On August 22, 2018, the U.S. stock market broke the record for longest-ever bull market run after the S&P 500 went 3,453 days without a drop of 20 percent (a decline normally associated with a bear market). In fact, despite recent financial turbulence associated with monetary policy normalization and escalating trade tensions, the U.S. market has been relatively tranquil since August 2015. The Volatility Index (VIX), which is widely used to measure financial volatility, has been close to record lows, especially in late 2017 and early 2018 (Figure 1).
Figure 1: VIX Index (1990-2018)
Note: Shaded areas indicate U.S. recessions. Source: Chicago Board Options Exchange
Against this backdrop, the U.S. administration has pursued several deregulatory initiatives, such as a recent interagency proposal to ease the compliance burden of the Volcker Rule (a touchstone of the post-crisis Dodd-Frank reforms which imposed restrictions on bank proprietary trading). This measure is in line with the Volcker Rule relaxations contained in the broader deregulatory bill signed into law this past May titled “Economic Growth, Regulatory Reform, and Consumer Protection Act.” Given that we are just ten years away from the worst financial crisis since the Great Depression, the current deregulatory trend has sparked many discussions, most of which conclude that the measures being pursued are misguided and harmful to financial stability.
However, this issue may be more complex than it appears. In our paper (IMF Working Paper 18/123), Does Financial Tranquility Call for Stringent Regulation, we analyze this issue using a stylized portfolio choice model that features the interaction between learning and externality. We show that as financial market tranquility continues over time, it does not always mean financial regulation should be tightened, nor does it always mean regulation should be relaxed. Instead, the direction of optimal regulation depends on a careful assessment of a trade-off: how much more risk investors have taken due to the continued financial tranquility (a “risk-taking effect”), versus how much more resilient the financial system has become in the eyes of the regulators (a “resilience effect”). Relatedly, our paper also highlights the importance of assigning the macroprudential regulation function to independent agencies with technical expertise, rather than to relatively uninformed politicians.
Without Government Regulation: Learning and Externality
We first consider the case without any government regulation. In our model, investors have access to a safe asset and a risky asset that, in addition to its conventional risk properties, may expose the entire financial sector to the risk of a systemic crisis. An example of such a risky asset is the collateralized debt obligations that were widely traded in the lead-up to the global financial crisis. Investors do not know the extent to which the risky asset exposes the financial sector to a systemic crisis, but they can learn about it from the financial system’s history of performance. A longer period of financial tranquility (that is, the absence of a crisis) builds up investors’ confidence and leads them to take larger positions in the risky asset. However, a larger aggregate risky asset position raises the probability of a systemic crisis in the event that investor confidence is mistaken. Such a systemic crisis hurts all investors, but since each individual investor does not care about other investors’ welfare, they end up taking an excessive position in the risky asset that is not socially optimal. This is the source of the inefficiency.
In our first main result, we derive the necessary and sufficient condition under which investors’ learning aggravates the inefficiency, that is: as investors become more optimistic after observing a longer history of financial tranquility, they invest even more in the risky asset, which strengthens the negative externality and creates more inefficiency.
With Government Regulation: A Trade-Off
We then turn to the question of government regulation in the form of macroprudential policies. Using both a theoretical model and numerical simulations, we show that at a point in time, a macroprudential regulator can reestablish efficiency by means of a capital income tax set at an appropriate level. Our second main result is that, under the same necessary and sufficient condition derived in the first result, the optimal tax rate rises as the degree of investor confidence (and, hence, the degree of market inefficiency) rises.
The condition we derive captures precisely the trade-off mentioned earlier. On the one hand, a longer period of financial tranquility (as experienced by the U.S. over the last 10 years) induces investors to be more confident, which leads them to take more risks, that is, to “input” more risks into the system. This is what our paper refers to as a “risk-taking effect,” and this effect would normally call for more stringent regulation.
On the other hand, a longer period of financial tranquility may be a signal (both to the market and to regulators) that the financial system is more resilient, otherwise a systemic crisis would have happened already. That is, it may be a signal that the transmission mechanism between investor risk-taking and systemic crisis may not be as strong as previously perceived by regulators. This effect is what our paper refers to as a “resilience effect,” and this effect would normally call for looser regulation.
Therefore, it is unclear a priori whether regulations should be made more or less stringent over a period of prolonged financial tranquility. This implies that optimal macroprudential policies are not always countercyclical, in contrast to most of the recent literature. The direction of the optimal policy depends on the relative magnitudes of the two effects just described: the increase in investors’ risk-taking, and the improvement in the regulator’s perception of the resilience of the financial system. The condition derived in our paper captures this comparison. We deliberately keep our model very simple in order to focus on this point, which is obscured in other papers that feature more complex financial market models.
The above arguments highlight a key challenge faced by macroprudential/financial regulators: the optimal policy depends on the true resilience of the financial system in the regulator’s perception; however, such true resilience is difficult to precisely calculate. This opens the door to “inefficient deregulation” or “inefficient regulation.”
On the one hand, a regulator who overestimates the resilience of the system will tend to reduce the stringency of macroprudential regulation as financial tranquility persists, and this will induce a buildup of financial risk that the regulators wouldn’t tolerate if they knew the system’s true resilience. Some commentators and policymakers (such as Alan Greenspan) have argued that this occurred in the U.S. in the 1990s and 2000s.
Recently, the U.S. has rolled back some Obama-era financial regulations. In particular, on May 24, 2018, the U.S. President signed the Economic Growth, Regulatory Relief, and Consumer Protection Act into law. The law is designed to provide regulatory relief to regional and community banks and does so by, amongst other elements: easing rules for mortgage lending by small banks, exempting banks with less than $10bn in assets from the Volcker Rule, and raising the asset threshold (often referred to as the “SIFI” threshold) at which banks are subject to heightened supervision from $50 billion to $250 billion. Although our paper does not explicitly assess the plausibility of such initiatives, it does raise the possibility that they may be the result of the regulators’ overoptimistic view of the financial system’s resilience.
On the other hand, a regulator who underestimates the resilience of the financial system may repress financial activities needlessly. This could prevent the uptake of financial innovations that might actually be effective at delivering value to investors (for example, by diversifying risk more effectively). Thus, the regulatory challenge is especially stark for innovative financial industries. Consider, for example, the online finance industry in China. As reported by the Financial Times on March 20, 2017, China’s digital payments market has exploded to nearly fifty times the size of the U.S. market. An offshoot of Alibaba has harnessed some of these online flows to build Yu’e Bao into one of the world’s biggest money market funds. During such a rapidly-booming period for an innovative industry like this, should the government loosen regulation to further support its development, or should the government tighten regulation to contain the future risk? Our model provides a framework for thinking about this pressing question.
Note that all results so far are derived by assuming that the underlying true resilience of the system is fixed. However, we show that all our results hold if the underlying resilience is instead time-varying and follows a Markov process (a good state may become bad or a bad state may become good). The main difference from the fixed-state case is that now agents may never learn the true state.
Connecting Our Results with the Practice of Policymaking
The previous discussion on inefficient regulation/deregulation highlights the importance of accurately gauging the true resilience of the financial system. Clearly, it follows that it is welfare-enhancing to assign the financial regulation function to politically independent agencies with technical expertise that allows them to gauge the underlying true resilience of the financial system. This is consistent with the viewpoints of the IMF (2011 and 2014).
In practice, the independence of regulatory agencies can be ensured by law, as is the case for the Federal Reserve in the U.S. And regulatory agencies could gain additional knowledge about the true resilience of the system through various systemic risk assessment tools, including stress tests and indicators of balance sheet vulnerabilities.
Conclusion and Policy Implications
In this paper, we examine how to account for the cycles of optimism and pessimism in the design of financial regulation and macroprudential policies. We illustrate the excessive risk-taking in financial markets through the interaction of negative externality and learning, and provide a simple framework to assess the efficiency of macroprudential regulation.
Our key policy implication is that it is not always optimal for financial regulators or macroprudential policymakers to go for or against the wind based on cyclical indicators alone. Instead, it is necessary to closely examine the underlying structural change of the system. For example, suppose that high credit growth is mainly driven by non-oil industry financings in an economy that used to rely heavily on oil exports. By diversifying the economy’s structure, the credit growth may have fundamentally improved the resilience of the system to shocks. In this case, even though cyclical indicators suggest a sustained credit boom, tightened regulation may not be desirable. This approach is consistent with the evidence in Dell’Ariccia et al. (2012), who document that more than a third of credit booms are not followed by economic underperformance. The approach would be useful in analyzing the current financial deregulation initiatives in the U.S. and the regulation of the rapidly growing online finance industry in China.
Such an approach is important also because financial cycles are much longer than business cycles, currently about 15 years in the U.S. and the U.K. according to Strohsal et al. (2015); and about 15 years for an average G-7 country, according to Schüler et al. (2017). Hence, during the long periods of credit decline, it may not be desirable to just keep loosening macroprudential policies, otherwise the underlying structural problems that cause the credit decline in the first place may be overlooked. Similarly, during the long periods of credit expansion, it may not be desirable to just keep tightening macroprudential policies without gauging the change of the resilience of the system.
 Emails: firstname.lastname@example.org; email@example.com. We are extremely grateful for the helpful discussions with a long list of colleagues (see the paper for a complete list): Viral Acharya, Tobias Adrian, Martin Cihak, Udaibir Das, Daniel Garcia-Macia, Gaston Gelos, James Morsink, Maurice Obstfeld, Miguel Segoviano, Ennio Stacchetti, and especially Alexander Murray, Tao Zha, and Divya Kirti. The views expressed here are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
 This is a form of Pigouvian taxation and is similar to the systemic risk taxation proposed by Acharya et al. (2010). Our results also apply if we instead use financial transaction tax, or other types of macroprudential policies such as a restriction on the leverage ratio.
 The famous admission of Alan Greenspan in 2008 that he had “made a mistake in presuming that the self-interest of organizations, specifically banks, is such that they were best capable of protecting shareholders and equity in the firms” was an ex post acknowledgement that regulators had overestimated the resilience of the financial system in the runup to the crisis.
 Acharya, V., Pedersen, L., Philippon, T., Richardson, M.P., 2010. Taxing Systemic Risk. In Regulating Wall Street: The Dodd-Frank Act and the New Architecture of Global Finance, edited by Acharya, V.V., Cooley, T., Richardson, M.P., Walter, I.. John Wiley & Sons: Hoboken, N.J.
 International Monetary Fund Staff Discussion Note (prepared by E. W. Nier, J. Osinski, L. I. Jacome, and P. Madrid), 2011. Institutional Models for Macroprudential Policy.
 International Monetary Fund, 2014. Staff Guidance Note on Macroprudential Policy.
 Dell’Ariccia, G., Igan, D., Laeven, L., Tong, H., Bakker, B., Vandenbussche, J., 2012. Policies for Macrofinancial Stability: How to Deal with Credit Booms. IMF Staff Discussion Note.
 Schuler, Y.S., Hiebert, P.P., Peltonen, T.A., 2017. Coherent Financial Cycles for G-7 Countries: Why Extending Credit Can Be An Asset. European Systemic Risk Board Working Paper Series No. 43.
 Strohsal, T., Proano, C.R., Wolters, J., 2015. Characterizing the Financial Cycle: Evidence from A Frequency Domain Analysis. Discussion Papers 22/2015, Deutsche Bundesbank.