Just as memories, not clock time, measure duration in our minds, there are many yardsticks for measuring duration. Baseball uses innings. Our boys measure how long before mommy comes home from a business trip by how many “sleeps”. Let’s do one from finance.
In options trading, models assume time passes linearly but we know market volatility is lumpy. It’s concentrated on business days and even within business days, it’s concentrated near the open and the close. Not all hours are created equal. An option barely erodes on a Saturday but decays off a cliff after a stock reports earnings. Option traders adjust for this behavior by specifying a schedule by which “variance” passes as opposed to time.
If you think of an option as insurance, the value of the contract decays at a non-constant rate. By analogy, imagine having a Carribean travel insurance policy that you secure for a year. The value of that policy will remain fixed for the first 9 months of the year then plummet after the hurricane season. Time passed linearly but the risk that is being insured against decays rapidly as we progress through the storm season.
For the markets people, I want to screw with your X-axis. We are used to seeing time series as a function of hours, months, years and so forth. Quant fund manager (and fellow Cornellian or “corndog” as wifey refers to us) Corey Hoffstein considers tracking prices across potentially more relevant domains than time. In his paper, he writes:
Information does not flow into the market at a constant rate. While time may be a convenient measure, it may actually cause us to sample too frequently in some market environments and not frequently enough in others. One answer may be to transform our measurements into a different domain. Rather than sampling price based upon the market close of each day, we might sample price based upon a fixed amount of cumulative volume, trades, or even variance. In doing so, we might find that our measures now represent a more consistent amount of information flow, despite representing a dynamic amount of data in the time domain.
I’d be excited to find such charts on Koyfin but I’m pretty sure I’d die holding my breath.