Tag Archives: Risk

Tackling the challenges of governments as financial institutions

From the MIT Sloan Newsroom:

Governments not only regulate the private financial marketplace, they also own and operate some of the world’s largest financial institutions. Government agencies make trillions of dollars of loans, insure large and complex risks, and design new financial products. Yet their leaders often lack the analytical support and rigorous financial training of their peers in the private sector, and transparency is often lacking.

 

The MIT Center for Finance and Policy officially launched this month to address those gaps, along with other challenges facing financial policy makers.

“This is a big unmet need,” said Professor Deborah Lucas, director of the center. “To have an academic center devoted to the broad swath of government financial policies that have such an enormous effect on the allocation of capital and risk in the world economy.”

“What we want to do is to promote research that policymakers, practitioners, and informed citizens can turn to as an objective source of information when they’re thinking about these policy issues. That information often isn’t available now,” she said.

Research endeavors so far include, among others: the production of a world atlas of government financial institutions, an effort helmed by Lucas to catalog, compare, and evaluate governments’ financial involvement worldwide; a project led by Professor Andrew Lo to develop a dashboard that measures systemic financial risk; a study of the effects of algorithmic and high frequency trading, led by Professor Andrei Kirilenko; and a study of policies on retirement finance led by MIT Sloan professor and Nobel laureate Robert Merton.

Lo, Kirilenko, and Merton are all co-directors of the center.

Along with the research work, Lucas said there is also an educational mission for the center. In many cases, the center’s leaders say, financial problems could have been avoided, mitigated, or at least predicted had public sector workers had an education on par with that received by many private sector finance professionals.

“The idea is to provide the people who are working on finance within a government context with the same skillset as their peers in private industry,” Lucas said. “One reason you see a lack of finance education is because it’s tended to be a rather expensive education. And people going into the public sector may not even realize that finance is what they will need to know.”

At MIT Sloan, work at the center has already led to the creation of Kirilenko’s new course—Core Values, Regulation, and Compliance—as well as a student club on financial markets and policy.

The center began sponsoring events in October 2013, but officially launched Sept. 12-13 with the inaugural MIT Center for Finance and Policy Conference in Cambridge, Mass. More than 120 people attended the invite-only event, which featured discussions on the cost of government credit support, the costs of single-family mortgage insurance, and contagion in financial markets. Peter Fisher, senior director at BlackRock Investment Institute and a former undersecretary at the U.S. Department of the Treasury, gave a keynote talk.

The conference also included a panel discussion on improving government financial institutions, which addressed the need for government agencies to improve how they manage credit portfolios and monitor program risk levels over time. Panel members also discussed ways to raise red flags when there are problems in government credit programs.

The outlook was not entirely dire. “The move toward embracing risk management concepts across the federal government has been impressive in recent years,” said Doug Criscitello, a managing director at Chicago-based audit, tax, and advisory firm Grant Thornton and the former CFO of the U.S. Department of Housing and Urban Development. “We’ve seen the rise of independent risk management offices … that are housed outside the credit extension department.”

Lucas said she believes MIT’s depth in finance, economics, policy, and systems thinking make it the ideal place to study governments as the world’s largest and most complex financial institutions.

“I think an important reason that more academics haven’t taken on these issues—despite their importance—is that they are extremely complex,” she said. “Making progress takes a big investment in understanding institutions and laws and motivations. The problems are inherently interdisciplinary. And MIT is this great institution in terms of having the horsepower and energy to go after it and say ‘We can hit this question from a lot of different dimensions.’”

A New Measure of Financial Intermediary Constraints

Hui Chen teaches 15.433 Investments at MIT Sloan

The ability to measure the degree of financial intermediary constraint is of crucial importance for regulators and investors. Standard measures of intermediary constraint often focus directly on the riskiness of the financial institutions themselves, such as their level of credit risk, volatility, or leverage ratio. One such example is the TED spread, which is the difference between LIBOR, a short-term interest rate for uncollateralized interbank loans, and the yield for Treasury bills. Large TED spreads indicate that banks, and hence uncollateralized interbank lending, are risky, which is the case during the financial crisis of 2008-09 (see in the picture below). However, safe banks are not the same as unconstrained banks. In fact, a period of low TED spreads could be precisely because of the tight constraints that limit the banks’ ability to take on risky investment.

In a recent research paper with Professor Scott Joslin at the University of Southern California and Sophie Ni at the Hong Kong University of Science and Technology, we propose a different approach to measure the tightness of intermediary constraint by observing how financial intermediaries manage their tail risk exposures. Specifically, we focus on financial intermediaries’ trading activities in the market of deep out-of-the-money put options on the S&P 500 index.

Our measure is motivated by theory. I will sketch out the main ingredients of the model here. We consider a general equilibrium setting where two types of agents, public investors and financial intermediaries, have different beliefs about the chances of major market crashes. Such disagreements lead to trading in the crash insurance market, with the more optimistic financial intermediaries selling crash insurance to the public investors during normal times. The assumption of financial intermediaries being more optimistic about tail risks is a shortcut to capture their superior ability to manage tail risks, or agency problems such as government guarantees and compensation schemes that encourage financial institutions to take on tail risks.

As in practice, financial intermediaries in our model face risk constraints, which limit the amount of tail risks they can take on relative to their net worth. If the intermediary risk constraint starts to tighten, which could be due to higher levels of tail risks in the economy or trading losses that reduce the net worth, the financial intermediaries will effectively become more risk averse, and their ability to provide risk sharing with the public investors will be reduced. As a result, the amount of crash insurance sold by the financial intermediaries becomes lower and can even turn negative (as the financial intermediaries become net buyers of crash insurance). In addition, the tighter constraint and reduced risk sharing will also make crash insurance more expensive and lead public investors to demand a higher risk premium for the aggregate stock market.

The deep out-of-the-money put options on the S&P 500 index (DOTM SPX puts) are effectively insurances against major market crashes and are well suited to test our theory. The figure below plots the net amount of DOTM SPX puts that public investors acquire each month (henceforth referred to as PNBO). This also reflects the net amount of the same options that broker-dealers and market-makers (financial intermediaries) sell in that month. The net public purchase of DOTM index puts was positive for the majority of the months prior to the recent financial crisis in 2008, suggesting that the financial intermediaries (public investors) were mainly net sellers (buyers) of crash insurance. A few exceptions include the Asian financial crisis (December 1997), the Russian default, the financial crisis in Latin America (November 1998 to January 1999), and the Iraq War (April 2003). However, starting in 2007, PNBO became significantly more volatile. It turned negative during the quant crisis in August 2007, and then rose significantly and peaked in October 2008, following the bankruptcy of Lehman Brothers. Afterwards, PNBO plunged rapidly and turned significantly negative in the following months. Following a series of government actions, PNBO first bottomed in April 2009, rebounded briefly, and then dropped again in December 2009 as the European sovereign debt crisis escalated.We find that PNBO is negatively related to the expensiveness of the DOTM SPX puts relative to the at-the-money options. This result is the opposite of the prediction of the demand pressure theory for option pricing, whereby exogenous demand shocks push up both the amount of options public investors buy and the price of the options, thus inducing a positive correlation between the public demand for an option and its expensiveness. Instead, the result is consistent with time variation in the tightness of intermediary constraints driving the pricing of the options and the endogenous public demand simultaneously.

Moreover, we find that PNBO significantly predicts future market excess returns. During the period from 1991 to 2012, a one-standard deviation decrease in PNBO is associated with a 3.4% increase in the subsequent 3-month market excess return. The R-square of the return-forecasting regression is 17.4%. The return predictability of PNBO is, again, consistent with the prediction of a tightened intermediary constraint driving up aggregate market risk premium.

Two alternative explanations of our predictability results are: (1) PNBO is merely a proxy for standard macro or financial factors that drive the aggregate risk premium; (2) the predictability captures the direct impact of intermediaries’ constraints on the aggregate risk premium. Consistent with the second view, we find that the predictive power of PNBO is unaffected by the inclusion of a long list of return predictors in the literature, including price-earnings ratio, dividend yield, net payout yield, consumption-wealth ratio, variance risk premium, default spread, term spread, and various tail risk measures.

In addition, we also compare PNBO with a list of funding constraint measures (including the VIX index, the growth rate of broker-dealer leverage, and liquidity measures in the Treasury market). We find that financial intermediaries tend to reduce their supply of market crash insurances when these measures of funding constraints tighten. When regressing market excess returns on lagged PNBO and other funding constraint measures jointly, only the coefficient on PNBO remains significant, which suggests that PNBO contains unique information about the aggregate risk premium relative to the other measures of funding constraints.

Our study sheds new light on a specific channel, the crash insurance market, through which intermediary constraints affect aggregate risk sharing and asset prices. Our measure of financial intermediary constraint has several advantages compared to the existing measures. Compared to the accounting-based measures such as the leverage for financial institutions, our measure has the advantage of being forward-looking and available at higher (daily) frequency. Unlike price-based measures such as the TED spread, our measure moves the focus away from whether the financial institutions themselves are risky and instead onto whether they behave as constrained in the financial markets.

For details of this research, see Hui Chen, Scott Joslin, and Sophie Ni, “Demand for Crash Insurance, Intermediary Constraints, and Stock Return Predictability.”