Smoothing the ride

Christian Kahl, President of FINCAD, looks at how using cutting-edge analytics and technology can help investors cope – and even benefit from – market conditions

If there are two areas that remain constant in the derivatives markets, they are change and uncertainty.

Just look at the events in the UK over the past couple of months where pension funds made unprecedented requests for emergency capital to avoid insolvency and meet collateral demands, following a raft of surprise fiscal policy announcements that rattled the markets, resulting in two weeks of declines in UK government bonds and gilts. Sterling reached historic lows against USD before rebounding after the government announced a U-turn on its policy, alleviating concerns of a ratings downgrade for UK government bonds.

Despite the Bank of England intervening to the tune of £70billion to stabilise the markets, the events resulted in a scenario that worked against both the derivative portfolios and collateral positions of many pension funds.

Along with other institutional macro investors, these funds increasingly struggle to keep pace during such turbulence. But a bigger part of the recent UK story was that the market moved with frightening speed against their derivatives positions, and margin calls prompted pension funds to liquidate gilts at unfavourable prices.

That resulted in further downward pressure in the gilt market and difficulties in meeting margin call obligations to the point where the pension funds have now called for greater credit support annex (CSA) flexibility to facilitate so-called ‘dirty’ CSAs (gilts and government bonds) as opposed to ‘clean’ CSAs (cash-only), with a preference for posting corporate bonds as collateral.

It’s an attempt to address underlying concerns around credit rating changes in currently accepted collateral, such as gilts and government bonds.

On average, funds are reporting losses in excess of 12 per cent, which points to a more difficult market overall for institutional investors and reinforces the need to engage in risk management practices as an operating principle, especially where the market continues to shift course in response to changes in monetary policy as a result of fluctuating fiscal strategy.

Navigating these troubled waters, it has been critically important to have insight into portfolio and collateral holdings in real time as well as being able to analyse market and rating scenarios – let alone being able to simulate future exposures under dirty collateral agreements. “In recent weeks, we have seen market participant clients asking for advanced derivative and collateral agreement modelling capabilities,” says Christian Kahl, president of FINCAD, the capital markets division of Zafin, which is a market leader in producing fixed income and derivative analytics software and services.

Some of FINCAD’s proposed methodologies for limiting exposure include collateral forecasting and scenario analysis along with more advanced measures, such as potential future exposure (PFE).

In highlighting what needs to be modernised to cope with present and future volatility, it might be helpful to put what’s currently affecting market operators into historical context.

Since the peak of the global financial crisis in March 2009, equity market indices have outperformed historical averages on the back of a strong global economy and expansive monetary policies where central banks set interest rates to ultra-low levels and engaged in quantitative easing programmes.

In terms of balance sheet expansion, from 2012 to 2022 the European Central Bank balance sheet expanded from €3trillion to close to €9trillion, as did the Federal Reserve Bank’s. Despite this unprecedented monetary expansion, headline inflation was well within the target range throughout this period and even America’s brush with economic isolationism in 2016 did not upset that sense of stability.

Negative rates in the period after the crash, of course, presented challenges, particularly in the Eurozone, where it was negative for eight years until September 2022, when it was increased by an unprecedented 0.75 per cent.

Negative accrued interest is challenging, both in terms of the costly practical

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application and the legal considerations, which gave rise to instruments with floored rates and the need to adopt pricing models that move past the notion that interest rates can be assumed to be lognormally distributed. This was mirrored in the derivatives market: from 2014, derivative analytics providers experienced a rush to expand support for negative rates.

But, as of September 2022, the era of negative rates seems to be firmly behind us. The current situation with inflation driving rate hikes, naturally gives rise to greater liquidity and, ultimately, credit risk, and this results in heightened market risk.

This is most observable with cryptocurrencies, commodities and foreign exchange, all of which had been relatively predictable for a decade.

There are other factors contributing to greater uncertainty now, too. The transition away from the London Interbank Offered Rate (LIBOR), the global benchmark rate for interbank lending, being one of them. While LIBOR reforms are welcome and justified, it will take time for the market to settle on standard conventions for instruments referencing the new indices that replace it.

Another harder requirement that has emerged recently is the need to perform routine model validation. That does not end with calibrating and validating models on the fly, using the applicable market data set, but being able to validate and take data-driven approaches to challenging trading strategies more fundamentally, by running back-tests against large data sets in a timely manner for trade execution.

Derivatives are more and more centrally cleared now, too, leading to computational challenges around collateral calculations, most notably the International Swaps and Derivatives Association’s Standard Initial Margin Model (SIMM).

Meanwhile, the capital requirements of banks have evolved dramatically, not only in requirements to increase their core equity capital but also to be more stringent and descriptive with regards to internal and external oversight.

Basel III/IV and the Fundamental Review of the Trading Book (FRTB) is a comprehensive suite of capital rules that set out strict guidelines requiring significant investment. Including counterparty credit risk resulted in broad changes in both accounting and regulation. When considering fixed income, rates, credit, and foreign exchange against such a global backdrop of change, a lot more attention needs to be paid to not just model validation and pricing and risk exposure calculations, but also to market scenarios, counterparty exposure profiles and value-at-risk limits.

“An aspect that makes the current uncertainty more unique is the fact that the response to COVID-19 was global in nature, both in fiscal and monetary response and, as a result, there is a stronger coupling, as can be seen when looking at the charts of one-year and five-year inflation swaps or equity benchmark indices since March 2020, regardless of the jurisdiction,” observe FINCAD’s Kahl.

“This is very different to February 2007, when the US subprime crisis broke, or in September 2008 when Merrill Lynch and Bear Sterns announced bankruptcy, where, for example, Canada’s exposure was of second order, largely in response to collapsed demand on the back of the recession that was underway in the US.

“All the data points to a stronger focus on market, liquidity, and credit risk whether a market participant’s concern is the need to be able to take speculative positions with confidence, identify mispricing or make informed hedging decisions.”

We see both buy and sell side institutions migrating away from large on-premise systems towards a best-of-breed architecture, using Cloud-based services

The nature of these undertakings brings onerous calculation requirements that are not only computationally expensive but need to be supported by scalable technology.

“Over the last decade or so, we have seen a shift in requirements toward solutions built on Cloud-native architecture,” says Kahl.

“Beyond operational cost effectiveness, the technology has expanded the possibilities of scaling calculations, buffered by continuous integration/deployment practices. This is invaluable for banks and hedge funds needing to perform routine but rigorous calculations where Cloud-scaling addresses time-sensitive reporting and submission requirements.

“We see both buy and sell side institutions migrating away from large on-premises systems towards a best-of-breed architecture, very often using Cloud-based services. For them, the need to scale is driven by both volume and more contemporary needs, like complex models and managing the large market data sets supporting the trade lifecycle, including pricing calibrations, model validation, and regulatory requirements.

“Market participants using Cloud-native technology also keep pace with changing conditions and regulations by having instant access to new features and models without needing on-premise upgrades to keep them operationally efficient.”

There is another reason why established players may want to up their technology game: competition. Many fixed income and derivative analytics applications are now more easily deployed and distributed, and Python-based programming has been adopted widely for desktop applications, largely due to its ease of use and because the Python community has produced a vast number of on-hand data analysis packages.

That means a new generation of quantitative developers can prototype, prove out, validate and code up and run applications in a desktop environment and deploy to Cloud infrastructure for unmatched scale, all with far greater ease than was ever possible before.

“In some respects, those that moved first with a technology switch are best positioned now to navigate today’s uncertain market and a potentially persistent fragile macro-economic environment,” says Kahl. “After all, previous crises tell us that where there is uncertainty, there tends to be mispricing and, with that, investment opportunities.”

He truly believes that staying ahead of the curve with easily deployable technology and best-of-breed analytics is the differentiating factor for market participants. And that is where FINCAD comes into play, by helping solve quantitative challenges with innovative simplicity. “Financial institutions face a significant challenge staying abreast of changing regulatory and market dynamics,” Kahl concludes. “Having access to a superior level of analytics and functionality puts today’s firms in an ideal position to adapt to inevitable market uncertainty.”