Tag Archives: Risk

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.”