基础数据类型，控制流，函数，文件输入输出
• Mathematical applications of Python: standard mathematical functions in the mathematics library;
  python的数学应用：数学库中的标准数学函数
• operations on numerical arrays; linear algebra; statistics; data plot
  操作数值数组；线性代数；数据plot（可视化）
• Good programming practices, documenting code; debugging
  好的编程实践；代码文档化；调试

Finance Primer 金融基础

This primer introduces the key concepts and different asset classes needed for the CQF program. Designed to benefit both those who are working in the industry and seeking a refresher, and those who have no experience within financial services but may be looking to move into this type of role, this ten-hour primer lays the foundations you’ll need to succeed.

金融基础课介绍CQF program所需要的关键概念和不同的资产类别。课程设计使用于希望更新知识的业内人士，也适用于没有金融服务相关经验，但是希望金融这个行业的人士。一共包含10小时的课程。

The primer covers:

• Macro Economics 宏观经济学
• Capital Markets in Fundamentals 资本市场
• Introduction to Money Markets 货币市场
• Time Value of Money 货币的时间价值
• Introduction to Equities 权益类资产
• Introduction to Bonds 债券
• Introduction to Swaps 互换
• Introduction to FX 外汇
• Introduction to Derivatives 衍生品
• Introduction to Commodities 大宗商品

CQF Qualification 认证阶段

Modules 模块

Module 1 - Building Blocks of Quantitative Finance

In module one, we will introduce you to the rules of applied Itô calculus as a modeling framework. You will build tools using both stochastic calculus and martingale theory and learn how to use simple stochastic differential equations and their associated Fokker- Planck and Kolmogorov equations.

在模块1会学习伊藤微积分作为建模框架。以随机分析和鞅理论为基础，学习如何使用随机微分方程和与之相关的Fokker- Planck and Kolmogorov方程.

• Lecture 1 The Random Behavior of Assets M1L1资产的随机行为

  • Different types of financial analysis 金融分析的不同流派
  • Examining time-series data to model returns 通过时间序列数据建模收益率
  • Random nature of prices 价格随机本质
  • The need for probabilistic models 概率模型
  • The Wiener process, a mathematical model of randomness 维纳过程，随机性的数学模型
  • The lognormal random walk- The most important model for equities, currencies, commodities and indices 对数正态随机游走

• Lecture 2 Binomial Model M1L2二值模型

  • A simple model for an asset price random walk 资产价格随机游走的简单模型
  • Delta hedging Delta对冲
  • No arbitrage 无套利
  • The basics of the binomial method for valuing options 用二值模型对期权定价
  • Risk neutrality 风险中性

• Lecture 3 PDEs and Transition Density Functions M1L3偏微分方程和转移概率密度函数

  • Taylor series 泰勒级数
  • A trinomial random walk 三叉随机游走
  • Transition density functions 转移概率密度函数
  • Our first stochastic differential equation 第一个随机微分方程
  • Similarity reduction to solve partial differential equations 化简和求解偏微分方程
  • Fokker-Planck and Kolmogorov equations Fokker-Planck and Kolmogorov方程（前向方程和反向方程）

• Lecture 4 Applied Stochastic Calculus 1 M1L4应用随机分析1

  • Moment Generating Function 矩量母函数
  • Construction of Brownian Motion/Wiener Process 布朗运动和维纳过程
  • Functions of a stochastic variable and Itô’s Lemma 随机变量的函数和伊藤引理
  • Applied Itô calculus 应用伊藤微积分
  • Stochastic Integration 随机积分
  • The Itô Integral 伊藤积分
  • Examples of popular Stochastic Differential Equations 常见的随机微分方程

• Lecture 5 Applied Stochastic Calculus 2 M1L5应用随机分析2

  • Extensions of Itô’s Lemma
  • Important Cases - Equities and Interest rates
  • Producing standardised Normal random variables
  • The steady state distribution

• Lecture 6 Martingales M1L6鞅

  • Binomial Model extended
  • The Probabilistic System: sample space, filtration, measures
  • Conditional and unconditional expectation
  • Change of measure and Radon-Nikodym derivative
  • Martingales and Itô calculus
  • A detour to explore some further Itô calculus
  • Exponential martingales, Girsanov and change of measure

预读

• Paul Wilmott, Paul Wilmott Introduces Quantitative Finance, second edition, 2007, Wiley (Chapters 3,4,5,7)

深入阅读

• 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)

终身学习课程
(Available to Full program and Level II delegates)

• 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 & Return

In module two, you will learn about the classical portfolio theory of Markowitz, the capital asset pricing model and recent developments of these theories. We will investigate quantitative risk and return, looking at econometric models such as the ARCH framework and risk management metrics such as VaR and how they are used in the industry.

模块二的内容包括马科维茨的经典资产组合理论、资本资产定价模型(CAPM)和这些理论近年的发展。我们会研究风险和收益量化、计量经济学模型，如ARCH框架和风险管理工具，如VaR，以及业界如何使用这些工具

• Portfolio Management M2L1组合管理
  • Measuring risk and return 度量风险和收益
  • Benefits of diversification 分散化的好处
  • Modern Portfolio Theory and the Capital Asset Pricing Model 现代组合理论和CAPM
  • The efficient frontier 有效前沿
  • Optimising your portfolio 最优化组合
  • How to analyse portfolio performance 分析组合的业绩
  • Alphas and Betas Alpha和Beta
• Fundamentals of Optimization and Application to Portfolio Selection M2L2优化理论及其在组合选择中的应用
  • Fundamentals of portfolio optimization 组合优化
  • Formulation of optimization problems 优化问题的表述方法
  • Solving unconstrained problems using calculus 用微积分求解无约束优化问题
  • Kuhn-Tucker conditions Kuhn-Tucker条件
  • Derivation of CAPM 推导CAPM
• Value at Risk & Expected Shortfall M2L3VaR和ES
  • Measuring Risk 度量风险
  • VaR and Stressed VaR VaR和压力VaR
  • Expected Shortfall and Liquidity Horizons ES和流动性窗口
  • Correlation Everywhere 相关性
  • Frontiers: Extreme Value Theory 极值理论
• Asset Returns: Key, Empirical Stylised Facts 资产收益
  • Volatility clustering: the concept and the evidence 波动性聚集（扎堆）
  • Properties of daily asset returns 日收益的特性
  • Properties of high-frequency returns 收益的高频特性
• Volatility Models: The ARCH Framework 波动率建模：ARCH框架
  • Why ARCH models are popular?
  • The original GARCH model
  • What makes a model an ARCH model?
  • Asymmetric ARCH models
  • Econometric methods
• Risk Regulation and Basel III 风险监管和巴赛尔III
  • Definition of capital 资本金
  • Evolution of Basel 巴赛尔协议的演进
  • Basel III and market risk 巴赛尔III和市场风险
  • Key provisions 关键条款
• Collateral and Margins 抵押品和保证金
  • Expected Exposure (EE) profiles for various types of instruments 不同金融工具的期望敞口（EE）
  • Types of Collateral 抵押品的种类
  • Calculation Initial and Variation Margins 计算初始保证金和可变保证金
  • Minimum transfer amount (MTA) 最低转让金额
  • ISDA / CSA documentation

预读

• 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)

深入阅读

• 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

终身学习课程
(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 & Currencies

In module three, we will explore the importance of the Black- Scholes theory as a theoretical and practical pricing model which is built on the principles of delta heading and no arbitrage. You will learn about the theory and results in the context of equities and currencies using different kinds of mathematics to make you familiar with techniques in current use.

模块三聚焦Black-Scholes理论。Black-Scholes理论是建立在delta对冲和无套利原则基础上的定价模型。通过学习股票和汇率定价中使用的数学理论和结论，熟悉当前业界正在使用的各种技术。

• Black-Scholes Model BS模型
• Martingale Theory - Applications to Option Pricing 鞅理论及其在期权定价中的应用
  • The Greeks in detail 希腊字母
  • Delta, gamma, theta, vega and rho
  • Higher-order Greeks 高阶希腊字母
  • How traders use the Greeks
• Martingales and PDEs: Which, When and Why 鞅和偏微分方程
  • Computing the price of a derivative as an expectation 衍生品定价
  • Girsanov’s theorem and change of measures Girsanov’s定理和测度变换
  • The fundamental asset pricing formula 资产定价公式
  • The Black-Scholes Formula
  • The Feynman-K_ac formula
  • Extensions to Black-Scholes: dividends and time-dependent parameters
  • Black’s formula for options on futures
• Understanding Volatility
  • The many types of volatility
  • The market prices of options tells us about volatility
  • The term structure of volatility
  • Volatility skews and smiles
  • Volatility arbitrage: Should you hedge using implied or actual volatility?
• Introduction to Numerical Methods
  • The justification for pricing by Monte Carlo simulation
  • Grids and discretization of derivatives
  • The explicit finite-difference method
• Exotic Options
  • Characterisation of exotic options
  • Time dependence (Bermudian options)
  • Path dependence and embedded decisions
  • Asian options
• Further Numerical Methods
  • Implicit finite-difference methods including Crank-Nicolson schemes
  • Douglas schemes
  • Richardson extrapolation
  • American-style exercise
  • Explicit finite-difference method for two-factor models
  • ADI and Hopscotch methods
• Derivatives Market Practice
  • Option traders now and then
  • Put-Call Parity in early 1900
  • Options Arbitrage Between London and New York (Nelson 1904)
  • Delta Hedging
  • Arbitrage in early 1900
  • Fat-Tails in Price Data
  • Some of the Big Ideas in Finance
  • Dynamic Delta Hedging
  • Bates Jump-Diffusion
• Advanced Greeks
  • The names and contract details for basic types of exotic options
  • How to classify exotic options according to important features
  • How to compare and contrast different contracts
  • Pricing exotics using Monte Carlo simulation
  • Pricing exotics via partial differential equations and then finite difference methods
• Advanced Volatility Modeling in Complete Markets
  • The relationship between implied volatility and actual volatility in a deterministic world
  • The difference between ‘random’ and ‘uncertain’
  • How to price contracts when volatility, interest rate and dividend are uncertain
  • Non-linear pricing equations
  • Optimal static hedging with traded options
  • How non-linear equations make a mockery of calibration

预读

• 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)

深入阅读

• 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

终身学习课程
(Available to Full program and Level II delegates)

• 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 & Machine Learning l

In module four, you will be introduced to the latest data science and machine learning techniques used in finance. Starting with a comprehensive overview of the topic, you will learn 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.

• An Introduction to Machine Learning l
  • What is mathematical modeling?
  • Classic tools
  • How is machine learning different?
  • Principal techniques for machine learning
• An Introduction to Machine Learning II
  • Common Machine Learning Jargon
  • Intro to Supervised Learning techniques
  • Intro to Unsupervised Learning techniques
  • Intro Reinforcement Learning techniques
• Math toolbox for Machine Learning I
  • Maximum Likelihood Estimation
  • Cost/Loss Function
  • Gradient Descent
  • Bias and Variance
  • Lagrange Multipliers
• Supervised Learning I – Regression Methods
  • Linear Regression
  • Penalized Regressions: Lasso, Ridge and Elastic Net
  • Logistic, Softmax Regression
  • Applications in Finance
• Supervised Learning II
  • K Nearest Neighbors
  • Naïve Bayes Classifier
  • Support Vector Machines
  • Applications in Finance
• Supervised Learning III
  • Decision trees
  • Ensemble Models – Bagging and Boosting
  • Applications in Finance
• Machine Learning Lab I
  • Supervised Learning implementation
  • Python - Scikit Learn
  • Financial Examples & Exercises

Module 5 - Data Science & Machine Learning ll

In module five, you will learn 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, but more importantly, analyze practical case studies exploring how these techniques are used within finance.

• Math toolbox for Machine Learning II
  • Principal Component Analysis
  • Kernal PCA
  • Factor Analysis, Linear Discriminant Analysis
  • The Mathematics of Deep Learning
• Unsupervised Learning
  • K Means Clustering
  • Self Organizing Maps
  • Applications in Finance
• Deep Learning & Neural Networks
  • What are Artificial Neural Networks and Deep Learning?
  • Perceptron Model, Backpropagation
  • Neural Network Architectures: Feedforward, Recurrent, Long Short Term Memory, Convolutional, Generative adversarial
  • Applications in Finance: Prediction of Stock returns with LSTM
  • Code examples
• Natural Language Processing
  • Pre Processing
  • Word vectorizations, Word2Vec
  • Deep Learning & NLP Tools
  • Application in Finance: sentiment change vs forward returns; S&P 500 trends in sentiment change; Earnings calls analysis.
  • Code examples
• Reinforcement Learning
  • Recap of multi-armed bandit
  • The exploitation-exploration trade-off
  • Exploration strategies: softmax versus epsilon-greedy
  • Risk-sensitivity in Reinforcement-learning
• Practical Machine Learning Case studies for Finance
  • High-frequency market-making as a reinforcement-learning problem
  • Coding reinforcement-learning agents in Python using TensorFlow
• AI Based Algo Trading Strategies
  • Basic Financial Data Analysis with Python and Pandas
  • Creating Features and Label Data from Financial Time Series for Market Prediction
  • Application of Classification Algorithms from Machine Learning to Predict Market Movements
  • Vectorized Backtesting of Algorithmic Trading Strategies based on the Predictions
  • Risk Analysis for the Algorithmic Trading Strategies
• Machine Learning Lab II
  • Sci kit Learn
  • Tensorflow
  • Financial Examples & Exercises

Module 6 - Fixed Income & Credit

In the first part of module six, we will review the multitude of interest rate models used within the industry, focusing on the implementation and limitations of each model. In the second part, you will learn about credit and how credit risk models are used in quant finance, including structural, reduced form as well as copula models.

• Fixed Income Products and Analysis
  • Names and Properties of the basic and most important Fixed-income Products
  • Features commonly found in Fixed-income Products
  • Simple ways to Analyze the Market Value of the Instruments: Yield, Duration and Convexity
  • How to construct yield curves and forward rates
  • Swaps
  • The Relationship between Swaps and Zero-coupon Bonds
• Stochastic Interest Rate Modeling
  • Stochastic models for Interest Rates
  • How to derive the pricing equation for many Fixed-income Products
  • The Structure of many popular one-factor Interest Rate Models
  • The Theoretical Framework for multi-factor Interest Rate Modeling
  • Popular two-factor Models
• Calibration and Data Analysis
  • How to choose time-dependent parameters in one-factor models so that
  • Today’s yield curve is an output of the model
  • The advantages and disadvantages of yield curve fitting
  • How to analyze short-term interest rates to determine the best model for the volatility and the real drift
  • How to analyze the slope of the yield curve to get information about the market price of risk
• Probabilistic Methods for Interest Rates
  • The Pricing of Interest Rate Products on a Probabilistic Setting
  • The Equivalent Martingale Measures
  • The Fundamental Asset Pricing Formula for Bonds
  • Application for Popular Interest Rates Models
  • The Dynamics of Bond Prices
  • The Forward Measure
  • The Fundamental Asset Pricing Formula for Derivatives on Bonds
• Heath Jarrow and Morton Model
  • The Heath, Jarrow & Morton (HJM) Forward Rate Model
  • The Relationship between HJM and Spot Rate Models
  • The Advantages and Disadvantages of the HJM Approach
  • How to Decompose the Random Movements of the Forward Rate Curve into its Principal Components
• The Libor Market Model
  • The Libor Market Model
  • The Market view of the Yield Curve
  • Yield Curve Discretisation
  • Standard Libor Market Model Dynamics
  • Numéraire and Measure
  • The Drift
  • Factor Reduction
• Further Monte Carlo
  • The Connection to Statistics
  • The Basic Monte Carlo Algorithm, Standard Error and Uniform Variates
  • Non-Uniform Variates, Efficiency Ratio and Yield
  • Co-Dependence in Multiple Dimensions
  • Wiener Path Construction; Poisson Path Construction
  • Numerical Integration for Solving Sdes
  • Variance Reduction Techniques
  • Sensitivity Calculations
  • Weighted Monte Carlo
• Volatility Smiles and the SABR Model
  • Vanilla options: European Swaptions, Caps, and Floors
  • Arbitrage Free SABR
• Co-Integration for Trading
  • Multivariate Time Series Analysis
  • Stationary and Unit Root
  • Vector Autoregression Model (VAR)
  • Co-Integrating Relationships and their Rank
  • Vector Error Correction Model (VECM)
  • Reduced Rand Model (Regression) Estimation: Johansen Procedure
  • Stochastic Modeling of Autoregression: Orstein-Uhlenbeck Process
  • Statistical Arbitrage using Mean Reversion
• Credit Derivatives & Structural Models
  • Introduction to Credit Risk
  • Modelling Credit Risk
  • Basic Structural Models: Merton Model, Black and Cox Model
  • Advanced Structural Models
• Credit Default Swaps
• Intensity Models
  • Modelling Default by Poisson Process
  • Relationship between Intensity and Arrival Time of Default
  • Risky Bond Pricing: Constant vs. Stochastic Hazard Rate
  • Bond Pricing with Recovery
  • Theory of Affine Models
  • Affine Intensity Models and use of Feynman-Kac
  • Two-factor Affine Intensity Model example: Vasicek
• CDO & Correlation Sensitivity
  • CDO Market Pricing and Risk Management
  • Loss Function and CDO Pricing Equation
  • Motivation from Loss Distribution
  • What Is Copula Function
  • Classification of Copula Functions
  • Simulating Via Gaussian Copula
  • 3 Gaussian Copula Factor Mode
  • The Meaning of Correlation. Intuition and Timescale
  • Linear Correlation and Its Misuse
  • Rank Correlation
  • Correlation in Exotic Options
  • Uncertain Correlation Model for Mezzanine Tranche
  • Compound (Implied) Correlation in Loss Distribution
• X-Valuation Adjustment
  • Historical Development of OTC Derivatives and Xva
  • Credit and Debt Value Adjustments (CVA and DVA)
  • Funding Value Adjustment (FVA)
  • Margin and Capital Value Adjustments (MVA and KVA)
  • Current Market Practice and Application
  • Implementation of Counterparty Credit Valuation Adjustment (CVA)
  • Review the Numerical Methodologies Currently Used to Quantify CVA in terms of Exposure and Monte Carlo Simulation and the Libor Market Model
  • Illustrate this Methodology as well as DVA, FVA and others

Advanced Electives

Your advanced electives are the final element in our core program. These give you the opportunity to explore an area that’s most relevant or interesting to you. Select two electives from the extensive choice below to complete the CQF qualification. You will also have access to every advanced elective as part of the Lifelong Learning Library.

Algorithmic Trading

The use of algorithms has become an important element of modern-day financial markets, used by both the buy side and the sell side. This elective will look into the techniques used by professionals who work within this area.

• What is Algorithmic Trading
• Preparing Data; Back Testing, Analyzing Results and Optimization
• Build Your Own Algorithm
• Alternative Approaches: Pairs Trading; Options; New Analytics
• A Career in Algorithmic Trading

Who is it for: Trading, Asset Management, Hedge Fund professionals

Advanced Computational Methods

One key skill for anybody who works within quantitative finance is how to use technology to solve complex mathematical problems.This elective will look into advanced numerical techniques for solving and implementing the math in an efficient and succinct manner, ensuring that the right techniques are used for the right problems.

• Finite Difference Methods (algebraic approach) and Application to BVP
• Root Finding
• Interpolation
• Numerical Integration

Who is it for: IT, quant analytics, derivatives, valuation, actuarial, model validation professionals

Advanced Risk Management

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 (Fundamental Review of the Trading Book, New Minimum, Capital of Market Risk).

One of the consequences of these changes is the dramatic increase in the need for efficient and accurate computation of sensitivities. To cover this topic we will explore adjoint automatic differentiation (AAD) techniques from computational finance. We will see how, when compared to finite difference approximations, this approach can potentially reduce the computational cost by several orders of magnitude, with sensitivities accurate up to machine precision.

• Review of New Developments on Market Risk management and Measurement
• Explore the Use of Extreme Value Theory (EVT)
• Explore Adjoint Automatic Differentiation (AAD)

Who is it for: Risk management, trading, fund management professionals

Advanced Volatility Modeling

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.

• Fourier Transforms
• Functions of a Complex Variable
• Stochastic Volatility
• Jump Diffusion

Who is it for: Derivatives, structuring, trading, valuations, actuarial, model validation professionals

Advanced Portfolio Management

As quantitative finance becomes more important in today’s financial markets, many buy-side firms are using quantitative techniques to improve their returns and better manage their client capital. This elective will look into the latest techniques used by the buy side in order to achieve these goals.

• Perform a Dynamic Portfolio Optimization, Using Stochastic Control
• Combine Views with Market Data Using Filtering to Determine the Necessary Parameters
• Understand the Importance of Behavioral Biases and Be Able to Address Them
• Understand the Implementation Issues
• Develop New Insights Into Portfolio Risk Management

Who is it for: Trading, fund management, asset management professionals

Counterparty Credit Risk Modeling

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.

• 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

Who is it for: Risk management, structuring, valuations, actuarial, model validation professionals

Behavioural Finance for Quants

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.

• System 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

Who is it for: Trading, Fund Management, Asset Management professionals

Data Analytics with Python

Data and data analysis has become a key tool in any quants toolbox. In this elective you will learn how to use Python and Python libraries to analyse financial data and organise it in ways that allow you to use the data in a meaningful and productive way to make decisions.

• Python Idioms and Data Structures
• Using NumPy for Numerical Analysis
• Using Pandas for Financial Time Series Analysis
• Financial Data Visualization for Static and Streaming Data

Who is it for: IT, Quant Analytics, Valuation, Actuarial, Model Validation professionals

Python Applications

Python has become an important modeling tool and programing language within the industry.
This elective will extend the material discussed in the primer which introduced the Python environment using enthought canopy, as well as much of the basic syntax and structures.

• Numerical Analysis - Fundamental and Important Techniques Applied to Finance
• File Manipulation and Working with Data
• Functions - Further Development of User Defined Functions as well as the Powerful Libraries for Probability and Statistics

Who is it for: IT, quant analytics, derivatives, valuation, trading, asset management professionals

Machine Learning Using Python

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, covering Python packages like NumPy, pandas, scikit-learn and TensorFlow.

This will provide the basis to further explore these recent developments in data science to improve traditional financial tasks such as the pricing of American options or the prediction of future stock market movements.

• Using linear OLS Regression to Predict Financial Prices & Returns
• Using scikit-learn for Machine Learning with Python
• Application to the Pricing of 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

Who is it for: IT, quant analytics, trading professionals

R for Quant Finance

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.

• Install R and R Studio
• Navigate R Studio to Unleash the Power of R and Stay Organised
• Use Packages
• Understand Data Structures and Data Types
• Use Some of R’s Most Useful Functions
• Plot Charts
• Read and Write Data Files
• Write your Own Scripts and Code
• Know how to Deal with some of R’s “Loveable Quirks”

Who is it for: IT, Quant Analytics, Valuation, Actuarial, Model Validation,Trading and Asset Management Professionals

Risk Budgeting: Risk-Based Approaches to Asset Allocation

Risk budgeting is the name of the last-generation approach to portfolio management.
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.

• Portfolio Construction and Measurement
• Value at Risk in Portfolio Management
• Risk Budgeting in Theory
• Risk Budgeting in Practice

Who is it for: Risk Management, Trading, Fund Management Professionals

Fintech

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.

• 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

Who is it for: IT, quant analytics, trading, derivatives, valuation, Actuarial, Model Validation professionals

C++

Intended for those who are completely new to C++ or have very little exposure to the language.
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.

• 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

Who is it for: IT, Quant analytics, Valuation, Derivatives, Model Valuation

Lifelong Learning

Stay at the top of your game throughout your career

The Lifelong Learning library, available at no extra cost to alumni, let’s you keep pace with the latest thinking in quant finance and machine learning, even after you earn the CQF. This rich, online resource is ever-expanding with new videos on hot industry issues, all delivered by eminent practitioners and globally recognized experts.

Key facts

• Over 900 hours of extra lectures delivered by expert speakers
• 10 subject areas on all conceivable finance topics
• 2 formats: lectures and masterclasses
• Latest CQF curriculum
• 24-hour access to Lifelong Learning on the CQF portal

Lectures on every quant finance topic

• Over 900 hours of extra lectures on every conceivable finance subject
• Ever-expanding and up-to-date content
• Delivered by eminent practitioners and academics, including Dr. Paul Wilmott, Dr. Peter Jäckel, Dr. Espen Gaarder Haug, Dr. Alonso Peña and Dr. Sébastien Lleo

Masterclasses on crucial subjects

• Deep dive into specific subjects
• Over 100 hours of rich material
• Delivered by experts such as Dr. Paul Wilmott, Dr. Claudio Albanese, Dr. Wim Schoutens

Intense coverage of mathematical methods

• Intensive 51-lecture program
• Covers mathematical methods applicable to real-world problems
• Equivalent to more than the first two years of a mathematics degree course

Programming essentials for quant finance

• Hundreds of hours of lecture on a variety of technical and programming techniques - all distilled down to what today’s market practitioners need to know
• Critical to a role as a modern quant in a top-tier investment bank
• Covers the theory of design and translating pricing models into working Python, C++ and other code

PS:
Modules内容提取代码

import requests
from lxml import etree

def parse(name, url) :
    resp = requests.get(url)
    data = resp.text
    result = etree.HTML(data).xpath("//div[@class='accordion-section']//div[@class='field__item']")
    print('\n\n')
    print(name)
    print('\n\n')
    for index in range(0, len(result), 3) :
        print('-', result[index].text)
        for r in result[index + 2].xpath("./ul/li") :
            print('  -', r.text)

urls = {
    'module-1' : 'https://www.cqf.com/about-cqf/program-structure/cqf-qualification/module-1-building-blocks-finance',
    'module-2' : 'https://www.cqf.com/about-cqf/program-structure/cqf-qualification/module-2-quantitative-risk-return',
    'module-3' : 'https://www.cqf.com/about-cqf/program-structure/cqf-qualification/module-3-equities-currencies',
    'module-4' : 'https://www.cqf.com/about-cqf/program-structure/cqf-qualification/module-4-data-science-machine-learning-l',
    'module-5' : 'https://www.cqf.com/about-cqf/program-structure/cqf-qualification/module-5-data-science-machine-learning-ll',
    'module-6' : 'https://www.cqf.com/about-cqf/program-structure/cqf-qualification/module-6-fixed-income-and-credit',
}

for item in urls.items() : parse(*item)