Oct 20, 2015

Risk Executive Series | What’s Driving Risk Infrastructure for OTC Derivatives?


James J. Jockle, Chief Marketing Officer at Numerix, discusses the latest requirements and challenges driving the evolution of  risk infrastructure amongst derivative market participants.

Given the vast amount of change in the OTC derivatives industry that’s taken place since the crisis, the growing list of technological demands on risk infrastructure, and the high stakes of financial and reputational loss on the line if infrastructure fails; risk managers have an immense responsibility on their plate when it comes to risk infrastructure. In the words of famed British architect Norman Foster, “As an architect, you design for the present, with an awareness of the past, for a future which is essentially unknown.

Have you taken a closer look at the requirements driving the evolution of your risk infrastructure? Are you addressing all the necessary requirements, at the optimal time, in the most efficient way?

And while, the regulatory future still remains in part “unknown,” with many shades of grey in between, today’s derivative practitioners can still plan for the future by pinpointing and addressing the key requirements currently driving risk infrastructure. What’s keeping you up at night? Let’s start by taking a closer look at the regulatory and other requirements driving risk infrastructures amongst derivative market participants.

Requirements Driving Risk Infrastructures


  • XVAs starting with CVA but also DVA, FVA, KVA, etc.
  • Required by Front Office, Risk and Treasury
  • Incremental and Marginal measures needed for each XVA
  • Pre-trade i.e. near real time performance is needed
  • Often Greeks & Hedge calculations required for XVAs
  • Ties directly to profitability, risk and capital optimization

Market Risk/FRTB

  • Need for Pre-Deal and Marginal Market Risk Measures
  • Focus on P&L driven by Volcker as well as FRTB & similar regs
  • Move from small number of VaR measures to large number of Expected Shortfall measures across varying liquidity horizons


  • Need to evaluate CSA options as institutions migrate to new CSAs
  • Increasing collateral needs due to IM/VM and central clearing
  • Firm wide collateral inventory needed as an input
  • Ability to identify collateral costs and collateral mitigating trades


  • Ability to replicate or approximate CCP Margin
  • Calculate bilateral IM/VM based on SIMM
  • Regulatory deadlines start in 2016 for the biggest banks
  • Allocate Margin to the trade and calculate pre-deal margin impact


  • Capital calculation based on Economic & Regulatory methodologies
  • Allocation of Capital to Desks and Trades / Porfolios
  • Ability to calculate pre­trade Capital impact, particularly for bilateral trades where Capital impact can be significant


  • Need for a limits system across different classes of risk
  • Flexibility to set limits along any risk measure at any level of granularity
  • Ability to capture limit breaches, utilization statistics intra‐day and end of day

Model Risk

  • Model Risk Assessment, Validation and Monitoring mandated by regs
  • In addition to qualitative standards such as documentation and audit controls, need for quantitative AVAs, reserves to capture Model Risk
  • Ability to run monitoring tests such as Hedge Effectiveness

Stress Testing

  • Enterprise wide stress testing mandated by regulators
  • Stress testing expected to be an ongoing process, frequency of such tests may increase and involve ad‐hoc tests as well
  • As with other risk measures, ability to attribute stress results to specific porfolios, businesses and trades is crucial

If these regulatory drivers are the compass directing us toward where we need to be—let’s also now take a look at the current state of production systems today:

A Checklist: The State of Production Systems Today
  • Do your risk systems fall into any of these categories?

    ✔ Risk management infrastructure that is siloed by type of risk
        (Market, Credit, Liquidity, etc.) and often by asset class  
  • ✔ Limited or fragmented market data warehousing
  • ✔ Traditional "grid"-based compute engines running in a batch mode
  • ✔ Lack of standardized data model for position data
  • ✔ Inability to stress test models in an automated way
  • ✔ Resource intensive (manual) model risk reviews and limited or
        no model risk monitoring 
  • ✔ Heavy use of static reports with limited drill down or
  • ✔ No API or platform strategy limits system integration


The Impact

First, siloed risk systems can be expensive to maintain in terms of human, compute and data costs. In addition, differences in "velocity of risk" between risk management and the front office can mean a delayed influence of enterprise risk and capital on trading decisions. Moreover, inefficiencies may also lead to a lack of timeliness in responding to new regulations, new business models and new market structures. As we've come to see, huge losses due to outdated or inaccurate models can also be problematic, as can limited transparency into risk. Also, the costs of adapting legacy architectures to upcoming needs and expensive, manual stress testing and model risk processes must also be considered. Duplicate processes and systems have also become an industry-wide challenge—in addition to the overall ballooning of risk IT costs.

Managing Growing Requirements

Truth is one. Paths are many. And, in looking toward the future, we are noticing an increasing trend in companies creating blueprints for more of a "Planned" Risk Infrastructure approach. Many institutions are also beginning to consider the ROI of what they consider to be a "Risk 2.0" Approach—which would ultimately include enterprise risk analytics and real-time risk. To learn more about building a "Risk 2.0" approach to risk analytics—and seven common mistakes to avoid in enterprise analytics infrastructure, view our related on-demand webinar.


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