I cant resist writing for this year economics prizes Bob, Gene , Hans. I have read Shiller most and one of the reason to learn finance , Fama is found in text books too often and Hansen is a pioneer in technical economics ( econometrics). Well , I would be talking mostly of Bob Shiller then Gene but Hansen work is too technical for me to talk about as his model for volatility is praised by pioneers in Econometrics but the truth is that i am too young to seriously talk about their innovative work .
Bob did (with Grossman and Melino) some of the best and earliest work on the consumption model, and his work on real estate and innovative markets is justly famous. But, space is limited so again I'll just focus on volatility and predictability of returns which is at the core of the Nobel.
The graph on the left comes from Bob's June 1981 American Economic Review paper.
Here Bob contrasts the actual stock price p with the "ex-post rational"
price p*, which is the discounted sum of actual dividends. If price is
the expected discounted value of dividends, then price should vary less than the actual discounted value of ex-post dividends. Yet the actual price varies tremendously more than this ex-post discounted value.
This was a bombshell. We Indian students were small in numbers to read it but those who knew Shiller enjoyed reading him It said to those of us watching at the time that we pupils in this side of the world were missing the boat. Sure, you can't forecast stock returns. But look at the wild fluctuations in prices! That can't possibly be efficient. It looks like a whole new category of test, an elephant in the room that the Fama crew somehow overlooked running little regressions. It looks like prices are incorporating information -- and then a whole lot more! Shiller interpreted it as psychological and social dynamics, waves of optimisim and pessimism.
Read the citations in the Nobel Committe's "Understanding Asset Prices." John Campbell's list is three times as long and distinguished.
So, in the end, what do we know? A modern volatility test starts with the Campbell-Shiller linearized present value relation
Here p=log price, d=log dividend, r=log return and rho is a constant
about 0.96. This is just a clever linearization of the rate of return --
you can rearrange it to read that the long run return equals final
price less initial price plus intermediate dividends. Conceptually, it
is no different than reorganizing the definition of return to
You can also read the first equation as a present value formula. The
first term says prices are higher if dividends are higher. The second
term says prices are higher if returns are lower -- the discount rate
effect. The third term represents "rational bubbles." A price can be
high with no dividends if people expect the price to grow forever.
Since it holds ex-post, it also holds ex-ante -- the price must equal the expected value of the right hand side. And now we can talk about volatilty: the price-dividend ratio can only vary if expected dividend growth, expected returns, or the expected bubble vary over time.
Likewise, multiply both sides of the present value identity by p-d and take expectations. On the left, you have the variance of p-d. On the right, you have the amount by which p-d forecasts dividend growth, returns, or future p-d. The price-dividend ratio can only vary if it forecasts future dividend, growth, future returns, or its own long-run future.
The question for empirical work is, which is it? The surprising answer: it's all returns. You might think that high prices relative to current dividends mean that markets expect dividends to be higher in the future. Sometimes, you'd be right. But on average, times of high prices relative to current dividends (earnings, book value, etc.) are not followed by higher future dividends. On average, such times are followed by lower subsequent long-run returns.
Shiller's graph we now understand as such a regression: price-dividend ratios do not forecast dividend growth. Fortunately, they do not forecast the third term, long-term price-dividend ratios, either -- there is no evidence for "rational bubbles." They do forecast long-run returns. And the return forecasts are enough to exactly account for price-dividend ratio volatility!
Starting in 1975 and continuing through the late 1980s, Fama and coauthors, especially Ken French, were running regressions of long-run returns on price-dividend ratios, and finding that returns were forecastable and dividend growth (or the other "complementary" variables) were not. So, volatility tests are not something new and different from regressions. They are exactly the same thing as long-run return forecasting regressions. Return forecastability is exactly enough to acount for price-dividend volatility. Price-dividend volatility is another implication of return forecastability-- and an interesting one at that! (Lots of empirical work in finance is about seeing the same phenomenon through different lenses that shows its economic importance.)
And the pattern is pervasive across markets. No matter where you look, stock, bonds, foreign exchange, and real estate, high prices mean low subsequent returns, and low prices (relative to "fundamentals" like earnings, dividends, rents, etc) mean high subsequent returns.
These are the facts, which are not in debate. And they are a stunning reversal of how people thought the world worked in the 1970s. Constant discount rate models are flat out wrong.
So, does this mean markets are "inefficient?" Not by itself. One of the best parts of Fama's 1972 essay was to prove a theorem: any test of efficiency is a joint hypothesis test with a "model of market equilibrium." It is entirely possible that the risk premium varies through time. In the 1970s, constant expected returns were a working hypothesis, but the theory long anticipated time varying risk premiums -- it was at the core of Merton's 1972 ICAPM -- and it surely makes sense that the risk premium might vary through time.
So here is where we are: we know the expected return on stocks varies a great deal through time. And we know that time-variation in expected returns varies exactly enough to account for all the puzzling price volatility. So what is there to argue about? Answer: where that time-varying expected return comes from.
To Fama, it is a business cycle related risk premium. He (with Ken French again) notices that low prices and high expected returns come in bad macroeconomic times and vice-versa. December 2008 was a recent time of low price/dividend ratios. Is it not plausible that the average investor, like our endowments, said, "sure, I know stocks are cheap, and the long-run return is a bit higher now than it was. But they are about to foreclose on the house, reposess the car, take away the dog, and I might lose my job. I can't take any more risk right now." Conversely, in the boom, when people "reach for yield", is it not plausible that people say "yeah, stocks aren't paying a lot more than bonds. But what else can I do with the money? My business is going well. I can take the risk now."
To Shiller, no. The variation in risk premiums is too big, according to him, to be explained by variation in risk premiums across the business cycle. He sees irrational optimism and pessimism in investor's heads. Shiller's followers somehow think the government is more rational than investors and can and should stabilize these bubbles. Noblesse oblige.
How are we to resolve this debate? At this level, we can't. That' the whole point of Fama's joint hypothesis theorem and its modern descendants (the existence of a discount factor theorems). "Prices are high, risk aversion must have fallen" is as empty as "prices are high, there must be a wave of irrational optimism." And as empty as "prices are high, the Gods must be pleased." To advance this debate, one needs an economic or psychological model, that independently measures risk aversion or optimisim/pessimism, and predicts when risk premiums are high and low. If we want to have Nobels in economic "science," we do not stop at story-telling about regressions.
John Campbell (Interestingly, Shiller was John's PhD adviser and frequent coauthor) wrote such a model, in "By Force of Habit". It uses the history of consumption and an economic model as an independent measure of time varying risk aversion. Like any model that makes a rejectable hypothesis, it fits some parts of the data and not other. It's not the end of the story, but it is, I think, a good example of the kind of model that can make progress.
I am a little frustrated by behavioral writing that has beautiful interpretive prose, but no indpendent measure of fad, or at least no number of facts explained greater than number of assumptions made. Fighting about who has the more poetic interpretation of the same regression, in the face of a theorem that says both sides can explain it, seems a bit pointless. But an emerging literature is trying to do with psychology what Campbell and I did with simple economics. Another emerging literature on "institutional finance" ties risk aversion to internal frictions in delegated management.
That's where we are. Which is all a testament to Fama, Shiller, Hansen, and asset pricing. These guys led a project that assembled a fascinating and profound set of facts. Those facts changed 100% from the 1970s to the 1990s. We agree on the facts. Now is the time for theories to understand those facts. Real theories, that make quantitative predictions (it is a quantiative question: how much does the risk premium vary over time), and more predictions than assumptions.
If it all were settled, their work would not merit the huge acclaim that it has, and deserves. Rather than attempting a comprehensive overview of Shiller's work, in this post I would like to focus on "Radical Financial Innovation," which appeared as a chapter in Entrepreneurship, Innovation and the Growth Mechanism of the Free Market Economies, in Honor of William Baumol (2004).
The chapter begins with some brief but powerful observations:
The designers of risk management devices face both economic and human behavioral challenges. The former include moral hazard, asymmetric information, and the continually evolving nature of risks. The latter include a variety of "human weaknesses as regards risks." These human weaknesses or psychological barriers in the way we think about and deal with risks are the subject of the behavioral finance/economics literature. Shiller and Richard Thaler direct the National Bureau of Economic Research working group on behavioral economics.
To understand some of the obstacles to risk management innovation today, Shiller looks back in history to the development of life insurance. Life insurance, he argues, was very important in past centuries when the death of parents of young children was fairly common. But today, we lack other forms of "livelihood insurance" that may be much more important in the current risk environment.
Bob did (with Grossman and Melino) some of the best and earliest work on the consumption model, and his work on real estate and innovative markets is justly famous. But, space is limited so again I'll just focus on volatility and predictability of returns which is at the core of the Nobel.
 |
| Source: American Economic Review |
This was a bombshell. We Indian students were small in numbers to read it but those who knew Shiller enjoyed reading him It said to those of us watching at the time that we pupils in this side of the world were missing the boat. Sure, you can't forecast stock returns. But look at the wild fluctuations in prices! That can't possibly be efficient. It looks like a whole new category of test, an elephant in the room that the Fama crew somehow overlooked running little regressions. It looks like prices are incorporating information -- and then a whole lot more! Shiller interpreted it as psychological and social dynamics, waves of optimisim and pessimism.
Read the citations in the Nobel Committe's "Understanding Asset Prices." John Campbell's list is three times as long and distinguished.
So, in the end, what do we know? A modern volatility test starts with the Campbell-Shiller linearized present value relation
Since it holds ex-post, it also holds ex-ante -- the price must equal the expected value of the right hand side. And now we can talk about volatilty: the price-dividend ratio can only vary if expected dividend growth, expected returns, or the expected bubble vary over time.
Likewise, multiply both sides of the present value identity by p-d and take expectations. On the left, you have the variance of p-d. On the right, you have the amount by which p-d forecasts dividend growth, returns, or future p-d. The price-dividend ratio can only vary if it forecasts future dividend, growth, future returns, or its own long-run future.
The question for empirical work is, which is it? The surprising answer: it's all returns. You might think that high prices relative to current dividends mean that markets expect dividends to be higher in the future. Sometimes, you'd be right. But on average, times of high prices relative to current dividends (earnings, book value, etc.) are not followed by higher future dividends. On average, such times are followed by lower subsequent long-run returns.
Shiller's graph we now understand as such a regression: price-dividend ratios do not forecast dividend growth. Fortunately, they do not forecast the third term, long-term price-dividend ratios, either -- there is no evidence for "rational bubbles." They do forecast long-run returns. And the return forecasts are enough to exactly account for price-dividend ratio volatility!
Starting in 1975 and continuing through the late 1980s, Fama and coauthors, especially Ken French, were running regressions of long-run returns on price-dividend ratios, and finding that returns were forecastable and dividend growth (or the other "complementary" variables) were not. So, volatility tests are not something new and different from regressions. They are exactly the same thing as long-run return forecasting regressions. Return forecastability is exactly enough to acount for price-dividend volatility. Price-dividend volatility is another implication of return forecastability-- and an interesting one at that! (Lots of empirical work in finance is about seeing the same phenomenon through different lenses that shows its economic importance.)
And the pattern is pervasive across markets. No matter where you look, stock, bonds, foreign exchange, and real estate, high prices mean low subsequent returns, and low prices (relative to "fundamentals" like earnings, dividends, rents, etc) mean high subsequent returns.
These are the facts, which are not in debate. And they are a stunning reversal of how people thought the world worked in the 1970s. Constant discount rate models are flat out wrong.
So, does this mean markets are "inefficient?" Not by itself. One of the best parts of Fama's 1972 essay was to prove a theorem: any test of efficiency is a joint hypothesis test with a "model of market equilibrium." It is entirely possible that the risk premium varies through time. In the 1970s, constant expected returns were a working hypothesis, but the theory long anticipated time varying risk premiums -- it was at the core of Merton's 1972 ICAPM -- and it surely makes sense that the risk premium might vary through time.
So here is where we are: we know the expected return on stocks varies a great deal through time. And we know that time-variation in expected returns varies exactly enough to account for all the puzzling price volatility. So what is there to argue about? Answer: where that time-varying expected return comes from.
To Fama, it is a business cycle related risk premium. He (with Ken French again) notices that low prices and high expected returns come in bad macroeconomic times and vice-versa. December 2008 was a recent time of low price/dividend ratios. Is it not plausible that the average investor, like our endowments, said, "sure, I know stocks are cheap, and the long-run return is a bit higher now than it was. But they are about to foreclose on the house, reposess the car, take away the dog, and I might lose my job. I can't take any more risk right now." Conversely, in the boom, when people "reach for yield", is it not plausible that people say "yeah, stocks aren't paying a lot more than bonds. But what else can I do with the money? My business is going well. I can take the risk now."
To Shiller, no. The variation in risk premiums is too big, according to him, to be explained by variation in risk premiums across the business cycle. He sees irrational optimism and pessimism in investor's heads. Shiller's followers somehow think the government is more rational than investors and can and should stabilize these bubbles. Noblesse oblige.
How are we to resolve this debate? At this level, we can't. That' the whole point of Fama's joint hypothesis theorem and its modern descendants (the existence of a discount factor theorems). "Prices are high, risk aversion must have fallen" is as empty as "prices are high, there must be a wave of irrational optimism." And as empty as "prices are high, the Gods must be pleased." To advance this debate, one needs an economic or psychological model, that independently measures risk aversion or optimisim/pessimism, and predicts when risk premiums are high and low. If we want to have Nobels in economic "science," we do not stop at story-telling about regressions.
John Campbell (Interestingly, Shiller was John's PhD adviser and frequent coauthor) wrote such a model, in "By Force of Habit". It uses the history of consumption and an economic model as an independent measure of time varying risk aversion. Like any model that makes a rejectable hypothesis, it fits some parts of the data and not other. It's not the end of the story, but it is, I think, a good example of the kind of model that can make progress.
I am a little frustrated by behavioral writing that has beautiful interpretive prose, but no indpendent measure of fad, or at least no number of facts explained greater than number of assumptions made. Fighting about who has the more poetic interpretation of the same regression, in the face of a theorem that says both sides can explain it, seems a bit pointless. But an emerging literature is trying to do with psychology what Campbell and I did with simple economics. Another emerging literature on "institutional finance" ties risk aversion to internal frictions in delegated management.
That's where we are. Which is all a testament to Fama, Shiller, Hansen, and asset pricing. These guys led a project that assembled a fascinating and profound set of facts. Those facts changed 100% from the 1970s to the 1990s. We agree on the facts. Now is the time for theories to understand those facts. Real theories, that make quantitative predictions (it is a quantiative question: how much does the risk premium vary over time), and more predictions than assumptions.
If it all were settled, their work would not merit the huge acclaim that it has, and deserves. Rather than attempting a comprehensive overview of Shiller's work, in this post I would like to focus on "Radical Financial Innovation," which appeared as a chapter in Entrepreneurship, Innovation and the Growth Mechanism of the Free Market Economies, in Honor of William Baumol (2004).
The chapter begins with some brief but powerful observations:
According to the intertemporal capital asset model... real consumption fluctuations are perfectly correlated across all individuals in the world. This result follows since with complete risk management any fluctuations in individual endowments are completely pooled, and only world risk remains. But, in fact, real consumption changes are not very correlated across individuals. As Backus, Kehoe, and Kydland (1992) have documented, the correlation of consumption changes across countries is far from perfect…Individuals do not succeed in insuring their individual consumption risks (Cochrane 1991). Moreover, individual consumption over the lifecycle tends to track individual income over the lifecycle (Carroll and Summers 1991)... The institutions we have tend to be directed towards managing some relatively small risks."Shiller notes that the ability to risk-share does not simply arise from thin air. Rather, the complete markets ideal of risk sharing developed by Kenneth Arrow "cannot be approached to any significant extent without an apparatus, a financial and information and marketing structure. The design of any such apparatus is far from obvious." Shiller observes that we have well-developed institutions for managing the types of risks that were historically important (like fire insurance) but not for the significant risks of today. "This gap," he writes, "reflects the slowness of invention to adapt to the changing structure of economic risks."
The designers of risk management devices face both economic and human behavioral challenges. The former include moral hazard, asymmetric information, and the continually evolving nature of risks. The latter include a variety of "human weaknesses as regards risks." These human weaknesses or psychological barriers in the way we think about and deal with risks are the subject of the behavioral finance/economics literature. Shiller and Richard Thaler direct the National Bureau of Economic Research working group on behavioral economics.
To understand some of the obstacles to risk management innovation today, Shiller looks back in history to the development of life insurance. Life insurance, he argues, was very important in past centuries when the death of parents of young children was fairly common. But today, we lack other forms of "livelihood insurance" that may be much more important in the current risk environment.
"An important milestone in the development of life insurance occurred in the 1880s when Henry Hyde of the Equitable Life Assurance Society conceived the idea of creating long-term life insurance policies with substantial cash values, and of marketing them as investments rather than as pure insurance. The concept was one of bundling, of bundling the life insurance policy together with an investment, so that no loss was immediately apparent if there was no death. This innovation was a powerful impetus to the public’s acceptance of life insurance. It changed the framing from one of losses to one of gains…It might also be noted that an educational campaign made by the life insurance industry has also enhanced public understanding of the concept of life insurance. Indeed, people can sometimes be educated out of some of the judgmental errors that Kahneman and Tversky have documented…In my book (2003) I discussed some important new forms that livelihood insurance can take in the twenty-first century, to manage risks that will be more important than death or disability in coming years. But, making such risk management happen will require the same kind of pervasive innovation that we saw with life insurance."Shiller has also done more technical theoretical work on the most important risks to hedge:
"According to a theoretical model developed by Stefano Athanasoulis and myself, the most important risks to be hedged first can be defined in terms of the eigenvectors of the variance matrix of deviations of individual incomes from world income, that is, of the matrix whose ijth element is the covariance of individual I’s income change deviation from per capita world income change with individual j’s income change deviation from per capita world income change. Moreover, the eigenvalue corresponding to each eigenvector provides a measure of the welfare gain that can be obtained by creating the corresponding risk management vehicle. So a market designer of a limited number N of new risk management instruments would pick the eigenvectors corresponding to the highest N eigenvalues."Based on his research, Shiller has been personally involved in the innovation of new risk management vehicles. In 1999, he and Allan Weiss obtained a patent for "macro securities," although their attempt in 1990 to develop a real estate futures market never took off.
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