Managing a derivatives portfolio through turbulent markets

Steering a portfolio of non-linear derivatives, such as options and more exotic products, is challenging at the best of times. Market risks change as markets move and time passes, risks offset in complex ways and proxy hedging is common. In this feature, Mark Higgins co-founder and chief operating officer of Beacon Platform, explores the importance of understanding how profit and loss is explained by different market components to effective portfolio management

 

This is most definitely not the best of times. The coronavirus pandemic has caused huge moves in every market, creating significant dislocations and stressing model calibrations to their limits and beyond.

I’ve lived through several periods of extreme volatility in my career as a quant and market-maker: for example, the Asian financial crisis of 1998, the burst of the dotcom bubble in 2001, the global financial crisis that began in 2007–08 and the European debt crisis of 2011. I’ve seen trading desks manage their positions effectively while continuing to service their clients because they had robust and nimble risk systems, and I’ve seen desks drop years of accumulated profit in weeks because they did not. After a long career on trading floors at two of the biggest banks in the world, I co-founded a company called Beacon Platform, which is helping clients across the financial services industry manage risks effectively. Here are five key lessons I’ve learned along the way about managing risks in turbulent markets and what makes for a good risk system.

 

1. Know your book

Portfolios of options and exotics can have between thousands and hundreds of thousands of open positions at any time, and understanding what happens to your book under large market shocks is impossible without flexible real-time position and scenario reporting.

Position reporting lets you zero in on the makeup of your portfolio where it matters. For example, knowing what strikes are nearby for options expiring in the short term helps you understand your ‘pin risk’ – how delta changes quickly as the underlying asset price crosses the strike level because of the concentrated gamma there. Or understanding where continuous barrier levels are for a portfolio with foreign exchange knockout options – delta jumps when the barrier is breached, but does not jump back when the forex spot price moves back. Keeping accurate track of your delta is really important in volatile markets, since delta risk is typically the largest contributor to profit and loss.

These risks go beyond the traditional Greeks reports that show local portfolio behaviour and help portfolio managers better understand the global nature of their risk.

 

2. Every scenario is different

As US Federal Reserve governor Kevin Warsh said: “If you’ve seen one financial crisis, you’ve seen one financial crisis.” It’s critical to give traders the flexibility to understand their books in the ways that make sense for the specific environment in which they find themselves.

Similarly useful is being able to view how risk metrics change under different market scenarios. Traders are accustomed to ladder reports that show headline risks under a simple set of market scenarios, such as different parallel shocks to the yield curve or a standardised set of equity shocks. These are useful for understanding non-linear risks beyond instantaneous gammas and cross gammas – especially for larger moves when the local Taylor series second order approximation breaks down.

Never let a serious crisis go to waste

   Rahm Emanuel, former White House chief of staff

In stressed markets, traders often want to see risk metrics under a custom set of specific scenarios that they feel represents plausible moves for the particular crisis period they’re experiencing. Your risk system should allow for this as well, either through custom scenario reporting or, more likely, through new bespoke reports written by financial engineers working on the desk who can translate qualitative instructions from traders into quantitative scenario definitions.

That ability to create new fully customised reports quickly, generally through programmatic extensions to reporting tools, is a hallmark of the best risk systems. Having worked with dozens of risk systems over the course of my career, I’ve observed that most of them do not have the developer tools that allow for such customisation. So, when building Beacon, it was important the platform had a full suite of developer tools that would let quants create, test and deploy modifications to reports as quickly as possible, while having controls around code release and a full audit trail of all changes.

 

3. Markets move in real time, and so should your risk

Being able to report on theoretical risk moves is useful, but even more important is seeing your risk calculated live from real-time market data inputs. Some risk systems do this through approximation techniques, such as a Taylor series based on the previous night’s closing risks, but that approach is extremely fragile in extreme market conditions. 

For example, in late 2008, a lot of exotics desks were using Taylor series approximations from the previous night’s risk calculations to manage their complex cross-asset risks. A rates/forex hybrids book might have a cross gamma between forex and rates, where a 1 basis point move in rates could translate into billions of US dollar‑equivalent forex position change. Rates were moving intraday by tens of basis points – well beyond the regime where the risk approximations were valid, so approximation error on real-time forex delta could run into tens of billions of dollars – at a time when forex rates were moving by several per cent in a day. A book could lose a year or more of accumulated revenue through a single day’s error in extrapolating real-time risk.

Full real-time risk recalculation is generally the most robust approach but often requires considerable compute resources to effect with a cycle time small enough to matter in fast-moving markets – such as 15–30 seconds for complex portfolios. In the past, real-time risk was difficult to achieve for many trading desks due to limitations in compute infrastructure; today, elastic cloud computing is an efficient way to scale up computing resources when you need them, and has the added benefit of avoiding paying for compute when you don’t. When my co‑founder and I designed Beacon, we made sure to build a user-friendly elastic cloud compute service right into the platform, so quants and traders could access compute on-demand to run real-time risk, at scale.

 

4. You must be able to react with confidence

In late 2007, forex-implied volatility skews were at all-time highs in the first phase of the financial crisis. This led to significant issues calculating accurate risk metrics across Wall Street because standard exotic pricing models were failing to calibrate to market levels. Risk reports errored out, and traders were flying blind.

Quants quickly figured out how to adjust the models so they could calibrate properly. However, at many shops, quickly changing the production risk system was impracticable – either the models were baked into the system by the vendor, or production deployment was infrequent and bureaucratic and couldn’t respond to rapidly changing conditions. Traders were pressed into using questionable approximations in handcrafted spreadsheets to cover the gap, and ended up running very conservative positions.

At market-making shops with the best risk systems, quants could make those changes intraday, while still satisfying institutional controls on technology deployment. Their traders navigated that market turbulence with all their instruments working. They could manage risk effectively and provide liquidity to their clients because they could see accurate and robust risk metrics, while many of their counterparts at other dealers could not.

It’s hard to balance nimble and iterative system changes with enterprise control policies designed to protect against accidental errors. Setting controls too tight leads to inflexible and slow-moving systems that can’t respond in a crisis, but setting them too loose leads to traders hedging off the wrong numbers. Having seen these trade-offs first hand, we built Beacon with the right mix of flexibility and controls and have successfully deployed the platform to businesses within highly regulated institutions, such as banks and insurance companies.

 

5. Having a good risk system makes all the difference

As with a pilot landing an airliner in fog, performance matters most when conditions are toughest. For trading desks, the key elements of good risk systems are:

• Controlled flexibility. Ideally, quants and engineers should be able to react quickly when market conditions demand it, without compromising code and testing quality. This balance is possible, but must be designed into a system from the ground up.
• Accurate real-time risk. When markets are moving, traders need to be able to trust that their risk system is presenting the right numbers, or they’ll either put on the wrong hedges or become scared and conservative when their clients need them the most. 
• Models need to work. Whether ensuring models are robust in extreme market conditions, or being able to adjust them when they break, it’s critical to be able to tend to models in real time – otherwise, you’re flying blind.

As a former quant and market-maker, I recognise how challenging it is to manage risk in the current environment. One of the reasons I co‑founded Beacon was to build a superior risk system that both dealers and their clients could use to navigate markets in good times and bad. When quants, engineers and traders can use the same platform to work together, it helps everyone manage risk and better serve clients. Having a good risk system with an integrated development platform that allows this kind of collaboration can make all the difference.

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