Quantitative Analysis & Model Validation

Quantitative Analysis & Model Validation

Overview

Today financial institutions rely on numerous financial models for various business applications ― such as risk management, valuation, hedging and financial / regulatory reporting. Over the recent years, regulatory requirements and innovation in business models have further increased the complexity of the financial models. This increased complexity and sophistication of the financial models expose the financial institution to model risk, which can lead to financial loss, poor business and strategic decision making, or damage to an institution’s reputation. Further financial institutions are required to compute standardized Model Risk indicators, across all asset classes, in a well documented and reproducible manner.

 

As part of our Model validation services, we assist clients with independent model validations for a wide range of financial models; including- Risk models, valuation models and hedging models. We provide the validation not only of the models or methodologies at hand, but also of their implementations. This involves reviewing the code provided by the front-office, running simulation stress tests, back tests and developing prototypes as alternative implementations for cross-check

Our Process

Replication We employ well known tools and environments such as VBA, Matlab, Excel, R to replicate the model calculations.
Comparison to Other Models Comparing the results of other valid models to the model being validated
Face Validity Asking experts about the system whether the model and its behavior are reasonable
Backtesting Using the existing historical data to backtest the model – Analysis of outliers, explanation for deviations etc
StressTesting Test the model under extreme scenarios of Market (e.g. high volatility or high correlation or High Interest rates, negative interest rates) – Historic stress simulation, replay scenarios etc
Sensitivity Analysis Verify the effect of change of the inputs and internal parameters of a model to determine the effect on the model’s behavior of output

Our Advantages

  • Extensive experience in various financial models, products, valuation of plain vanila and complex financial derivatives
  • Hands-on experience with programming languages and tools
  • Expertise in prototyping in vba, matlab, r, python
  • Quick delivery
  • Focus on high level of transparency and documentation

Client Advantages

Clients can greatly reduce their Model risks such as:

  • Incorrect model – The model does not describe the problem at hand
  • Incorrect assumptions and methodology (e.g Correlation in VaR Model)
  • Badly approximated solutions
  • Incorrect implementation – Software and hardware bugs
  • Incorrect data usage or poor data quality (e.g proxies, missing data)
  • Using models designed for liquid market factors for illiquid market factors