If you look at implied volatility, or actual volatility, it goes up when markets decline, and falls when markets increase. The reigning best explanation is that since most companies have some amount of debt, bad news increases leverage as the firm values fall, and higher leveraged firms have higher equity volatility, ceteris paribus. While this ‘Merton model’ explanation explains a little of what’s going one, it probably doesn’t explain that much in practice. I mean, it’s not as if a Merton model helps predict equity volatility, even though KMV likes to convince its clients this is one of their valuable special tactics in predicting firm distress (don’t believe them).
John Geanakoplos has been interested in leverage and business cycles (I noted his lecture below). Along the way he noted that there isn’t really good theory for the stylised fact that volatility tends to go up when markets go down. Why should volatility be contemporaneously correlated with market declines rather than market climbs?
His theory is as follows: because people lever up more on assets that have higher bad news/high volatility correlations, because such assets don’t fall much after the bad news, because so there is still a lot of uncertainty to resolve. Investors prefer such assets with negative news/volatility correlations because they can lever them more.
Now, this argument is tenable only under the cover of a complex setup using real analysis, so that the simple argument being made seems like Godel’s incompleteness theorem. Yet, the answer is simply baked into his assumption of how various assets generate payoffs, and so has this faux-endogeneity that economists love. You see, endogenous results come out of the maths (supposedly), there’s no assuming the result, whereas ‘exogenous results’ are simply assumptions. Of course, these models have their exogeneity hidden in the rigged set up (note his asset payoff trees), which is as convoluted and artificial as any mathematical treatise on spherical horses moving through a vacuum.
Myself, I prefer the simpler theory. When times are good you simply do more of what you did yesterday, because that was good and you want more of it. There is little uncertainty in doing more. When things are bad–eg, you are losing money, or can’t borrow any more–then you need to do something different, you can’t afford to do what you did yesterday. As they say, things always end badly, otherwise they wouldn’t end. There is a lot of uncertainty in doing things different, because there are now a bazillion things that you could do. Thus, bad news brings more uncertainty because it implies change, and good news bring lower uncertainty because it implies repetition.
Now, that won’t make it to JET (the most rigorous of the esteemed economic journals) like Geanakoplos’s paper, but it’s truly a better theory.
NOW WATCH: Money & Markets videos
Business Insider Emails & Alerts
Site highlights each day to your inbox.