2020 was always going to be a challenge for quants with the upcoming discontinuation of IBOR, but the Covid-19 pandemic has upset the status quo, and brought forward more challenges. Based on hundreds of research calls with quants, QuantMinds editor-in-chief Vincent Beard now summarises the five key conundrums on quants’ minds this year.
Covid-19 and the economy
It is no surprise that since the start of the year, quant roles (as well as many others) have primarily been focused on Covid-19, specifically what the fallout from the virus will be on day-to-day roles. The word “unprecedented” has been used at an unprecedented level in the last 6 months and it is our responsibility as quants to try and help solve these challenges, particularly in these trying times to get a better understanding of the current predicament as well as the short- and long-term challenges of recovery.
While we can only speculate, some macroeconomic and consumer trends are already emerging.
“I think this is now the new normal”, Svetlana Borovkova, Associate Professor Of Quantitative Finance, Vrije Universiteit Amsterdam, told us in a recent webinar. “[Consumer] sentiment is negative or almost negative, which is something we have never seen before, even during the crisis of 2007-2008.”
Modelling the unmodellable
One of the biggest questions that has arisen from the pandemic is the realisation that historical data only works when current events can be contextualised through the eyes of past events. But what happens when current challenges are completely unrelatable and unique?
Sure, we have had recessions in the past with the 2008 financial crisis being particularly bad. In modern times, however, we have never had a crisis such as this, where every country in the world grinds to an almost complete halt and every sector is hit. Supply chains dismantle overnight and tens of millions of jobs disappear in the blink of an eye.
Quant teams and risk managers who are able to tackle these issues and start looking at model risk with a fresh lens and building models from scratch will be some of the biggest winners in the post Covid-19 world.
“It is important to leverage events like the Covid-19 pandemic and previous financial crises to learn about the general rather than the specific”, Maurizio Garro, Senior Lead IBOR Transition Programme, Lloyds Bank, wrote. “I would consider the following important points to improve the predictability of our models: intense and thorough data scrutiny, identification of the key risk factors (proxies of black swan events), robust and flexible calibration and continuous monitoring of models’ performance and limitations.”
IBOR reform
Many regulations either are being postponed or deferred due to the current crisis, but arguably the biggest fundamental change this generation of banking has seen is still going ahead – the reform and discontinuation of IBOR after 2021. With the deadline approaching and liquidity in the market having dried up, it is even more imperative for quants to work together with their organisations and plan accordingly, factoring in many challenges including outstanding modelling questions, considering conduct and reputational risk, and finding the most seamless and least disruptive solution.
Backward-looking rates are the favourite among regulators and market participants as a replacement for LIBOR. Leading the research in completing the model and applications, Fabio Mercurio, Global Head of Quant Analytics, Bloomberg L.P., presented his recent findings during the QuantMinds Digital Week:
“The forward market model accommodates both the traditional forward-looking (LIBOR-like) rates and the new setting-in-arrears backward-looking rates, which are expected to replace LIBORs in derivative contracts.”
This presentation is available on demand in our latest eMagazine.
Machine learning
A topic that has been on the tip of every quant’s tongue in the last few years. Covid-19 has brought up interesting questions and has people wondering how these same machine leaning strategies talked about in the past have fared up compared to non-machine learning techniques during the crisis.
Alongside this, with digitisation being at the forefront of most financial institutions’ future, further questions will continue to be asked around topics such as explainability and interpretability.
“The next big thing in quantitative finance”, Polina Baranova, Quantitative Research Associate, JP Morgan, wrote last year, “is indeed the development of explainable artificial intelligence (XAI) tools, to enable a broader adoption of AI. XAI focuses not only on building model interpretations, but also on developing more interpretable models without sacrificing accuracy, aiming to solve a long-standing trade-off between interpretability and precision.”
Finding your edge
Portfolio management theory, active vs passive management, data driven trading & investing, and factor investing. With high levels of volatility in the markets, now more than ever, quants are looking at ways to extract alpha through quantitative finance. Questions around differentiation are important, and new techniques will need to be discovered in order to ensure profitability and longevity for investors.
“We all know that in the markets, there is no such thing as a free lunch”, Jessica James, Managing Director, Senior Quant Researcher, Commerzbank, wrote earlier this year. “Risk-free returns don't exist – and if they did, even briefly, they would be traded on until they disappeared. Except sometimes, somehow, under just the right circumstances, the elusive free lunch may temporarily appear on the table.”
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