RISK ANALYTICS
Stress Testing
Risk Analytics calculated by shocking portfolio risk factors such as FX Rate, Yield Curve, Volatility, etc
e.g.: What is portfolio risk if USD interest rate increases by 50bp?
Overview
Understanding changes to portfolio exposure, VaR and other analytics by shocking one or more risk factors i.e. hypothetical scenarios is an important risk capability for multi-asset portfolios.
Users can create individual factor shock scenarios and also composite scock scenarios that include shocks to multiple risk factors.
Note – The ‘Dolphin’ risk platform uses portfolio re-valuation technique to calculate all risk analytics. Therefore shocking of multiple risk factors will result in affecting the underlying security positions for only those risk factors that are applicable to that security.
For example, Equity instrument results are not affected by shocking just the yield curve however fixed rate instruments are impacted. And of course, fx rate shock can have an impact on both the instruments.
Key Features
Provide both Single and Composite risk factor scenarios
1. ‘Single risk factor’ scenario enables shocking of a single risk factor
e.g.: Shock FX rate or Shocking of Yield Curve
2. ‘Composite risk factor’ scenario provides ability to shock more than one risk factor together.
e.g.: Shock YC, FX Rate and Asset Price all together as one scenario
Support multiple risk factors for stress testing
1. Yield Curve factor (Paralle and Non-parallel)
2. Asset Price factor (Single Security and Index)
3. FX Rate factor
4. Volatility factor
5. Spread factor
Stress Testing Screenshots
View configuration and result screens of Stress Testing calculations
Stress Testing Demo
Demo running of Stress Testing Calculations
FAQ
Here are some answers to Frequently Asked Questions.
Can we perform Stress Testing in real-time?
All risk calculations can be run by users using user interface and calculation results can be viewed using web user interface.
Calculations are not batched and are run as and when requests are made.
System scales the number of engines required dynamically as volume of requests increase and the number of engines are brought down if no requests are pending thus enabling pay-per-use business model.