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, analysing results and optimisation
- 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
- 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 optimisation, 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
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 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 with 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