Advanced Electives

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.

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.

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.

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.

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.

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.
 

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.
 

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.
 

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.

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.

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.

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.

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. 

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.
 

The Generative AI and Large Language Models (LLMs) elective will guide participants through the essentials of LLMs from the basics to gaining an understanding of their architecture. This elective focuses on practical aspects of building Generative AI applications to finance.

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

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.

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.

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.