Market Risk Analysis SAP MRA

Purpose

The core task of the Market Risk Analyzer is the analysis of market risks in financial positions you are managing. Market price risks in the trading book area and market price/revenue risks in the non-trading book area are represented transparently for purposes of risk control. Operational enterprise activities and treasury transactions are both subject to these risks. The methodical variety of functionality of the Market Risk Analyzer allows the detailed assessment of your existing positions with respect to price determination factors.

Risk Analysis can be used for internal risk control , as well as for regulatory external control . Both processes are required by the Capital Adequacy Directive, for example, the idea being that there should be no conflicting goals between external and internal risk controlling.
Today's risk management concepts either focus on NPV target figures (such as equity NPV), or on a periodic result. In SAP's Risk Management you can use both types of figure for controlling.



For trading book areas, value at risk and NPV figures are calculated using stress scenarios. Grid scenarios are used for option price risks. In these scenarios, you can depict the risk effect of interest and volatility shifts on option portfolios. The system also offers standard procedures for the annual band method and the duration method in compliance with the German Banking Act. The requirements for internal models laid down by the Capital Adequacy Directive are fulfilled, and also the main requirements of Principle I of the German Banking Act.

The value at risk and NPV concept procedures are also used in the non-trading book area. Gap analyses, balance sheet structure simulations and P+L simulations (all supporting strategic new transaction simulations) are available in the system for depicting period risks. These tools allow you to disclose risks quickly - in particular revenue risks. Several different procedures are available for determining new transaction volumes. These can be used to plan and evaluate new transactions in the context of different organizational structures.

Implementation Considerations

To use the functions of the Market Risk Analysis (IS-B-RA-MR) application component, you need to make the necessary system settings in Customizing under

SAP Banking > Strategic Enterprise Management (SEM) > Risk Analysis > Market Risk Analysis.

Integration

P+L simulations are used to simulate the net interest income as well as interest and currency related write-down risks. By means of these calculation routines, you can calculate the desired loss limitation for a given horizon (in compliance with CAD, for example).

In addition to P+L simulation, the gap and NPV analysis functions can be applied to future periods in time, taking into account the new transactions planned. The possibility of taking planned new transactions into account allows you to determine the market value of your equity at the end of the following month, for example.

Scope of Functions

The Risk Analysis modules comprise the following evaluations:

1. NPV analysis
2. Value at risk analysis
3. Gap analysis
4. Balance sheet structure and P+L simulation

Risk Analysis is designed (in particular with regard to data aggregation) to enable you, in the context of risk-return approaches , to compare the revenues and risks of any organizational units or product groups, as well as view them in relation to the risks and revenues of the entire bank. This leads to greater transparency for future decision-making, particularly when you are dealing with questions relating to optimal equity capital allocation.

Basic Principles, Architecture, and Data Retention

To understand the basic concepts, it helps to sidetrack a little from our actual topic, the Analyzers, for a brief look at their roots, SEM Banking. This process allows us a view of the key features of use and concepts. As an analytical module, SEM Banking has no actual operational processes like creation of transaction, payments, or accounting evaluations.
It loads market and transaction data from the operational processes from different systems into its database, then performs the actual analyses. So, it acts as a sort of data warehouse. Each external system has its own concepts, entities, and architecture. Thus, the first task is to transform the different entities into a uniform language and format. Here is where we need the terms financial object and analysis characteristics, which we explain next.

Financial object

The financial object is the central harmonization entity on which all the loaded positions and transactions are mapped. But it isn't a central data object containing all the information of a position or transaction. Rather, the financial object is a meta-object with the following properties:

The financial object is the central harmonization entity on which all the loaded positions and transactions are mapped. But it isn't a central data object containing all the information of a position or transaction. Rather, the financial object is a meta-object with the following properties:

• It contains information about which type of financial instrument it is based on—that is, where the actual position or transactional data is located. This can involve both an external position and a ledger position, or a simulated position.
• It contains central administrative information.
• It contains the information needed by and optimised for each analytical module in different parts, such as a series of analysis characteristics as attributes, for instance.

Analysis characteristics

The analysis characteristics provide a harmonized view of the concepts of an external system. You can define your own analysis characteristics so that the analytical requirements and the descriptions and entities selected for them can be decoupled from operational concepts — but need not be. This lets you control the filling of analysis characteristics from the different systems individually via derivation rules.
If you replace the external system with the Transaction Manager, you now have a notion of the core features of the architecture of the Analyzers. This inheritance from SEM Banking results in a few characteristics of incredible value for the Analyzers:

• Availability of information-containing structures adapted to the needs of each analytical application.
• The use of analysis characteristics that can be defined independently from operational concepts and attributes.
• The capability of processing and analyzing data not only from SAP Treasury and Risk Management, but also from other modules like SAP Cash and Liquidity Management, SAP Loans Management, General Ledger (FI-GL), SAP In-House Cash, or simply manually entered objects.

This gives you the capability of building a type of data warehouse with powerful analytical components.

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