Risk Management for Electronic Trading – Part II

Audience Note & Disclaimer: The targets of this article are larger financial institutions that build or buy, and then integrate risk platforms to cover the electronic order and trade lifecycle. The ideas in this post can also apply to the wider set of firms both large and small who use trading platforms themselves and configure their in-built risk controls.  As an owner of a brokerage or even just its electronic business function, this article contains ideas to help you identify your risk objectives and work out your risk measures. It is meant as a starting point, to help figure out what is important in both design, integration and use. For a dedicated trading supervisor, or an in-business risk manager, it should help you visualise a framework for setting risk controls.  And for those beyond the first line of defence, this article provides useful insight as to how risk policy should work with the business, to achieve both regulatory compliance as well as accurately meet the business’s risk objectives.  This article is Part II of an overview, and will be accompanied by additional chapters covering topics in further detail.

As mentioned in Part I, Risk is often ill-defined and misunderstood. To put it simply, Risk is the probability of adverse events, or the exposure to adverse events.  To what extent is it possible to predict and avoid these events? This is the imperfect task of a risk manager, and the responsibility of the business itself.  Part I has already covered a handful of common examples that roughly fit within the category of 1) Operational Risk of Electronic Trading.  Part II will now provide a few more examples of 2) Market Risk and 3) Credit Risk. I will highlight where Risk Management and Risk Platforms can be better used, and where else they should be applied or developed further.

2) Market Risk of Electronic Trading: The order and the trade itself are the central data points on the timeline of electronic access. Ensuring that the order does not move the market, that its impact is minimalized, many consider to be the learned skill of a seasoned broker or trader. Today, algo-assistance and real time data analytics can help optimise the order-placing decisions, and even take some of the decisions themselves.  Best execution is the other side of the etrading risk coin – and decision making aids like algo wheels could benefit from taking more of these more obvious downside risks into account.  Even the simpler parameters should be considered: When to trade, what ticket sizes to avoid, which order type of algo is best suited, which counterparties or lit or dark venue will yield the best fill – all can be considered as market risk.  Smart Order Routing (SOR) is a function in itself – but needs to be fully understood, configured and owned by the business. To set SOR rules does increase automation and responsiveness, but does not necessarily reduce market risk in itself.  At the other end of the scale, more rudimentary controls are commonplace, like the ubiquitous “max order size”, per lot or notional values.  However, they would better be defined by percentages of average daily volume or of median order size.  Similarly, as with the Operational Risk parameters, the input and maintenance of these Market Risk limits can be manually intensive and could in theory be derived from a single risk appetite value, applied to a market-based risk parameter simple mathematical model. As mentioned in Part I, more automated and intelligent controls will bring alternative risks of technical failure unless carefully architected.

3) Credit Risk of Electronic Trading: Depending on where you sit, and what role your business plays, credit risk will mean different things to you.  To a Prop Trader, Credit Risk may be the availability of your own credit line from your prime broker.  To the Broker, this could be the overall remaining, unutilised counterparty buying power of your client from their traded and cleared positions. Comparatively, to a large Asset Manager, Credit Risk may be the credit worthiness of your broker, or various trading counterparties and potentially even the credit worthiness of specific venues and clearing houses .  Perhaps the latter may apply if the clearing house’s default fund is small, opaque or not frequently tested. However, regardless of one’s function or one’s own firm, the approach to calculate Credit Risk will have some commonality. In terms of controls, you can measure the value of what is traded, and what is “at risk” (to clients, to counterparties, to brokers…).  This can be done on an individual order basis, or on a more aggregated basis – such as by product, within the trading day’s activity, or the current, live portfolio in full.  Consideration should be given to open (and as yet unfilled) orders – as this is a reflection of what could be traded.  Measurement can be done on various bases: by simple notional, or by margin & P&L, or going beyond these with more complex greeks, and second order metrics. Given that the central role of financial institutions is still credit intermediation – it is safe to predict that those that better define, measure and control credit risk, will perform better as a business in the longer term.

Risk is often seen as binary, a black and white event.  In reality it is often a series of events in the run up, and time directly after a larger event.  A further post will examine risk as a series of events, and put this in the context of control frameworks and optimal system design around workflows and data analytics.

In summary to the three main risks described here, risk is often ill-defined, and there are a great many dimensions to risk, depending on what role you play in the execution process, and what sort of market participant you are.  Operational, Market and Credit Risk within and around Electronic Trading can overlap. It is too easy to be distracted by what is currently available in terms of basic risk checks within platforms, without due thought as to what you actually want from these controls.  What controls are in place, how the values for these controls are derived, reviewed, and maintained are all important aspects. A clear understanding of electronic trading business activities will help, and is a necessary first step towards defining a firm’s own risk objectives.

Taking a step back, a firm’s Risk Management objectives must be defined and be intuitive and supportable. These should take into account all the identifiable risks around the firm’s electronic trading activities .These objectives will then lay the foundation both risk policy, and a corresponding control framework. 

Good control frameworks can be overlaid not just on newly introduced or upgraded platforms but also can be adjusted for new activities.  They can also fit evolving Regulator requirements and changes to business risk appetite (also referred to as a firm’s risk profile).  A control framework is best visualized as a pyramid-shaped hierarchy: objectives and policy are the highest blocks of the pyramid often overlapping in part, with the lower levels representing the key controls (and the risk metrics).  The control framework provides an intuitive means of describing the current state, to test and to prove the efficacy of the controls the business has mandated.  Ultimately, in all policies and control frameworks, there needs to be clear business responsibility and an auditable, robust mechanism for accepting risks (formally referred to as ‘risk acceptance’).  Building your framework is absolutely key to a scalable and truly risk-managed business. From this framework, a firm can then carefully select, integrate and use trading and risk platforms as part of their risk-managed electronic trading architecture.

More information, risk assessment and formal selection review is available upon request.

Norton Edge provides Subject Matter Expertise, helping you understand your firm’s risk, define your risk objectives and control framework. Norton Edge helps you apply this across your electronic trading and risk stack.  Our experience is cross-asset class, and a broad range of firms from small family office and proprietary trading companies, to large buy- or sell-side institutionals integrating or developing new electronic and risk platforms.  Norton Edge provides guidance straight from the designers-in-chief and business owners of Risk Platforms and broader Risk Systems.