About the IATACF

The advanced electives represent the culminating component of our core program. They offer you the chance to delve into a subject matter that resonates most with your The elective on advanced ensemble learning will center on the hands-on exploration of ensemble modeling techniques. This course imparts crucial proficiencies needed to construct, assess, and monitor diverse ensemble machine learning models.

• Grasping the Machine Learning Lifecycle
• Comprehending Learning and Data Representation
• Unraveling the Mechanics of Learning Algorithms
• Exploring the Depths of Ensemble Learning
• Fine-Tuning Model Performance
• Crafting Ensemble Models

In today’s financial landscape, the prominence of quantitative finance has grown significantly, with many buy-side institutions increasingly turning to quantitative methods to enhance their investment returns and efficiently oversee client assets. This elective course delves into the most cutting-edge approaches employed by buy-side professionals to realize these objectives.

• Dynamic Portfolio Optimization through Stochastic Control
• Integration of Market Data and Investor Views through Filtering Techniques for Determining Essential Parameters
• Acknowledging and Mitigating Behavioral Biases
• Addressing Implementation Challenges
• Gaining Fresh Insights into Portfolio Risk Management

Target Audience: Professionals involved in trading, fund management, and asset management.

Advanced Machine Learning I is a course that delves into the practical aspects of deep sequential modeling within the realm of Machine Learning (ML). This elective covers a wide range of topics, from the fundamentals of the ML framework and feature engineering to the art of building and fine-tuning Neural Networks.

• Introduction to Machine Learning: Definition, Trends, and Landscape
• Seven Steps to Solve an ML Problem
• Understanding Learning and Data Representation
• Working Principles of Learning Algorithms
• Exploratory Data Analysis
• Feature Engineering for Date and Time Data
• Feature Engineering for Numeric Data
• Dealing with Class Imbalances
• Overview of Feature Selection Methods
• Feature Selection using the Boruta Algorithm
• Understanding Sequences in Machine Learning
• Generating Sequence Data
• Getting Started with TensorFlow and Keras API
• Building and Training a Multivariate LSTM Model
• Hyperparameter Optimization and Tuning
• Evaluating Machine Learning Models

This elective course is designed as an advanced continuation of the Advanced Machine Learning program, with a primary focus on the practical aspects of machine learning. In this elective, you will acquire vital skills necessary for constructing, assessing, and monitoring a range of machine learning models.

• Comprehending the Machine Learning Lifecycle
• Enhancing Models with Experiment Tracking
• Developing Data and Machine Learning Applications in Python
• Mastering Ensemble Learning
• Crafting Ensemble Models for Trend Forecasting
• Tailoring TensorBoard for Machine Learning Experiments

In this elective course, we will delve into cutting-edge developments in Quantitative Risk Management. Our journey begins with a fresh perspective on how market risk is both conceptualized and assessed, within the banking industry (utilizing metrics like VaR and ES) and in alignment with the Basel Frameworks (focusing on a sensitivities-based approach). We’ll also investigate the application of Extreme Value Theory (EVT) and Radial Basis Functions (RBF) for this purpose.

Moving forward, we’ll shift our focus to credit risk correlation and the contemporary techniques employed for estimating asset correlation within a portfolio. Using the Multifactor Vasicek model and real-world data pertaining to defaults and downgrades in the markets, we’ll scrutinize the estimation of intra and inter-sector correlations. Furthermore, we’ll evaluate the validity of resulting correlation matrices, ensuring they adhere to the positive semi-definite criterion through techniques such as eigenvalue analysis and the Gershgorin Theorem. Armed with this knowledge, we will construct stressed correlation matrices for effective risk management.

Subsequently, our exploration will extend to novel methodologies for conceptualizing and quantifying climate risk in the financial industry. We will critically review the outcomes of the recent (2022) climate risk stress test conducted by the European Central Bank (ECB) and delve into the broader perspectives emphasized by the United Nations Intergovernmental Panel on Climate Change (IPCC).

Finally, we will draw upon the lessons derived from the recent pandemic and its repercussions on financial risk management. The tumultuous landscape brought about by the Covid-19 pandemic amplified not only market and credit risks but also the operational risks faced by financial institutions.

The mastery of volatility and the skill to effectively model it are indispensable components of any quantitative modeling endeavor. This elective course delves into the prevalent methodologies employed in the financial industry to model volatility. It equips participants with the requisite mathematical knowledge and numerical techniques for addressing challenges in stochastic volatility, jump diffusion, fractional Brownian motion, and the intricate concept of rough volatility.

• Fourier Transforms
• Functions of a Complex Variable
• Stochastic Volatility
• Jump Diffusion
• Fractional Brownian Motion
• Rough Volatility

This course is ideal for professionals engaged in derivatives, structuring, trading, valuations, actuarial work, and model validation.

Are you interested in diving into the world of algorithmic trading and quantitative finance? The Algorithmic Trading I elective offers you a comprehensive journey, starting from the ground up. This course equips you with the essential skills to embark on your quantitative trading adventure, whether you’re completely new to the field or seeking to enhance your knowledge.

1. An Overview of Algorithmic Trading
2. Initiating Your Journey with the OpenBB SDK
3. Managing Open Source Data APIs
4. Exploring TradingView Lightweight Charts

Target audience: Traders and quantitative analysts seeking to acquire and apply Python skills in the realm of trading.

The algorithmic trading elective offers a do-it-yourself (DIY) roadmap for those looking to embark on their quantitative trading journey from the ground up. Serving as a natural progression from Algorithmic Trading I, this elective delves into the realm of best software practices for developing quantitative applications. It equips you with the knowledge and skills necessary for tasks such as automatic data ingestion, comprehensive backtesting, and the real-time programmatic execution of trades through a variety of APIs.

• Introduction to LEAN Algorithmic Trading Engine
• Getting Started with Alpaca Python SDK
• Mastering MarketData API Handling
• Executing Strategies Using Trading APIs

Target Audience: This elective caters to traders and quantitative analysts (quants) seeking to harness the power of Python in their trading endeavors.

In the realm of quantitative finance, a profound understanding of behavioral finance and its interplay with human psychology is pivotal. These factors exert a profound influence on our quantitative models and financial decision-making processes. This elective course is meticulously designed to furnish participants with a robust toolkit, enabling them to recognize and navigate the critical psychological pitfalls that impact their financial modeling endeavors.

• Distinguishing System 1 from System 2 Thinking
• Unraveling Behavioral Biases
• Exploring Heuristic Processes
• Analyzing Framing Effects
• Understanding Group Processes
• Navigating the Fine Line Between Loss Aversion and Risk Aversion
• Delving into SP/A Theory
• Discerning the Dynamics of Linearity and Nonlinearity
• Strategic Insights from Game Theory

Who Should Attend:

Professionals engaged in Trading, Fund Management, and Asset Management, who seek to refine their quantitative modeling skills while gaining a deep understanding of the behavioral dimensions that shape financial decisions.

This elective course is designed for individuals who are brand new to C++ or have limited prior exposure to the language. The curriculum starts with fundamental concepts such as basic keyboard input and screen output and progresses through various topics, concluding with an introduction to simple Object-Oriented Programming (OOP).

Here’s an overview of the course content:

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

Introduction to OOP

• Simple Classes and Objects

This course is suitable for individuals in IT, quantitative analytics, valuation, derivatives, and model valuation fields.

In the aftermath of the global financial crisis, the importance of accounting for counterparty credit risk and its interconnected risks has significantly increased. This elective course will comprehensively explore the various risks associated with counterparties and their integration into pricing and modeling frameworks.

Topics Covered:

• Transition from Credit Risk to Credit Derivatives
• Understanding Counterparty Credit Risk: CVA, DVA, FVA
• Dynamic Models and Modeling for Interest Rate Risks in Counterparty Credit
• Interest Rate Swap CVA and the Implementation of Dynamic Models

Who Should Attend:

This course is designed for professionals in risk management, structuring, valuations, actuarial, and model validation.

Blockchain technology stands as one of the most transformative innovations of the 21st Century. Though its roots can be traced back to the early 1990s, it truly gained prominence following the introduction of Bitcoin in 2009. With an ever-expanding array of applications built upon this foundation, these technologies possess the potential to shape the future across various domains, spanning from finance to manufacturing.

This elective course offers an in-depth exploration of the financial technology revolution, demystifying the intricate concepts underpinning these cutting-edge technologies.

Course Topics:

• Blockchain Fundamentals
• Bitcoin Mining Prototyping in Python
• Unraveling the Mysteries of Decentralized Finance (DeFi)
• Ethereum Essentials & Smart Contracts
• Solidity Programming
• Crafting Smart Contracts on the Ethereum Network

Who Should Attend: This course is tailored for IT professionals, quantitative analysts, traders, derivatives experts, valuation specialists, actuarial professionals, model validation experts, and anyone eager to embrace and master these innovative technologies.

This course offers a comprehensive exploration of quantitative strategies commonly employed in energy markets. It serves as a bridge between quantitative finance and energy economics, delving into concepts such as storage theories, net hedging pressure, volatility modeling, and the pricing framework for energy derivatives.

Throughout the course, the primary focus lies in comprehending the behavior of diverse market participants and the development of trading strategies aimed at capitalizing on inefficiencies arising from their actions and risk management needs. The curriculum also addresses recent structural shifts associated with the financialization of energy commodities and their connections to other financial asset classes.

The core objective of this course is to equip students with practical knowledge of energy trading strategies. These strategies encompass systematic risk premia, volatility arbitrage, and approaches based on fundamental, flow, and macroeconomic data. Drawing from the instructor’s extensive 20-year experience in managing the energy trading business, the course places particular emphasis on the highly liquid oil market, with some exploration of other energy commodities.

In this elective course dedicated to FX trading and hedging, you will gain a comprehensive understanding of the tools and knowledge required to navigate the dynamic realm of foreign exchange. Upon completion of this course, you will be equipped with the skills and expertise to make well-informed decisions. Here are the key areas you’ll explore:

• Gain insights into the historical development of FX trading models
• Master the use of backtesting techniques to assess the historical performance of models, including employing statistical tests to detect potential over-optimization
• Explore the application of these techniques to examine popular FX trading models and their performance across varying market conditions
• Comprehend how specific trading models serve as active hedges for other asset classes
• Acquire knowledge about FX risk hedging for diverse asset classes, utilizing both active and passive approaches
• Learn the fundamentals of basic delta hedging strategies
• Recognize the influence of FX rate correlations with different asset classes on the selection of optimal hedging methods
• Compare and contrast various hedging methods involving options and forward rates
• Understand basic option trading strategies, how to conduct backtesting, and the significance of high-quality datasets
• Develop and test more advanced option trading models while appreciating the inherent risks associated with option selling strategies

Explore the intersection of quantum mechanics and computer science with a focus on its implications for the financial sector in this advanced elective. During this course, participants will:

• Gain a comprehensive understanding of quantum computing and its significance in the finance industry.
• Examine the fundamental components of quantum computing: qubits, quantum gates, and quantum circuits.
• Survey a range of quantum computing applications across different domains.
• Create a basic quantum circuit online using IBM Quantum.
• Develop quantum programs using the Python module Qiskit.
• Investigate practical quantum algorithms in finance, including the pricing of European options, interest rate products, and credit risk assessment.

Intended Audience: This course is designed for quantitative analysts, risk management professionals, and financial analysts seeking to leverage quantum computing for their work.

No exploration of mathematics is truly comprehensive without delving into the realm of numerical analysis. When confronted with problems that lack closed-form solutions or those that are too intricate for explicit methods, the pursuit of a numerical or computational solution becomes essential. Such solutions, while approximate, play a pivotal role in addressing complex mathematical challenges.

In this one-day elective course, we will delve into a range of fundamental numerical methods designed to tackle some of the most timeless problems in mathematics. The topics covered include:

 

• Basic Iteration and Convergence
• The Bisection Method
• Newton-Raphson Method
• Understanding Rates of Convergence
• Taylor Series and Its Associated Error Terms
• Numerical Differentiation
• Trapezoidal Method
• Simpson’s Rule
• Error Analysis
• Euler’s Method
• Runge-Kutta Method
• Lagrange Interpolation
• Cubic Splines
• LU Decomposition
• Successive Over-Relaxation (SOR) Methods

R, a robust programming language and software environment designed for statistical computing, is highly favored by academics and widely embraced by statisticians and data miners for their analytical tasks. Join us in this workshop as we delve into R programming, starting from the ground up, to address quantitative finance and machine learning challenges. We will equip you with the necessary mathematical and computational tools from a quantitative perspective.

Workshop Modules:

• Introduction & Installation
• Getting Started with R & RStudio
• Data Handling Techniques
• Crafting Custom Functions
• Data Visualization and Charting
• Statistics and Probability
• Machine Learning Applications in R

Who Should Attend: This workshop is tailored for IT professionals, data scientists, risk management experts, traders, fund managers, and machine learning practitioners seeking to enhance their skills in R for data science and machine learning.

In the realm of modern portfolio management, the concept of risk allocation takes center stage.

Diverging from the traditional Markowitz approach, which primarily addresses the risk-return tradeoff, risk allocation places a keen emphasis on the quantifiable aspects of risk and the establishment of predefined risk limits. This course delves into the intricacies of risk allocation and its practical applications within portfolio management.

Course Modules:

• Crafting Portfolios with a Focus on Risk
• Measuring Value at Risk for Optimal Portfolio Management
• Theoretical Foundations of Risk Allocation
• Putting Risk Allocation into Action

Intended Audience: Professionals in Risk Management, Trading, and Fund Management