This course aims at finance individuals, including risk managers, investment managers, and analysts.
IBM Algorithmics Foundations to RiskWatch
RiskWatch™ is the core analytical engine within the Algo Market Analytics product, providing a complete set of methodologies to measure, monitor, simulate, and restructure risk. This two-day course is intended to provide participants with an overview of RiskWatch functionality, and hands-on experience with various methods of setting up and analyzing portfolios.
The participant will be able to:
- Build financial instruments with the associated models and risk factor 'curves'
- Assemble portfolios of financial instruments
- Build a portfolio hierarchy
- Model a spread curve
- Understand the procedures for modeling financial instruments with currency exposure
- Build Scenarios and Scenario Sets in RiskWatch
- Use Scenario Sets as a basis for stressing portfolios to generate Mark-to-Future (MtF) portfolio valuations across time.
- Set up the Stress Room with required attributes, including the use of simulation functions
- Calculate Value-at-Risk (VaR) in RiskWatch using the Monte Carlo and or Historical simulation methods
- Aggregate portfolios by various single and multiple attributes
- Build risk management reports on the portfolio
cena szkolenia netto
This two-day course is delivered through a number of mediums, including slide presentation, product demonstrations, instructor-led exercises and self-paced hands-on practice.
- Introduction and course agenda
- RiskWatch within the Algo One framework
- RiskWatch Navigation
- Building financial instruments in Riskwatch
- Defining models and risk factors
- Building portfolios and portfolio hierachies
- FX Room overview
- Defining scenarios in RiskWatch
- Differentiation between Standard, Generated, and Iterative Scenarios
- Setting up the Stress Room for across-time and scenario set valuation of portfolios
- Calculation of Historical and or MonteCarlo simulated Value at Risk (VaR) in the Stress Room
- Calculation of Parametric VaR
- Simulation functions
- Portfolio Aggregation
- Exporting results and building MtF Cubes
Wymagane przygotowanie uczestników
Students should have:
- Basic knowledge of financial modeling, risk measurement, and derivative finance.