Trading & Hedging Decisions
Box I: NQuantX Analytical Framework: A New Paradigm of a Shared Analytical Environment
The NQuantX Analytical Framework was designed to provide risk managers and corporate hedgers at non-financial corporations with state-of-the-art tools to measure and manage market, counterparty, collateral, and liquidity risk.
Even though ETRM systems implementations require substantial up-front investments and long implementation times, the risk management functionality of most systems is limited at best. In addition, many firms have multiple ETRM systems in place to manage different parts of their business but struggle to have a comprehensive view of risk at the firm-wide level.
As such, many firms end up relying on isolated solutions running on multiple workstations, which results in a culture of compartmentalised, non-shared and non-standardised data and analytic computation. Many of these isolated solutions rely on in-house or third-party analytical tools designed to provide single-instrument, single-time slice (e.g. current Mark-to-Market) pricing.
Those tools may work well on with the single-user model, but clearly fail to meet the needs of enterprise-wide deployments that require multi-step simulation of critical state variables and modelling the portfolio response. Compartmentalised solutions also suffer from scalability issues. Adding new instrument types and valuation routines is usually haphazard at best.
Based on our experience, the technical challenges to building a useful system to perform dynamic risk analysis are manifold. Fortunately, new technologies and programming techniques such as XML, .NET and modular framework architectural design can bridge those information gaps and bring down IT costs associated with sharing this information. For example, contract, market and counterparty information that often resides in different systems used by market and credit risk groups needs to be integrated in order to perform Earnings at Risk (EaR) and Cash Flow at Risk (CFaR) analysis.
To capture the value of a shared environment, energy firms need to change how they develop and implement analytic, one-off solutions. For example, calibration of individual analytic models may benefit from including cross-portfolio information. Also, realistic dynamic portfolio simulations necessitate inclusion of new trades determined by an overall portfolio, and not an exclusive single asset or set of exposures. Thus, most analytical solutions, even those related to instruments that belong to a particular instrument class, require a truly universal view that can be made possible by a shared analytical environment.
Modular Framework Design
A flexible risk engine should allow for the segregation of analytic processes to enable modular design and increased scalability. For instance, the separation of instrument-level valuation analytics from the market scenario generation engine increases the ease of editing or adding new valuation or Monte Carlo routines in the future. In addition, a modular design gives the risk engine the ability to plug-in in-house or third-party analytical pricing models or use sensitivity results (delta; delta-gamma approximations) coming from existing systems. Conversely, a modular framework design also helps increase the usability of results into other legacy systems.
Data Aggregation
New technologies exist which streamline the storage and consistent definition of data across the enterprise, and risk management systems should take full advantage of them to achieve consistency of risk information across different business units. Another benefit is the ability to calculate incremental and marginal exposure and risk metrics at different levels of the portfolio hierarchy. For example, if scenario-level results are stored at the lowest necessary denominator (e.g. trade or any sub-portfolio), the data aggregation process provides output data which is more universally useful. The impact of hypothetical trades can also be evaluated in the context of a larger portfolio, using the same calculations in the existing portfolio of instruments used for risk analysis, without the need to recalculate the full portfolio again. This not only streamlines the risk analysis process, but enables the work of risk analysts to assist other parties that require risk information for decision-making such as traders and senior managers.
The NQuantX analytical framework is written in C++ for scalability, speed,and ease of integration. The Framework can be
accessed through multiple interfaces and allows for quick deployment of new models for pricing and risk analysis of energy and commodity physical and derivatives portfolios. Source: NQuantX
40 worldPower 2010
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