We distinguish three different conditions of financial markets: the normal economy, when the liquidity wedge is small and leverage is high; the anxious economy, when the liquidity wedge is big and leverage is curtailed, and the general public is anxiously selling risky assets to more confident natural buyers; and, finally, the crisis or panicked economy, when many formerly leveraged natural buyers are forced to liquidate or sell off their positions to a reluctant public, often going bankrupt in the process. A recent but growing literature on leverage and financial markets has concentrated on crises or panicked economies. We concentrate on the anxious economy (a much more frequent phenomenon) and provide an explanation with testable implications for (1) contagion, (2) flight to collateral, and (3) issuance rationing. Our theory provides a rationale for three stylized facts in emerging markets that we describe below, and perhaps also explains some price behavior of other “emerging asset” classes like the US subprime mortgage market.The authors studied “emerging asset” economies, i.e. emerging markets, but they note that their analysis is applicable to some of the smaller liquidity constrained markets such as “the US subprime mortgage market”. In the paper, they introduce the concept of the “anxious economy”:
This is the state when bad news lowers expected payoffs somewhere in the global economy (say in high yield), increases the expected volatility of ultimate high yield payoffs, and creates more disagreement about high yield, but gives no information about emerging market payoffs. A critical element of our story is that bad news increases not only uncertainty, but also heterogeneity. When the probability of default is low, there cannot be much difference in opinion. Bad news raises the probability of default and also the scope for disagreement. Investors who were relatively more pessimistic before become much more pessimistic afterward. One might think of the anxious economy as a stage that is frequently attained after bad news, and that occasionally devolves into a sell-off if the news grows much worse, but which often (indeed usually) reverts to normal times. After a wave of bad news that lowers prices, investors must decide whether to cut their losses and sell, or to invest more at bargain prices. This choice is sometimes described on Wall Street as whether to catch a falling knife.
They go on to model how financial leverage exacerbate the booms and busts to create Minsky Moments in an anxious economy:
Agents are allowed to borrow money only if they can put up enough collateral to guarantee delivery. Assets in our model play a dual role: they are investment opportunities, but they can also be used as collateral to gain access to cash. The collateral capacity of an asset is the level of promises that can be made using the asset as collateral. This is an endogenous variable that depends on expectations about the distribution of future asset prices. Together with the interest rate, the collateral capacity determines an asset’s borrowing capacity, which is the amount of money that can be borrowed using the asset as collateral. The loan to value (LTV) of an asset is the ratio of the asset’s borrowing capacity to its price. The haircut or margin of an asset is the shortfall of its LTV from 100 percent—in other words, the fraction of the price that must be paid in cash. The maximal leverage of an asset is the inverse if its margin. The leverage in the system, like the other ratios just mentioned, is determined by supply and demand; it is not fixed exogenously…
The underlying dynamic of the anxious economy—fluctuating uncertainty and disagreement— simultaneously creates the leverage cycle and the liquidity wedge cycle; that is why they run in parallel. Since leverage affects the liquidity wedge, the leverage cycle amplifies the liquidity wedge cycle. So what does collateral, and the possibility of leverage, add to the liquidity wedge cycle already discussed? It generates a bigger price crash, not due to asset undervaluation during anxious times, but due to asset overvaluation during normal times. This may lead the press to talk about asset price bubbles.
While the dynamic described by Fostel and Geanakoplos was intended to describe smaller emerging market economies, their description appears to sound an awful lot like the American and European economies in the current environment of rising concerns about sovereign debt.
Higher volatility ahead
One of the conclusions of the paper is that during and in the aftermath the downleg of the crisis, volatility is high because of a heightened liquidity wedge:
We define the liquidity wedge as the spread between the interest rate optimists would be willing to pay and the rate pessimists would be willing to take. As we shall see, the liquidity wedge is a useful way of understanding asset prices. When the liquidity wedge increases, the optimists discount the future by a bigger number, and all asset prices for which they are the marginal buyers fall. The liquidity wedge increases because the disagreement between optimists and pessimists about high yield grows, increasing the desire of optimists to get their hands on more money to take advantage of the high yield buying opportunity. The portfolio and consumption effects create a liquidity wedge cycle: as the real economy moves back and forth between the normal and the anxious stage, the liquidity wedge ebbs and flows.
The observation about higher volatility is consistent with my comments about rising volatility and the implications for investment policy. The chart below from Macquarie Research illustrates the environment of rising macroeconomic volatility.
Wayne Whaley of Witter & Lester has observed the same effect.
Jonathan Tepper of Variant Perception has commented on the same thing.
Examine investment policy assumptions
In the current environment of heightened macroeconomic volatility, which imply higher investment risk and uncertainty, investors need to re-think their approaches to investing and asset allocation. Buy-and-hold allocations may be suboptimal under such circumstances.