Financial Innovation and Risk Management
“We allow our standards of living to be determined essentially by a game of chance.”
Robert Shiller, Macro Markets, 1993.
In 2013, Robert Shiller shared the Nobel Prize for Economics with Eugene Fama and Lars Peter Hansen for their research on asset pricing. While Shiller is known as a critic of the efficient markets hypothesis and as a proponent of behavioral finance, less appreciated is his work on advancing financial technology to help societies manage fundamental economic risks.
At a time when the recent crisis has given financial innovation a bad name, Shiller’s contrarian message is that well-designed financial instruments and markets are an enormous boon to social welfare. We agree.
Historical examples supporting Shiller’s view abound. One classic illustration is the advent of inflation-indexed bonds that allow investors to insure themselves against inflation risk. The first such bond was issued by Massachusetts in 1780 during the Revolutionary War. Subsequently, more than two centuries passed before the U.S. government issued the first “Treasury Inflation Protected Security” (TIPS) in 1997 (other countries – including Australia, Canada, Mexico, New Zealand, Sweden, and the United Kingdom – had already begun to do so after 1980). Today, there are more than $1 trillion of TIPS outstanding, representing 8.5% of marketable federal debt held by the public. In addition to helping households and firms hedge against U.S. inflation risk, TIPS also provide a measure of long-term inflation expectations that Federal Reserve policymakers can use to help keep inflation low and stable.
As the long, but interrupted history of inflation-indexed bonds highlights, financial innovations come and go. Some innovations that appear to offer a useful means for risk sharing fail to be economically viable, only to be revived at a later date. Others, like commodity futures (which make it possible for farmers and others to hedge their production and income risks) have proven robust across centuries. (The first organized futures exchange – for rice – was established by 1730 in Osaka, well ahead of the 19th century exchanges in Chicago, Frankfurt, London, and New York.)
Today, there remain many fundamental economic risks that are difficult, if not impossible, for households to hedge. A large group of people facing uncorrelated risks would seem to be perfect candidates for an insurance pool. However, as we discussed in an earlier post, there is inadequate sharing of income-related risks: despite the high and rising level of idiosyncratic income risk at the household level, mechanisms to insure those risks (such as livelihood insurance) are not readily available.
Another example regards mechanisms to manage exposure to systematic income fluctuations at the aggregate level: Kamstra and Shiller have proposed that governments issue “trills” – a perpetual security with an annual coupon that pays one-trillionth of nominal GDP. For example, in the United States, that coupon would have been set at $17.60 as of the third quarter of 2014. In theory, when income growth is strong, government can afford higher coupon payments, while lower coupons would cushion the fiscal balance when the economy falters. At the same time, owning a trill would allow pension managers both to protect their clients against inflation and to benefit from the economy’s long-term growth – including the gains in both wages and profits. And, if many governments were to issue trills – with coupons related to their national GDP – savers could easily form a globally diversified investment portfolio, providing a cheap hedge against the idiosyncratic income risks of their domestic economy. So far, however, no government has issued a trill.
Housing ownership seems to be a particularly promising opportunity for improved risk sharing. According to the recently released Survey of Consumer Finance for 2013, the typical U.S. household in the middle of the income distribution owns its primary residence (see chart). Moreover, their home is their largest single asset, while more than one third of the middle-quintile income group owns their homes unencumbered. For those with mortgages, the median net worth in their home is equivalent to two thirds of the median household’s overall net worth. The bottom line is that the typical U.S. household remains under-diversified in terms of real estate risk.
Median Household Assets (Based on Middle Income Quintile)
Source: Survey of Consumer Finance (2013) and authors’ calculations.
Moreover, household risk emanating from fluctuations in the value of real estate rose substantially over the past decade. Indeed, at the low end of the wealth distribution, households had their net worth virtually wiped out by the recent collapse in house prices.
To the extent that these developments reflect increased systematic risk at the nationwide level, they would not present an opportunity for risk pooling (at least not inside U.S. borders). The question is whether idiosyncratic risk in housing has risen as well. The answer appears to be yes. The next chart shows the evolution since 1991 of the cross-sectional standard deviation of monthly percent changes of the individual sub-indexes that make up the 20-city Case-Shiller house price index. At each time t, the figure shows the dispersion of the monthly percent changes for the available city sub-indexes. On average, that standard deviation has risen to an estimated 0.70% since 2001, up markedly from the prior decade.
Case-Shiller House Prices for 20 Cities: Cross-sectional Standard Deviation of Monthly Percent Changes
Sources: S&P Dow Jones Indices LLC, FRED, and authors’ calculations.
Some of this increased dispersion is likely associated with a rise in nationwide (undiversifiable) housing price risk. Extracting the idiosyncratic component from this increased aggregate volatility is a complicated research exercise. But one back-of-the-envelope approach (described for those with interest in the technical note below) suggests that perhaps half of the increase is idiosyncratic.
So, where does this leave us? If confirmed, the evidence of increased idiosyncratic risk implies that the benefits from sharing housing equity risk have increased notably. Yet, we still do not see mortgage lenders offering options for down-payment insurance (as there would be, say, in a “shared responsibility mortgage”).
Naturally, there can be a wide range of obstacles to risk pooling. As with most kinds of insurance, these have to do with incentives, complexity and cost. Overcoming these challenges is the key to creating new, useful financial instruments and markets. Which institutions and innovations that promote risk sharing also are economically viable? How can the incentive problems associated with risk sharing be managed? And, how can people learn about and access new mechanisms to hedge their risks?
There also are numerous pitfalls. Financial institutions and markets can and do fail, sometimes dramatically. Effective regulation is needed to keep the financial system safe. And, individuals may find it difficult to get the information they need to make financial decisions that are truly in their best interest.
But, as Shiller has argued for decades, used properly, finance is a powerful means to improve people’s lives and promote a better society.
Technical note: The rough-and-ready extraction procedure starts by measuring the cross-sectional dispersion at time t of the residuals from regressing the percentage changes of each of the available city-by-city house price sub-indexes on the 20-city Case-Shiller index. These regressions are analogous to estimating the alphas and betas for individual stocks and using the residuals to examine time-varying idiosyncratic volatility in the stock market. One can then look at the pairwise correlations among the residuals. The finding that these pairwise correlations average close to zero is consistent with interpreting the dispersion of the residuals as a gauge of idiosyncratic risk. The post-2001 increase of this measure is roughly half the increase of the post-2001 average in the cross-sectional standard deviation chart above.