If regulations don’t bend, they’ll break

Financial regulation should be adaptive, not reactive, argues Andrew Lo

The tenth anniversary of the disastrous weekend that nearly brought down the global financial system is fast approaching. But in many of the jurisdictions that were central to the crisis, financial regulations introduced in the aftermath, aimed at preventing a repeat, are now being rolled back. The pendulum of regulation is now swinging back towards fewer and looser restrictions – and if the past is any guide, a ramp-up in systemic risk exposures will be the result.

Rather than simply following this pendulum, a better approach would be to craft financial regulation that adapts to these cycles – but how can such a goal be achieved?

Memories fade, and many forget how desperate things were during the crisis, so it’s a good time to reflect on those events. Our ignorance was deep. Back then, mortgage-backed securities, collateralised debt obligations and credit default swaps were obscurities to most of the economics profession, let alone those outside it. Suddenly, in the summer of 2007, hedge funds were being gated, the US housing market was in freefall, and the news was filled with updates about debt tranches, toxic assets and Ted spreads. From their initial responses, it quickly became apparent that policymakers were also at a loss, and improvising as they went.

The regulatory and market environments have shifted a long way since then. Unemployment is down, inflation is under control, and markets have stabilised to a prolonged period of low volatility, and record highs for equities. In fact, experts now worry that volatility is too low – so much so, that a brief spike of the Vix above 35 caused a sensation in February.

In this seemingly salutary economic environment, the attempt to dismantle many of the oversight provisions of the Dodd-Frank Act of 2010 – the centrepiece of the US’s response to the crisis – has begun. Most recently, the Volcker Rule, which restricts the ability of commercial banks to engage in proprietary trading, has been streamlined and softened, even though certain legacy funds have conformed to the rule only since last year (Bain and Schmidt, 2018).

The leadership of key regulatory bodies is also shifting under the new US administration. Multiple news stories have described the administrative confusion and neglect at the Dodd-Frank-mandated Consumer Financial Protection Bureau by its acting director, Mick Mulvaney, who, before his appointment, stated the bureau was “extraordinarily frightening” and a “sick, sad” joke (Stewart, 2017).

These swings of the regulatory pendulum are not new. After all, the ultimate driver of this pendulum is human behaviour. Once we become accustomed to the new status quo, we forget, and eventually go back to our old ways – only to repeat our earlier mistakes

These swings of the regulatory pendulum are not new (see, for example, Blinder, 2015). After all, the ultimate driver of this pendulum is human behaviour. It’s a common story: we feel frightened and contrite after a crisis, and throw the book at what we perceive to be the causes of our problems. But once we become accustomed to the new status quo, we forget, and eventually go back to our old ways – only to repeat our earlier mistakes.

We need to keep systemic risk below a certain threshold, yet we don’t measure that risk in any organised fashion – and you can’t manage what you don’t measure. As a result, swings in financial stability lead to regulatory responses that bounce between two extremes, but those responses are almost always slightly out of phase. Given the pendulum’s current trajectory, and the fact that the pace of innovation is increasing, we’re at risk of rekindling the flames of financial crisis.

How can we break out of this vicious cycle? One answer lies in adaptive regulation. Systemic risk is a dynamic quantity, and we need equally dynamic rules that can adapt sufficiently rapidly to changing financial conditions, like those in the years leading up to 2008. Instead of viewing financial markets as a static machine that we can fine-tune, we should think of them as a complex ecosystem, populated by different species of stakeholders, each with its own motivations, needs and constraints (Levin and Lo, 2015).

Among the adaptive tools currently available to the regulator are margin requirements and other types of leverage constraints, which help manage expectations and prevent bad surprises among investors least likely to be able to deal with them (Brennan and Lo, 2014).

However, these constraints don’t move fast enough in response to shifts in market volatility, especially those subject to human judgement. If leverage constraints change more slowly than changes in market risk, then the probability of loss will vary, as well – requiring investors, regulators and other stakeholders to deal with these uncertain variations in some manner.

Now imagine a scenario in which regulators can monitor even the slightest changes in systemic risk, and then changing aggregate leverage in the entire financial ecosystem, so as to keep the probability of financial instability relatively constant and predictable, allowing businesses to manage their risk more effectively. How do we accomplish this system-wide risk management goal? The answer surely lies in making better use of the massive amounts of data disseminated by large financial institutions.

This could include employing large-scale machine-learning algorithms to predict changes in risk exposures, aggregating these predictions to identify the most significant threats to financial stability, and then implementing stabilisation policies – including changes in capital requirements, margin levels, collateral and other leverage constraints – to dynamically adjust to the probability of a systemic shock. Some regulators, including the Bank of England, are already exploring such a path.

This process is in some ways akin to what broker-dealers routinely do to monitor risks internally, but on a more massive system-wide scale.

Of course, the challenges to adaptive regulation at the macroprudential level are significantly more complex than those faced by any single exchange or counterparty. It’s very hard to see the sources of systemic risk from within the system itself. But as these risks are measured, analysed and better understood, we’ll be able to devise regulation that knows when best to “take the punchbowl away” from the market, and when to let it have another cup.

Andrew W Lo is: Charles E and Susan T Harris Professor at the MIT Sloan School of Management; director, MIT Laboratory for Financial Engineering; principal investigator, MIT Computer Science and Artificial Intelligence Laboratory; external professor, Santa Fe Institute.

The views and opinions expressed in this article are those of the author only, and do not necessarily represent the views and opinions of any institution or agency, or any of their affiliates or employees.

Editing by Mauro Cesa and Tom Osborn

References:

• B Bain and R Schmidt (2018)
  ‘Wall Street Gets Win as Fed Set to Ease Volcker Trade Limits’
  Bloomberg
• AS Blinder (2015)
  ‘Financial Entropy and the Optimality of Over-regulation’
  The New International Financial System: Analyzing the Cumulative Impact of Regulatory Reform edited by DD Evanoff et al
  World Scientific Studies in International Economics 48
• TJ Brennan and AW Lo (2014)
  ‘Dynamic Loss Probabilities and Implications for Financial Regulation’
  Yale Journal on Regulation 31, 667–694
• SA Levin and AW Lo (2015)
  ‘Opinion: A new approach to financial regulation’
  Proceedings of the National Academy of Sciences 112, 12543–12544
• E Stewart (2017)
  ‘Mick Mulvaney once called the CFPB a “sick, sad” joke. Now he might be in charge of it’
  Vox.com
• RR Wing and S Phelan (2005)
  ‘Long-term weight loss maintenance’
  The American Journal of Clinical Nutrition 82, 222S–225S

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• Topics
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