Category: General Finance

Bridging the knowledge gap on governments as financial institutions

Ask most finance experts about the “world’s largest financial institutions,” and you’ll hear names like Citigroup, ICBC (China’s largest bank) and HSBC. However, governments top the list of large financial institutions, with investment and insurance operations that dwarf those of any private enterprise. For instance, last year the U.S. federal government made almost all student loans and backed over 97% of newly originated mortgages. Add to that Uncle Sam’s lending activities for agriculture, small business, energy and trade, plus its provision of insurance for private pensions and deposits, and you’ll discover it’s an $18-trillion financial institution. By comparison, JP Morgan Chase, the largest U.S. bank, had assets totaling about $2.4 trillion.

While government practices differ across countries, the basic story is much the same everywhere. As the world’s largest and most interconnected financial institutions — and through their activities as rule-makers and regulators — governments have an enormous influence on the allocation of capital and risk in society. And as financial actors they are confronted with the same critical issues as their private-sector peers: How should a government assess its cost of capital? How should its financial activities be accounted for? What are the systemic and macroeconomic effects? Are the institutions well-managed? Are its financial products well-designed?

Surprisingly, little research has focused on governments as financial institutions in their own right, and on finding solutions to the challenges they face. While private-sector financial theory and practice have benefited from decades of transformational innovations, public-sector financial thinking and education have not kept pace.

To help to bridge this knowledge gap, MIT is launching the Center for Finance and Policy (CFP) under the aegis of MIT Sloan’s Finance Group. A primary goal of the CFP is to be a catalyst for innovative, cross-disciplinary, and non-partisan research and educational initiatives. Its activities will address the unique challenges facing governments in their role as financial institutions, and also as regulators of private financial institutions. The aim is to provide much-needed support for policymakers and practitioners that will ultimately lead to improved decision-making, greater transparency, and better financial policies.

A quick look at recent headlines shows just how much is at stake and some of the significant decisions that need to be made. There’s the announcement by the BRICs about the formation of the New Development Bank, which will serve as a channel for large government-backed investments in those countries. In the U.S. there’s been heated debate over whether the Export-Import Bank should be reauthorized and whether the federal student loan programs are adequately serving students. There’s also the question of if, how and when the U.S. mortgage market will be reprivatized.

Research supported by the CFP is organized around three main tracks: the evaluation and management of government financial institutions, the regulation of financial markets and institutions, and the measurement and control of systemic risk. A critical focus of the CFP is the dissemination of knowledge to turn theory and data analysis into practice. That will be accomplished through policy briefs, conferences, the website, a visiting scholars program, and other initiatives.

We’re also focusing on education. The aim is to provide greater access to the tools of modern financial analysis to current and future regulators, policymakers and other stakeholders in the public sector. People working in the public sector have traditionally faced barriers to obtaining high-level financial education due to the cost and lack of a developed curriculum. We’re planning to use MIT edX to develop and offer material that will reach a broad audience free of charge. We also plan to offer special executive education programs and short courses at Sloan. To support those efforts, the CFP is investing in curriculum development in the application of financial concepts to public policy contexts.

The CFP will be officially announced in conjunction with our inaugural conference, which will take place Sept. 12-13. The conference will highlight new research related to its three main research areas. It features six paper sessions, three panel discussions, and a keynote address. Over 100 participants are expected to attend including policymakers, practitioners, and academics. The event will be available afterwards online.

I believe that the CFP’s research and educational initiatives will significantly move the needle on how policymakers think about their role as financial decision-makers and regulators, and ultimately have transformative effects on the quality and conduct of financial policy.

Deborah Lucas is the Sloan Distinguished Professor of Finance at MIT Sloan and Director of the MIT Center for Finance and Policy

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.”

Early Peek Advantage?

From 2007 to June 2013, a small group of fee-paying, high-speed traders received the bi-monthly results of the Michigan Index of Consumer Sentiment (ICS) from Thomson Reuters at 9:54:58, two seconds before the broader release at 9:55:00. This arrangement was initially reported on April 5, 2013 by Financial Times regarding a complaint by a former Thomson Reuters employee for his dismissal after telling US federal agent about this arrangement of tiered distribution of information. Wider media coverage followed in June and July as well as a review by the office of New York Attorney General (see, for example, “Thomson Reuters Gives Elite Traders Early Advantage” by CNBC and “Traders Pay for an Early Peek at Key Data” by Wall Street Journal on June 12, “Seconds Out” by Economists on July 13, 2013). In July 2013, Thomson Reuters decided to suspend the program.

Despite the negative “optics” projected by this practice of tiered information release, a series of questions were raised: To what extent does it give an advantage to those with early information? Does it hurt general investors and hence damage the integrity of the financial market? How does it affect the efficiency of the price discovery process in the market? More careful analysis is needed in order to answer these questions.

In a recent research paper with Professor Xing Hu at University of Hong Kong and Professor Jun Pan at MIT, we have examined in detail the price dynamics and trading activity in E-mini S&P 500 futures around ICS releases during this episode. We focus on S&P 500 futures because ICS, reflecting consumer opinions of the overall economy, is likely to move the entire market instead of individual stocks. Being highly liquid and unaffected by short-sale constraints, E-mini S&P 500 futures is an ideal instrument to trade on both positive and negative market-wide information.

During the period when Thomson Reuters offers early peek advantage, we find abnormally high trading activity in E-mini S&P 500 futures at 9:54:58 on ICS announcement days. On average, the trading volume jumps to 1,473 contracts per second at 9:54:58, well above the sample average of 124 contracts per second, which reflects the trading volume of general investors. One second later at 9:54:59, the abnormal volume drops to 261 contracts, still well above the sample average but sharply down from the trading volume at 9:54:58. The volume pattern makes two points: First, early peek by high-frequency traders does generate high volume of trading, but mostly among themselves. (There is no reason be believe that general investors will choose to trade more at the time when they have an information disadvantage.) Second, the first second at 9:54:58 is disproportionally more meaningful to them.

More detailed study of price dynamics after the early peek reveals a clearer picture: We find that the prices are fully adjusted to the ICS news after the first 10% of the trades during 9:54:58, which lasts about 14 to 16 milliseconds. There is no evidence of further price drift after the initial price discovery. This implies that most of the transactions during 9:54:58 and all the transactions afterwards, including the public announcement at 9:55:00, are traded at the fully adjusted market prices. The scope of the early peek advantage is therefore narrowly contained and limited to high-speed traders trading amongst themselves. Outside of this narrow time window, general investors, as well as high-speed traders, trade at fully adjusted prices and are not disadvantaged by the early peek of a few.

The initiation and later suspension of the early peek program by Thomson Reuters also provides a natural experiment for us to examine how different mechanisms of information release might impact the speed of price discovery. Associated with the early peek program is highly concentrated trading amongst those fee-paying, high-speed traders over a span of two seconds. As a result of this intense and coordinated trading, we see a superfast price discovery in the order of 14 to 16 milliseconds. After the suspension of the early peek program, however, we do not see the same level of trading intensity and we find that the price discovery takes much longer. From this perspective, one might argue that, as a mechanism of information release, the tiered program provides a venue to facilitate concentrated and coordinated trading among informed high-speed traders and therefore makes price discovery more efficient.

How information actually transmits and impounds into market prices remain a central question in our understanding of how the financial market functions. Empirical investigations aimed at tackling this question are always hindered by the fact that most information is private in nature and hence unobservable to researchers, even ex post. The multi-tiered process adapted by data vendors in feeding market-moving information to their different clients, as in the case of Thomson Reuters when releasing CSI data, offers a rare instance where we know precisely what information is transmitted, when and to what subset of market participants. This situation allows us to examine with more clarity how information, private to some traders, drives their trading behavior and influences the market. It may also help us to better design and regulate the information dissemination process in the market.

For details of this research, see Grace Xing Hu, Jun Pan and Jiang Wang, “Early Peek Advantage?”