Ermas Consulting Inc. : Exceeding Expectations

Our Approach to Integration Consulting

Ermas Economic Capital

  1. Overview
  2. Benefits
  3. Features
  4. Screenshots
  5. System Requirements

Overview

  • Highly configurable platform allows implementing proprietary Economic Capital/ Credit-Value-at-Risk models for most accurate risk management.
  • Ermas Economic Capital supports building a holistic Economic Capital model by modeling and integrating different risk types consistently.
  • Economic Capital models can include Monte-Carlo Simulations, Historical Simulations, Parametric Models or Proprietary Models.
  • For example Economic capital models can depend on market risk factors (including interest rate risk, foreign exchange risk, equity risk and vega risk), credit risk factors (such as default probability, loss severity, and prepayment speed), and (macro-) economic factors (such as unemployment rate, gross domestic product growth rate or house price indices) among other factors.
  • The integration of different risk types is based on jointly modeling and simulating all risk factors allowing correlating all risk factors among each other, whereby risk factors may partially offset each other.

Benefits

Cost efficient
  • Ermas Software deployment is based on basic SAS Modules referring to SAS BASE, STAT, ETS, IML, Graph, Integration Technology and SAS Share. This is a very cost effective way of deploying Risk Analytics.
  • Ermas Software also supports R language, C and C++ code. This enables the user for example to build Risk Analytics based on R open source language, in order to reduce commercial Software Fees.
Integrated with SAS
  • Proprietary SAS Model code can be fully applied within Ermas software.
  • Ermas Software is fully integrated with the SAS language and enables the user to develop proprietary SAS models and to expedite the deployment of new production SAS models.
Scalable
  • Ermas Software can leverage a multi-core computing infrastructure and can be grid-enabled for fastest processing and scalability.
  • For example, Ermas Software can use SAS Connect (MP/Connect) to distribute processing load across multiple CPU cores fastest processing and maximum scalability.
  • No limitation in data volume processed.
Flexible
  • Open integration with proprietary or third party models allows to apply 'best-in-class' models for each financial product type ensuring unlimited financial product coverage and leveraging full pricing.
  • Each individual model (such as cash flow model, pricing model, PD-, LGD- and prepayment model, term structure model, credit-Value-at-Risk model, Economic Capital model, or other model) can be chosen by the user allowing leveraging proprietary knowledge and propriety models to perform most accurate risk management.
  • The Ermas Risk Framework allows banks to develop, validate, deploy, and track risk models faster, cheaper, and more flexibly than outsourced alternatives or desktop solutions.
Consistent
  • Ermas Risk Framework provides a risk management framework that is consistent with best practice of risk modeling in the financial services industry.
  • Consistent, accurate and reliable data is required in order to achieve best practice in risk management. Based on Ermas Risk Framework you can efficiently manage vast quantities of data from across the enterprise - this includes data from loan capture systems, trading systems, and risk factor agencies, loan performance data, among other data.
  • Ermas Risk Framework ensures to apply risk models and model parameters consistently across all portfolio and exposure types.

Features

  • Correlations and co-dependencies among all risk factors are considered either based on a covariance matrix or based on copula functions.
  • Correlations can be stressed for example by increasing or decreasing correlations by a certain percentage. A stressed correlation matrix/covariance matrix can be used to drive the simulation.
  • Ability to perform large-scale multivariate simulations from separate models, each of which may be fitted by using different distributional specifications, which includes non-normal distributions, allowing combining different types of risk factor models within one simulation analysis (mixed models).

Screenshots

System Requirements

SAS Software Requirements - SAS 9.1.3 or 9.2
  • SAS 9.1.3 or 9.2
  • SAS BASE, STAT, ETS, IML, Graph
  • SAS Connect
  • SAS Integration Technology
  • SAS Share
Java Requirements
  • Java 1.6
Web Portal Requirements
  • LifeRay 6.0