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. You need to select two electives from the extensive choice below to complete the CQF qualification. Struggling to choose just two electives? Don’t worry, you will have access to every advance elective as part of the CQF Lifelong Learning Library

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Advanced Ensemble Modeling

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The advanced ensemble learning elective will focus on the practical consideration of ensemble modeling techniques. The elective teaches essential skills required to build, evaluate and track various machine ensemble learning models.

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  • Understanding Machine Learning Lifecycle
  • Understanding Learning and Data Representation
  • Working of Learning Algorithms 
  • Understanding Ensemble Learning
  • Optimizing Models
  • Building Ensemble Models 
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Advanced Portfolio Management

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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.

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  • 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

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Advanced Machine Learning I

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The Machine Learning (ML) elective will focus on the practical consideration of deep sequential modeling. From gaining an understanding of the Machine Learning framework to feature engineering and selection, the elective teaches essential skills required to build and tune Neural Networks.

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  • Definition, Trends, and Landscape
  • Seven Steps to model an ML problem
  • Understanding Learning and Data Representation
  • Working of Learning Algorithms 
  • Exploratory Data Analysis
  • Feature Engineering on Date - Time Data
  • Feature Engineering on Numeric Data
  • Addressing Class Imbalances
  • Overview of Feature Selection Methods
  • Feature Selection using Boruta Algorithm
  • Understanding Sequences 
  • Sequence-data Generation
  • Getting started with TensorFlow and Keras API
  • Building & Training a Multivariate LSTM Model
  • Hyperparameter Optimization and Tuning
  • Evaluation of ML model 
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Advanced Machine Learning II

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This elective is an extension of Advanced Machine Learning that focuses on the practical consideration of machine learning. The elective teaches essential skills required to build, evaluate and track various machine learning models.

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  • Understanding Machine Learning Lifecycle
  • Optimizing Models with Experiment Trackers
  • Building Data / ML Apps in Python
  • Understanding Ensemble Learning
  • Building Ensemble Models for Trend Prediction
  • Customizing TensorBoard for ML Experiments 

Who is it for: IT, Data Science, Risk Management, Trading, Fund Management, and Machine Learning Professionals

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Advanced Risk Management

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This elective will explore some of the recent developments in Quantitative Risk Management. It will take as a point of departure the paradigms on how market risk is conceived and measured, both in the banking industry (VaR, ES) and under the Basel Frameworks (sensitivities-based approach). It will explore how to use Extreme Value Theory (EVT) and Radial Basis Functions (RBF) for this purpose. 

This elective will then explore credit risk correlation and the modern approaches used to estimate the asset correlation for a portfolio. Using the Multifactor Vasicek model and data from defaults/downgrades in the markets, it will explore how to estimate intra and inter sector correlations. Furthermore, it will assess if the resulting estimated correlation matrices are valid, i.e. positive semi-definite, by using techniques form matrix algebra, such as eigenvalue analysis and the Gershgorin Theorem. Using these it will then construct stressed correlation matrices that can be used for risk management purposes. 

Next, this elective will continue to explore the new approaches to conceive and quantify climate risk is the financial industry. It will review the results of the recent (2022) climate risk stress test exercise conducted by the European Central Bank (ECB) and discuss the wider perspectives highlighted by the United Nations Intergovernmental Panel on Climate Change (IPCC). 

Finally, it will conclude with the lessons learned from the recent pandemic and its consequences on financial risk management. The stressed environment of the Covid-19 pandemic increased not only market and credit risks but also the operational risks of financial institutions.

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  • Review of New Developments on Market Risk Management and Measurement
  • Explore the Use of Extreme Value Theory (EVT)
  • Explore Adjoint Automatic Differentiation (AAD)
  • Discuss credit risk correlation and the modern approaches used to estimate the asset correlation for a portfolio
  • Explore the new approaches to conceive and quantify climate risk is the financial industry
     

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

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Advanced Volatility Modeling

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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, jump diffusion, the concept of fractional Brownian motion and rough volatility.
 

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  • Fourier Transforms
  • Functions of a Complex Variable – a detailed approach
  • Stochastic Volatility and Jump Diffusion
  • Fractional Brownian Motion and rough paths


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

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Algorithmic Trading I

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The Algorithmic Trading elective is a DIY guide that enables you to start your quantitative trading from scratch. From gaining an understanding of data science workflow to retrieving data using API, the elective teaches essential skills required for different quant applications.
 

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  • Introduction to Algorithmic Trading 
  • Building Blocks of Quantitative System
  • Handling Open Source Data APIs 
  • Getting Started with OpenBB SDK
  • Introduction to TradingView Lightweight Charts


Who is it for: Traders and quants who want to learn and use Python in trading.

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Algorithmic Trading II

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The Algorithmic Trading elective is a DIY guide that enables you to start your quantitative trading from scratch. This elective is an extension of Algorithmic Trading I and covers some of the best software practices in developing quant applications including data ingestion, backtesting, and live programmatic execution of trades using APIs.
 

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  • Getting Started with Docker and Databases
  • Handling Market Data API
  • Backtesting Strategies in Python
  • Getting Started with Alpaca Python SDK
  • Strategy Execution Using AlpacaTrading API


Who is it for: Traders and quants who want to learn and use Python in trading.

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Behavioral Finance for Quants

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Behavioral 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.

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  • 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

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C++

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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.

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  • 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

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Counterparty Credit Risk Modeling

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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.

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  • 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

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Decentralized Finance Technologies

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Blockchain technology is one of the biggest innovations of the 21st Century. While this technology dates back to the early 1990s, it gained popularity after the launch of Bitcoin in 2009. As the number of applications that were built on it grew rapidly, such technologies have the power to shape the future from finance to manufacturing.

This elective gives an insight into the financial technology revolution as we demystify the concepts surrounding these new-age technologies.

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  • Blockchain Basics
  • Prototyping Bitcoin Mining in Python
  • Demystifying Decentralized Finance [DeFi]
  • Ethereum Basics & Smart Contracts 
  • Programming with Solidity
  • Developing Smart Contracts on Ethereum Network


Who is it for: IT, quant analytics, trading, derivatives, valuation, Actuarial, Model Validation professionals, and anyone who wants to learn these new-age technologies.

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Energy Trading

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The elective provides a comprehensive overview of commonly traded quantitative strategies in energy markets. The elective bridges quantitative finance and energy economics covering theories of storage, net hedging pressure, volatility modeling, and the pricing framework for energy derivatives.

Throughout the elective, the emphasis is placed on understanding the behavior of various market participants and trading strategies designed to monetize inefficiencies resulting from their activities and hedging needs. It will then discuss recent structural changes related to financialization of energy commodities, and linkages to other financial asset classes.

The objective of the elective is to provide students with practical knowledge of energy trading strategies, including systematic risk premia, volatility arbitrage, and strategies based on fundamental, flow, and macroeconomic data. These strategies are based on the instructor’s personal experience in managing the energy trading business for over 20 years. The focus will be primarily on the most liquid oil market with some extensions to other energy commodities. 

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  • Develop market-based approaches to traditional theories of storage and hedging pressure
  • Construct systematic risk premia strategies supported by these theories in the oil market
  • Describe practical implementation of volatility risk premia strategies and gamma trading strategies
  • Present a novel quadratic normal model for pricing oil options and vega arbitrage    
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FX Trading and Hedging

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This elective on FX trading and hedging will equip you with the knowledge and skills to understand FX trading models, backtesting techniques, hedging strategies, and option trading methods, enabling you to make informed decisions in the dynamic world of foreign exchange.
 

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  • Understand the history of FX trading models
  • Be able to use backtesting techniques to evaluate historical model performance, including statistical tests to indicate the likelihood of over-optimization
  • Learn how to investigate popular FX trading models using these techniques and understand how different models have performed in different environments
  • Understand how certain trading models have been used as active hedging of other asset classes
  • Learn about hedging the FX risk of different asset classes using active or passive techniques
  • Understand how to perform basic delta hedging
  • Appreciate how the correlation behavior of FX rates with different asset classes leads to different optimal hedging methods
  • Be able to compare and contrast hedging methods with options and forward rates
  • Understand simple option trading strategies and how to backtest them  and appreciate the importance of good data sets
  • Learn how to construct and test more sophisticated option trading models and appreciate the risks inherent in option selling methods
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Quantitative Methods for ESG

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This elective provides an overview of quantitative approaches to Environmental, Social, and Governance (ESG) finance, emphasizing the use of diverse data sources, including climate, social, and unstructured data, to assess climate risks. The elective will critically assess ESG Scores from major rating agencies and explore their use in translating complex information into actionable financial insights. The elective also examines complexity theory and Agent-Based Models to understand the financial impact of drastic events like sea-level rise and pandemics. Practical sessions in Python will be conducted to apply the discussed quantitative methods using publicly accessible databases.
 

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  • Introduce ESG and its role in finance
  • Review some of the quantitative techniques applicable to ESG
  • Discuss Climate Change, Climate Risk, and ESG Scores
  • Review the Agent-Based Model approach to ESG 
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Quantum Computing in Finance

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Quantum Computing is about the application of the principles of quantum mechanics to computer science. In this advanced elective we will:

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  • Define quantum computing and its relevance in finance
  • Review the three key ingredients of quantum computing: qubits, quantum gates and quantum circuits
  • Enumerate some of the applications of quantum computing in various fields
  • Construct ourselves a simple quantum circuit online using the IBM Quantum 
  • Learn how to write our own quantum program using the Python module Qiskit
  • Explore examples of quantum algorithms in finance, including pricing European options, interest rate products and credit risk


Who is it for: Quantitative analysts, risk management professionals, financial analysts

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Numerical Methods

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Any study in mathematics is incomplete without treatment of numerical analysis. When a closed form solution is not available or the problem to be solved is too complex to make amenable to explicit methods, a numerical/computational solution is sought. The resulting solution is an example of an approximate solution. 

This one-day elective will present several basic numerical methods for solving some of the most classic problems in mathematics.

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  • Basic iteration and convergence
  • Bisection method
  • Newton-Raphson
  • Rates of convergence
  • Taylor series and the error term
  • Numerical differentiation
  • Trapezoidal method
  • Simpson’s rule
  • Error analysis
  • Euler
  • Runge-Kutta
  • Lagrange interpolation
  • Cubic splines
  • LU decomposition
  • SOR methods
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R for Data Science & Machine Learning

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R is a powerful programming language and software environment for statistical computing. It is one of the favorite tools among academicians and is widely used among statisticians and data miners for their data analysis. In this workshop, we'll revisit R programming from scratch to solve quant finance and machine learning problems that help in understanding mathematical and computational tools from a quant’s perspective.

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  • Introduction & Installation
  • Getting Started with R & RStudio
  • Working with Data
  • Writing your own Custom Functions
  • Visualization & Charting
  • Statistics and Probability
  • Machine Learning Applications in R


Who is it for: IT, Data Science, Risk Management, Trading, Fund Management, and Machine Learning Professionals 

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Risk Budgeting: Risk-Based Approaches to Asset Allocation

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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.

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  • 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

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Fixed Income & Credit