Quant of the year: Michael Pykhtin

The banking rulebook is becoming increasingly complex, so regulators need good quants to design and explain it - but they must also tackle the big questions of the crisis

In the wake of the financial crisis, regulators in the US and Europe decided the existing capital requirements regime was inadequate and needed to be overhauled. The industry’s models had failed to capture risks – most obviously credit and counterparty exposures – and some critics were calling for them to be reined in by blunter, simpler standards.

The Basel Committee on Banking Supervision only half agreed. Basel 2.5 and Basel III require capital to be held against new exposures, such as ratings transitions and the variability of credit valuation adjustment (CVA), and they allow models to be used. But the CVA charge in particular is prescriptive, and the new standardised approaches that have been introduced require a higher degree of sophistication.

Now, a new balance is being struck, requiring real technical expertise – lacking at many regulators – and it helps explain why Risk’s 2014 quant of the year, Michael Pykhtin, left his position as a senior quantitative financial analyst at Bank of America four years ago to join the Federal Reserve Board in Washington, DC, first as senior economist and now as manager of its quantitative risk management team.

Since then, he has been trying to learn the lessons of the crisis for capital requirements, and helping the industry understand where they come from. His supervisory role is mainly in bank exposure model validation, and he participates in Basel Committee working groups looking at new capital rules – for instance, being involved in ongoing revisions to the capital treatment for securitisations. In addition, he continues to pursue his own independent research.

Industry quants are careful to be polite about regulators when on the record, while often despairing of their technical abilities behind closed doors. But Pykhtin is well respected among the quant elite.

I think our current regulatory capital rules could be substantially simplified, but this requires having people like Michael

“Risk measurement is complex. Before one can design standardised capital rules, one has to understand just how complex so you can decide where and how to approximate. I think our current regulatory capital rules could be substantially simplified, but this requires having people like Michael. He’s a great asset for the Fed and for the Basel Committee – regulators need more quants like him. We in the industry see him as an important point person to discuss technical issues in regulation,” says Eduardo Cannabarro, global head of risk analytics at Morgan Stanley in New York. Another senior quant at a European bank says simply: “He’s probably the smartest guy the regulators have got.”

Pykhtin is not afraid of tackling the big questions. Perhaps the biggest is how to manage crises themselves – systemic risk. Few have tackled the subject, for the simple reason that it’s complicated. But in Exposure under systemic impact, Pykhtin and co-author Alexander Sokol, a consultant at CompatibL, took a first stab at the subject (Risk September 2013, page 87, www.risk.net/2291448, and pages 88–93, www.risk.net/2290019).

In particular, the article tackles exposures to big, interlinked entities. The Federal Reserve has flagged up the issue by creating a list of systemically important financial institutions, the default of which would jeopardise other firms. Pykhtin is careful not to conflate his so-called systemically important counterparties (SICs) with the official definition – apart from anything else, his includes sovereigns – but is focused on how to manage the particular kind of wrong-way risk involved in trading with SICs.

“Systemic risk is at the forefront of everyone’s mind but is notoriously difficult to quantify. Pykhtin’s clear and pragmatic approach goes a long way towards setting a rigorous framework to measure and control it,” says Vladimir Piterbarg, head of quantitative analytics at Barclays in London.

Wrong-way risk is about the dependence between exposure and default: if a counterparty is systemically important, news of its default will affect all the market risk factors, and so the exposures in a portfolio, through a jump process. Because they are rare, calibrating these effects is tricky, but data on market reaction to the defaults of Argentina and Russia in the 1990s, and the bankruptcy of Lehman Brothers in 2008, can be used to infer the effects of another crisis for sovereigns and financials, respectively.

“We realised that when the counterparty is systemically important, there is another kind of wrong-way risk that is completely different to the traditional kind. When SICs default, they have the potential to move entire markets very quickly, faster than their counterparties are able to close out their portfolios. This means the exposure you simulate traditionally is not the exposure you experience on their default,” says Pykhtin.

And it’s not just market levels that are affected, but risk-factor volatility, and that generates some sobering numbers. “It’s about how the market processes information about a default into increased volatility. Since exposure is roughly proportional to mark-to-market volatility, this effect can be significant. If volatility doubles on default, so does the exposure,” he says. In the aftermath of the Lehman Brothers default, the calibration shows the volatility of the North American CDX investment-grade credit default swap index jumped by a factor of 80 – meaning exposure numbers that neglected this risk would have been wild underestimates.

This is new work in a young field. There is no sign yet that it will be included in any future regulation, and Pykhtin does not know of a bank that has implemented the method. But as well as tackling the big conceptual questions, it is his work explaining the thinking behind regulation, and how to follow them consistently, which is so appreciated in the industry.

In two articles, Counterparty risk capital and CVA and Model foundations of the Basel III standardised CVA charge, Pykhtin brought the regulatory view of some of the most contentious parts of the new regime to a practitioner audience (Risk August 2011, pages 66–71, and Risk July 2012, pages 60–66).

The first was motivated by the desire to include CVA in the counterparty credit risk module in Basel III consistently. This can be fixed in two ways, either as market or credit risk. The former uses a basic value-at-risk calculation for CVA jointly with the trading book; the latter uses an asymptotic single risk factor (ASRF) approach in a banking-book style.

The second performed an even greater public service, by showing where the opaque standardised CVA charge came from. Many in the industry were perplexed by this formula, with seemingly arbitrary factors – there was an unexplained, odd-looking 2.33 in one formula, which became notorious – and a lack of obvious connection to the real world. Pykhtin peeled back the layers to show the assumptions and where they came from.

The simplifications in the model include a flat hazard-rate term structure, a linear approximation of how credit spreads drive CVA variation, uniform correlations and a first-order approximation of spread dynamics driven by a single Gaussian systematic factor. It turns out that 2.33 is the 99th percentile of the Gaussian distribution, to two decimal places, and came from a value-at-risk-style calculation.

“What I wanted was to bring CVA into the capital framework naturally, starting from fundamentals, and the two perspectives of market risk for sophisticated dealers and credit risk for more buy-and-hold banks. Then for the standardised capital charge, I knew how the formula was derived, but talking to people from the industry, I realised most didn’t have a clear idea. You can derive it as an analytic approximation to the exact definition, so I derived it more rigorously than in the Basel documents and show what simplifications are needed. I see this as an important part of my role – communicating these technical details,” he says.

This dialogue between industry and regulator is an increasingly valuable function as rules and guidelines get more technical. It’s familiar to Pykhtin – from both sides. At his first job in finance – nearly five years spent as a capital modelling quant at KeyCorp in Cleveland, Ohio – he was involved in the back-and-forth over models that presaged the Basel II regime. At the Fed, he was heavily involved in the development of the CVA charge and non-internal model method for counterparty credit risk capital, and he is a member of the Basel Committee’s risk measurement working group.

Since starting at Keycorp in 2000, he has built up a formidable set of publications on credit and counterparty risk, particularly developing analytical formulas for VAR in the multi-factor Merton model. He is clear that modelling is essential to good capital treatment, and argues quants can help create a more stable financial system.

“Quants can and should play an important role in regulation, as capturing risk in the financial system is getting increasingly complex. It’s important there are technical experts who can understand and communicate the ideas to industry,” he says.

Whether more quants will follow him into regulation is uncertain, but as the rules become more sophisticated, more technical experts will surely be needed. The Basel Committee’s fundamental review of the trading book – published as a second draft in October – is in some ways the most technical regulatory document yet, but the process has been interpreted by the industry as being anti-model. The Fed’s hiring of Pykhtin shows this need not be a one-way trend.