In an era when the global financial markets have become increasingly complex, interconnected, and influenced by quantitative models, data science, and machine learning, investors and other market participants need to develop the right analytical tools and techniques to keep pace with the change. The Certificate in Quantitative Finance (CQF) is a professional qualification that focuses on conveying the essential concepts and technical skills needed to model across a range of asset classes and products. New modules provide a deep dive into machine learning, and a final project helps cement the knowledge gained throughout the program.
The course is designed with the working professional in mind, offering flexibility to complete the 6-month program in one cohort or allow up to 3 years, with lectures delivered live online and available to watch on demand.
Who should apply to the CQF program?
The CQF is suitable for people from a variety of backgrounds. Many delegates have undergraduate or postgraduate degrees in finance or economics. Others come from physics, engineering, mathematics, computer science, or even astronomy programs. Some have been working in the financial industry for a while, particularly in areas such as risk management, IT, portfolio management, quant analytics and trading. Others are looking to use the CQF to enter the industry. However, all have the same desire to upskill themselves in the latest quant finance and machine learning techniques used by practitioners in order to solve real world problems in finance.
How can you prepare to study the CQF program?
Prior to the start of each cohort, in-depth primers in Math, Finance, and Programming allows all delegates the opportunity to refresh their skills and get up to speed, ensuring that they will be able to keep pace with the course modules and develop their skills and understanding further as they go along. A recent poll conducted by the CQF Institute shows, these domains are considered essential for a career in quant finance.
What does the CQF syllabus cover?
The CQF program syllabus consists of the following six modules:
- Module 1 - Building Blocks of Quantitative Finance
In module one, you will be introduced 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.
- 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. You 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.
- Module 3 - Equities & Currencies
In module three, you will explore the importance of the Black-Scholes theory as a theoretical and practical pricing model which is built on the principles of delta hedging 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.
- 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.
- 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, you 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.
- Module 6 - Fixed Income & Credit
In the first part of module six, you 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.
- Advanced Electives
Your advanced electives are the final element in the program. These give you the opportunity to explore an area that’s most relevant or interesting to you. You will select two electives from an extensive choice to complete the CQF qualification.
Course requirements include the successful completion of weekly assignments, a series of take-home exams, and a final project focused on the advanced electives chosen in module six. The CQF is a master’s level program, and distinguishes itself from a Masters in Financial Engineering (MFE), through the dedicated focus on both theory and practice, with a hands-on approach to learning fostering an environment of thoughtful critique on the models and methods in use today. Delegates are better equipped for jobs in quant finance than those who might have only learned the theory. The CQF teaches financial theory in a clear and rigorous manner, and the program lecturers are committed to balancing the abstract notions with insights on how models are actually used in industry, what assumptions underpin them, and what strengths and weaknesses can be observed in various situations.
The CQF was founded by Dr. Paul Wilmott to fill the gap between academia and the use of quant finance in industry. The program is taught by world-renowned practitioners such as Dr Espen Haug, Dr Peter Jaeckel, Dr Claus Huber, Dr Marc Henrard, and more. The faculty, senior alumni practitioners, and leading industry figures are also consulted a quarterly basis to keep the CQF syllabus cutting-edge and ensure that new models, methods, and popular topics are added to the curriculum.
Throughout each module, the students work directly with quant models in labs to gain experience with direct implementation. The assessments for the program require delegates to accomplish three objectives: derive the theory on which the model is based, build, and implement the model from the ground up, and analyse the output of the model, offering a cogent critique of the results. This learning process supports the development of “desk-ready” skills, a feature that has distinctive value to the delegates and is valued by employers. These skills are essential and staying current is a powerful advantage in the job market.
How can the CQF help delegates stay up to date with new techniques throughout their careers?
As seen from the recent CQF Institute survey, students also place emphasis on the importance of lifelong learning, networking at industry events, and on the job training, to varying degrees. After delegates complete the CQF program, they will have access to the Lifelong Learning library which includes 900+ hours of additional lectures, all the advanced electives, masterclasses, and the latest syllabus of the CQF itself as it evolves from year to year.
CQF alumnus, Mudit Gupta, began his career as a market risk analyst and Credit Suisse and is now in model validation at a large consulting firm (PwC IAC). He commented, “As I settle into this new role, I’m also planning to sit in on the CQF alumni sessions to guide those who are looking forward to taking the course. I’m also planning to polish my skills in AI so that I can solve real-life problems and challenges at work using machine learning and deep learning.”
These sentiments are echoed by many candidates around the world.
What happens after the CQF?
In returning to their existing jobs, or in seeking new jobs, CQF alumni pursue professional paths in quantitative analysis, quant research, and quant development; model validation and model development; risk management, strategy, and quant trading, among other roles.
Leticia Mortoza started in model validation and has expanded towards model development. Some CQF delegates will focus on programming, risk management, and compliance including model validation and testing.
Jean-Paul Jaegers worked as a portfolio manager and deputy head of research focused on Multi-Asset Solution and has moved into a leadership role in wealth management since completing the CQF. Delegates may take on front, middle, or back office roles in asset management, fund management, or hedge funds.
Medhi Bourai began his career as a portfolio manager and analyst at a boutique in Paris and is now working with alternative data in the context of ESG investment. CQF delegates may work in a wide range of data-intensive roles, drawing on skills developed throughout the program for modeling, analytics, and portfolio management.
As CQF alumni examine the employment opportunities, these roles are expected to see an increase in demand in the years to come.