In a speech from 2005.

House prices could be high for some good, fundamental reasons. For example, there have been changes in the tax laws that reduce the potential tax bite from selling one home and buying another. Another development, which may be making housing more like an investment vehicle in the U.S., is that it’s now easier and cheaper to get at the equity—either through refinancing, which has become a less costly process, or through an equity line of credit. These innovations in mortgage markets make the funds invested in houses more liquid. There are also constraints on the supply of housing in a number of markets, including the Bay Area. Probably the most obvious candidate for a fundamental factor is low mortgage interest rates. Even so, the consensus seems to be that the high price-to-rent ratio for housing cannot be fully accounted for by these factors. So, while I’m certainly not predicting anything about future house price movements, I think it’s obvious that the housing sector represents a serious issue for monetary policymakers to consider.

…In my view, it makes sense to organize one’s thinking around three consecutive questions—three hurdles to jump before pulling the monetary policy trigger. First, if the bubble were to deflate on its own, would the effect on the economy be exceedingly large? Second, is it unlikely that the Fed could mitigate the consequences? Third, is monetary policy the best tool to use to deflate a house-price bubble?

My answers to these questions in the shortest possible form are, “no,” “no,” and “no.”

In hindsight, her third “no” is the most persuasive. Her point is that regulatory policy could have served to deflate the bubble more directly than monetary policy. However, political leaders at the time were primarily focused on policies that served to inflate the bubble.

The pointer is from John Hussman, a Stanford-trained investment adviser who is concerned that we are in the midst of an equity bubble. He writes,

while price/earnings multiples appear only moderately elevated, those multiples themselves reflect earnings that embed record profit margins that stand about 70% above their historical norms.

Read Hussman’s entire essay. My comments:

1. I share the concern that stock prices may be overvalued.

2. However, I am not convinced that there is anything but a psychological connection between monetary policy and stock valuation.

3. One can hope that even a large “correction” in stock prices would not produce a financial crisis. It seems that debt, rather than equity, is the main cause of financial crises.

4. Therefore, I would not be appealing to the Fed to try to pop the (alleged) stock market bubble.

Tobias Adrian and Nina Boyarchenko write,

Value-at-Risk constraints were incorporated in the Basel II capital framework, which was adopted by major security broker-dealers in the United States—the investment banks—in 2004. Thus, capital constraints are imposed by regulation. In our staff report, we embed the risk-based capital constraint in a model with three sectors: a production sector (firms), a financial intermediary sector, and a household sector. Intermediaries serve two functions: 1) they create new production capacity through investment in the productive sector, and 2) they provide risk-bearing capacity to the households by accumulating wealth through retained earnings. The tightness of the capital constraint—measured by the maximal allowed ratio between intermediary leverage and one over the VaR on the intermediary’s assets—thus affects household welfare. When this ratio is decreased, the intermediaries are more restricted in their risk-taking and can therefore finance less investment. At the same time, since intermediaries take on less leverage and less risk, the systemic risk of the intermediary sector decreases. Accordingly, there is a trade-off between the amount of risk-taking and the price of credit in the real economy.

Pointer from Mark Thoma.

Using value-at-risk to regulate capital is one of the worst ideas ever. First, it assumes a normal distribution of returns, which is not valid far from the mean. Second, as the authors point out, it tends to be procyclical. Third, it is not a measure of the size of the loss in a bad scenario; instead, it is a measure of the size of the loss in scenario that is just a bit better than a bad scenario.

A better approach would be to spell out a specific scenario–an x percent drop in house prices, or a y percent decrease in bond prices, or something along those lines.

The New York Fed Chief says,

I am not yet convinced that breaking up large, complex firms is the right approach. In particular, these firms presumably exist, in large part, because there are scale or network effects that allow these firms to offer certain types of services that have value to their global clients. These benefits might be lost or diminished if such firms were broken up. In addition, the costs incurred in breaking up such firms need to be considered. Finally, the breakup of such firms would not necessarily result in a significant reduction in overall systemic risk if the resulting component firms were still, collectively, systemic.

He cites no evidence about “scale or network effects,” and he knows enough economics to know that those firms need not exist “in large part” because of them. In financial markets where a few basis points can be a tremendous advantage, the too-big-to-fail subsidy can be much more important than any legitimate scale economies. As for considering the costs of breaking up such firms, how does that cost compare to the cost of another financial crisis?

The point that smaller firms could have systemic risk is true, but not decisive. Smaller firms, because they cannot expect bailouts, would act in a more disciplined manner. They and their creditors would have to make decisions knowing that under most circumstances they, not the taxpayers, will bear the consequences.

Here is the 30-minute version of the 2009 New Yorker video interview with Bob Shiller and Nassim Taleb. (Tyler linked to a four-minute segment a few days ago). I want to talk about the difference between Shiller’s and Taleb’s views of inefficient markets.

When I teach regression in statistics, I show what I call the Pythagorean relationship, which describes what computer programs report as the analysis of variance. You are trying to predict a variable, Y, and the predicted values along the regression line are called Y-hat. I draw a right triangle with the standard deviation of Y-hat on one side, the standard error of the regression on another side, and the standard deviation of Y on the hypotenuse. The Pythagorean Theorem then gives you the analysis of variance.

Anyway, a lesson of this is that in an efficient prediction, the variance of your prediction will be less than the variance of the variable that you predict. Mathematically, this is because one side of a right triangle is always shorter than the hypotenuse. Intuitively, if your predictions vary by more than the variable you are trying to predict, then you can do better by toning down your predictions and moving them closer to the mean of the variable.

Shiller’s insight was to apply this idea to asset prices. In some sense, the stock price is a prediction of discounted future dividends, which I will refer to as average realized dividends. In that case, if the stock market is efficient, then the variance of stock prices should be less than the variance of average realized dividends. In fact, it is easy to see that the variance of stock prices is much higher than that of average realized dividends.

What this says, and what Fama and French later confirmed, is that you can make money by betting on mean reversion in stock prices. To do so, you assume use historical average dividends as a proxy for average realized dividends going forward. If you follow a strategy of buying when prices are low relative to historical average dividends and selling when prices are high relative to historical average dividends, then it seems that you will earn an above-normal profit.

Taleb would not bet on mean reversion. Instead, he would load up on out-of-the-money options. That way, you are betting on Black Swans.

Taleb’s point of view gets back to my criticism of Shiller’s work. From Taleb’s point of view, Shiller is like the turkey, who every day notices that the farmer is feeding him and taking care of him. The turkey does not realize that Thanksgiving is coming, and this will change the farmer’s behavior. Similarly, the markets appear to be mean-reverting, but what Shiller does not know is that a Black Swan event could come along.

For example, suppose that bond market investors have a probability p of a Black Swan, meaning that the U.S. government runs out of other options and monetizes a lot of its debt, leading to hyperinflation and making long-term bonds effectively worthless. For simplicity, suppose that this Black Swan either will or will not occur on January 1, 2020. With that simple assumption, on January 1, 2020, the true value of a long-term bond will be either 100 or 0. Whichever it turns out to be, when Shiller does his analysis in 2025, he will find that the variance of the “correct” bond price is zero. Since the price of bonds between now and 2020 is a predictor of the “correct” future bond price, to be an efficient predictor its variance can be no larger than zero.

However, between now and January 1, 2020, the bond price will vary as bond market investors’ estimate of p varies. Thus, the variance of bond prices will not be zero.

I take the view that this possibility of a Black Swan (aka, the peso problem) precludes the use of realized data to construct a “variance bound.” Only in a world where you can rule out Black Swans can you be certain that Shiller has found a market anomaly.

Although I lean toward Taleb, I consider that Shiller may be right. In any case, it is worth contemplating the tension between the two.