Build It in a Day Seminar, Presented by the ERM Program

January 19 – May 3, 2024
Time for all workshops: 1:00 pm – 4:00 pm ET

These workshops offer the opportunity to experience risk management processes by building them. Examples include counterparty credit risk estimation, FAMA-French factor models, bank default modeling, risk management infrastructure at a lending company, the Fundamental Review of the Trading Book (FRTB), Comprehensive Capital Assessment and Review (CCAR). These major concepts are as much about their implementation as they are about their theoretical description; and organizations gain competitive advantage by effectively implementing these concepts. As a professional, a first step to adding value to this work at an organization is knowing how to build a simple version in a day.

How To Join a Session

Click here at 1:00 pm ET on the day of your desired workshop to join virtually. Each workshop ends at 4:00 pm ET.

Workshop Info 

Friday, January 19 | 1:00 pm to 4:00 pm ET
Economic Capital Model
Understand the concept of economic capital and its application in assessing and managing risk at the enterprise level, including methodologies for quantifying capital requirements and optimizing risk-return trade-offs.

Friday, January 26 | 1:00 pm to 4:00 pm ET
AI, Text Data, Trading, and Risk
TBA.

Friday, February 2 | 1:00 pm to 4:00 pm ET
CCAR
Explore the entire process of CCAR and simulate one of the government defined scenarios for a given portfolio. The workshop also reviews different institutions and their official CCAR submissions.

Friday, February 9 | 1:00 pm to 4:00 pm ET
FRTB (Fundamental Review of the Trading Book) 1
Delve into the revised market risk framework. Build the standardized model to understand the impact on capital requirements and risk management practices for financial institutions.

Friday, February 16 | 1:00 pm to 4:00 pm ET
FRTB (Fundamental Review of the Trading Book) 2
Delve into the revised market risk framework. Build the Internal model to understand the impact on capital requirements and risk management practices for financial institutions.

Friday, February 23 | 1:00 pm to 4:00 pm ET
Generative AI in Finance
TBA.

Friday, March 1 | 1:00 pm to 4:00 pm ET
Bank Failure Modeling
Probability of Default (PD) scorecards have become the workhorse of credit risk management. We’ll talk a little about their history and how they have been changing over time and will build a rudimentary bank failure model using publicly available data. We will also cover the future of this credit analytics and how best to prepare for it.

Friday, March 8 | 1:00 pm to 4:00 pm ET
Causal Modeling
Explore causal relationships in financial data and learn how to build statistical models to identify cause-and-effect relationships, enabling participants to make better-informed decisions and predict outcomes.

Friday, March 29 | 1:00 pm to 4:00 pm ET
Interest Rate Modeling
We’ll examine the assumptions and mathematics underlying interest-rate modeling, building models of interest-rate evolutions used in generating the forward movements used for interest-rate derivatives, and the Risk Management of “Rates risk."

Friday, April 5 | 1:00 pm to 4:00 pm ET
Generative AI in Finance
TBA.

Friday, April 12 | 1:00 pm to 4:00 pm ET
Build a GPT Model
We will cover the fundamentals of neural net modeling and show how to create a large language model (LLM). We will also explore the world of existing pre-trained models and show how to adapt them to your specific needs. If time permits, we can explore where the field is going and new interesting research.

Friday, April 26 | 1:00 pm to 4:00 pm ET
Fama French Factor Modeling
Build the Fama-French three-factor model and its extensions, examining the impact of factors such as size, value, and profitability on asset returns, and their implications for portfolio construction and performance evaluation.

Friday, May 3 | 9:00 am to 12:00 pm ET
Generative AI in Finance
TBA.

Friday, May 3 | 1:00 pm to 4:00 pm ET
Mutual Fund Performance Modeling
Analyze performance measurement and attribution techniques for mutual funds, including risk-adjusted performance measures, benchmarking, and factor-based models, to evaluate and improve fund performance.