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Template:M intro design System redundancy: Difference between revisions
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, 26 September 2023→Assembling a risk period out of snapshots
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The second time-bound scenario tells us something small, but meaningful about the history of the world. The snapshot does not. | The second time-bound scenario tells us something small, but meaningful about the history of the world. The snapshot does not. | ||
====Assembling a risk period out of snapshots==== | |||
Here is the appeal of datamodernism: you can assemble the appearance of temporal continuity — the calculations for which are gargantuan — out of a series of data snapshots, the calculations for which are merely huge. Processing capacity being what it is — still limited, approaching an asymptote and increasingly energy consumptive (rather like approaching the speed of light) — there is still a virtue in economy. The shorter the window of time we must represent, the better. The listed corporate’s quarterly reporting period is one such arbitrarily short timescale. Commentators lament how it prioritises unsustainable short term profits over long term corporate health and stability — it does — while modernists, and those resigned to it, shrug their shoulders with varying degrees of regret, shake their heads and say that is just how it is. | |||
For our purposes, another short period that risk managers look at is the ''liquidity period'': the longest plausible time one is obliged to hold onto a given period of risk before one can get out of it. This is the period over which one can measure — guestimate — one’s maximum potential unavoidable loss. | |||
This differs for different assets. For equities, for the most part it is almost — not quite, and this is important — instant. It is generally treated as “a day or so”. | |||
For an investment fund it might be a day, a month, a quarter, a year or longer. For real estate it is realistically months, but in any case indeterminate. | |||
Generally the more liquid is your investment, the better you can control its risk. But liquidity, like volatility, comes and goes. It is usually not there when you most need it. So we should err on the long side when estimating liquidity periods in a time of stress. | |||
But there are strong incentives to keep it as short as possible. Calculability for one. | |||
====Glass half full and multidimensionality==== | |||
Here is where history — real history , not the synthetic history afforded by data modernism — makes a difference. | |||
For we might say the realistic range a stock can move in in a liquidity period — its “gap risk” — is relatively stable. Say 30 percent of its market value. This value we derive from a few technical and fundamental things: the fundamental book value of a business, the presumption that there is a sensible bid and ask, so that the stock will oscillate around its “true value”. What that is you can derived from a sufficiently long observation period (the more illiquid, the longer you need, as the bulls and the bears must iron each other out). | |||
But modernist techniques are not good at estimating that period. Their optimal period, remember, is infinitesimal. A still life. In a still life, a glass half full and a glass half empty look exactly alike. We apply the same metric to them: ''assuming the market value is fair, how much could I lose in the time it would realistically take me to sell''? 30 percent, right? | |||
====Liquidity period - how long is your risk period==== | |||
====Snapshots of a glass half full are a bad proxy==== | |||
=== It’s the long run, stupid=== | === It’s the long run, stupid=== | ||