This seminar will be conducted in English

Day 1: Initial @RISK Functions and Applications

1. Review of @RISK functions on a real portfolio model: Does the institution have dominant portfolios? Which portfolio has a better performance/risk ratio?
2. Project assessment: Net Present Value (NPV) distributions vs. point value estimations: What is the probability of earning over $20,000 on the project? What is the probability of having a loss? What happens if new competitors enter during the process?
3. Brownian geometric motion and random trajectory: pricing, interest rates and exchange rates. How do trend and volatility impact results?
4. Inflation projection and other key variables.

Day 2: Exchange Rate and Interest Rate Risk Models

1. Initial applications of Value at Risk (VAR) methodology in exchange rates and interest rates. Is it valid the normality assumption for series of performances?
2. Assets and liabilities management model (GAP analysis), traditional and with simulation. What factor has a greater impact on the financial margin of the institution?
3. Duration and modified duration models with simulation. How much can rate changes devaluate the equity of the institution?
4. Credit card model with random quota.

Day 3: Liquidity and Credit Risk Models

1. Liquidity model with random cash flow.
2. Model of Value at Risk (VAR) of liquidity. How much can liquid deposits devaluate?
3. Model of VAR of liquidity with correlations: How much cash can be withdrawn from the institution?
4. Loss (frequency and severity) distributions identified with @RISK to estimate provisions and regulatory funding.
5. Credit model with simulation: creating a loss distribution, calculating expected and unexpected loss, and estimating provisions and prudential funding. How much do results change in correlation with systemic risk?

OBJECTIVE: At the end of the course, participants will be able to practically and immediately implement the studied models.

KEY TOPICS:

• Introduction to using simulation in banking and to financial risk (credit, liquid assets, interest rates, exchange rates).
• Provide guidelines on how to improve banking and financial statistical models by introducing uncertainty.
• Recommendations for successful implementation of models commonly used in the field.

» Palisade Brasil
» Cronograma de Seminários