COMMODITY DATA MANAGEMENT
Although many companies – large
and small – perform their curve construction and management within an Excel environment, the use of such a medium has its issues. They are difficult to maintain with overly complex formulae to account for rollover and holiday calendars; different asset class quotation periods are required; there is a lack of audit traceability and stewardship management; and there’s a lot of manual intervention, onerous data manipulation, lack of data version control, and inadequate data licence control and traceability. It is thus difficult for any COO or senior risk manager to defend their decision to spend large sums of money on a new E/CTRM system, while retaining Excel as the key analysis and curve management tool in today’s accounting and regulatory environment.
Getting the Architecture Right Figure 2 details the data management
architectural framework. This provides the flexibility for managing all types of data in the commodity enterprise as a precursor to the construction, management and control of the forward curves. If you use industry leading
Figure 2: Agile Data Management High Level Architecture Presentation Layer .NET
SOAP Web Service Client VBA
JAVA SOAP/HTTP/REST Services Layer
Web Services Security & Auditing
Scheduling Business Rules
Messaging Layer Message Queue (JMS Service Provider) C++
Source: DataGenic Data Access PROPRIETARY JDBC
Reference Data Time-Series Data Transaction Data Persistence Layer
development and integration technologies (as well as an agnostic database system, application server and operating system), this kind of framework will no doubt offer long-term benefits and maximum flexibility and scalability. There are, of course, many things to consider for an organisation to achieve this outcome. For instance, does the current internal system warrant further investment to realign the business expectations, or is it better to look outside to a specialist supplier of data management and curve applications and services? There is no easy answer here. There may be very strong arguments on both sides for ‘buy versus build’, which would include discussions on the time to market, migration costs, software costs, maintenance, service reaction time, enhancements input and timeliness, reliability, team knowledge, etc. This information needs to be processed and analysed in a systematic way with a non-biased agenda to ensure the right decision for the company. Having this ‘controlled framework’ in place – that is to say, the
Database
right data management framework strengthened by good data management and stewardship practices – supplies the backbone and integrity to provide the level of confidence in the market
Database
Database
observable forward curve data. However, under a Level 2 fair value hierarchy and using aMarket Approach Valuation technique, the fair value measurement input data is somewhat more complex. There are many things to consider for the construction methodology alone, notwithstanding all the other complexities of
Having the right agile data management platform in place allows the full complement of services to be harnessed during the curve build and management processes
managing curves. These are determined by the type of commodity, curve frequency (and resolution), market liquidity, comparable commodities, vendor quotations, seasonality, storage, transportation, etc. There are no ‘one- size-fits-all’ scenarios, as each curve is specific to the OTC instrument and its particular characteristics, although having the right framework allows reusability of curve libraries.
June 2012 61
OLAP
Archive Dictionary Conversions Modelling Functions Curves
Workflow Quality Reporting Matrices
Import / Export
Quality Derivations Integration
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