Template:M intro isda tail events: Difference between revisions

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 I’ll have a cup of tea<br>
 And tell you of my dreaming.
-:—Blondie, ''Dreaming'' (1979)}}{{d|Tail event||n|}}{{nld}}
+:—Blondie, ''Dreaming'' (1979)}}{{d|Tail event|/teɪl ɪˈvɛnt/|n|}}{{nld}}
-{{L1}}'''Statistics''': Of a range of possible independent events, one whose frequency is three or more [[Normal distribution|standard deviation]]s from the mean. An event with a low [[probability]]. <li>
+'''{{helvetica|Statistics}}''': Of a range of possible independent events, one whose frequency is three or more [[Normal distribution|standard deviation]]s from the mean. An event with a low [[probability]]. <br>
-'''Work life''': An unwanted outcome you didn’t expect, to which you weren’t paying attention, and, therefore, for which you don’t think you should be blamed.
+'''{{helvetica|Work life}}''': An unwanted outcome you didn’t expect, to which you weren’t paying attention, and, therefore, for which you don’t think you should be blamed.
-</Ol>
 ====The randomly distributed marketplace====
-{{Drop|A|market, in the}} abstract, looks like a [[nomological machine]]. There is a bounded environment, a finite trading day, a limited number of market participants and a defined set of financial instruments with which one can engage in a limited range of transactions, whose outcomes will set the price for the traded instrument, which can be easily compared with the last traded price for that instrument (in that it will be higher, lower, or the same).
+{{Drop|A|market, in the}} abstract, looks like what [[Nancy Cartwright]] calls a “[[nomological machine]]”. A simplified ''model'' of the real world having defined boundaries and simplified operating conditions: a finite trading day, a limited number of market participants and a defined set of [[fungible]] financial instruments with which one participants engage in a limited range of transactions, whose outcomes deterministically set observable prices for that set of traded instruments, bearing  numerical relationships with previous traded prices for the same instrument (in that they will be higher, lower, or the same).
-From this information we can ''derive'' a relationship between transactions — price went up, price stayed the same, price went down — and a ''trend''. A trend is a stab at extracting a [[signal]] from the [[noise]].
+The world these instruments represent is intractable. It does not have boundaries, even similar “instruments” are not fungible, the range of possible events that can occur to them in undefined.
+{{Quote|“A portfolio of [[asset-backed securities]] cannot,” a commodities trader would say, “suffer water damage. They do not rust.”}}
+Not having to deal with rust, water damage, and manufacturing defect simplifies the business of investing. The ''effects'' of these events are supposed to play out in the information layer , and translate efficiently into the prices at which related instruments trade. If an oil company’s tanker is wrecked, it's share price declines.
+It is tempting to infer information from price: to put a drop in the market to “unexpectedly soft non-farm payroll data”. Many people make a living reading tea-leaves in this way.
+From this price information we can ''derive'' a relationship between transactions — price went up, price stayed the same, price went down — and a ''trend''. A trend is a stab at extracting a [[signal]] from the [[noise]].
 The [[signal]] depends on a theory of the game,  Otherwise the “relationship” between the two discrete transactions is arbitrary. Without a theory, everything is [[noise]].
 =====The theory-dependence of signal=====
-If given events are truly “independent” — in a first order sense, they are: the participants in the later trade do not know who or where the participants in the earlier even are, let alone what their motivations for trading were — then a “trend” we draw between them is, more or less, meaningless. All that is left is mathematics.
+If events are truly “independent” — in a first order sense, they are: the participants in the later trade do not know who or where the participants in the earlier even are, let alone what their motivations for trading were — then any “trend” we draw between them beyond their distribution is, more or less, meaningless. All that is left is mathematics.
-But we ''have'' a theory, so draw the line all the same. We make assumptions about the homogeneity of all market participants: we assume all have similar price information, and that all are propelled by the same essential economic rationalism: you don’t sell things you expect to do well, and you don’t buy things you expect to do badly.
+But we have a theory, so we draw the line all the same. We assume the market is homogeneous, that all participants have similar price information — those who have more are forbidden to trade — and that all are propelled by the same rationale: you don’t sell things you expect to do well, and you don’t buy things you expect to do badly.
 =====Private narratives wash out=====
-Each investor’s private motivations, and opinions, may be nuanced and personal — how is the rest of its portfolio positioned, what local risks is it especially sensitive to — but these idiosyncrasies cancel out in a large sample — they are like the [[Brownian motion]] of molecules in a [[nice hot cup of tea]]. They are reversions to the [[entropy|entropic mean]]; baseline white noise — so we can disregard them. Which is just as well for the complexity of our models. Until it isn’t.
+Given these assumptions, across the market investors’ private motivations, opinions, theories and idiosyncrasies cancel out — they are like the [[Brownian motion]] of molecules in a [[nice hot cup of tea]]. They are reversions to the [[entropy|entropic mean]]; baseline white noise — so we can disregard them. Which is just as well for the complexity of our models. Until it isn’t.
 Put another way: although the “interconnectedness” of similar transactions means they do ''not'' have the quality of independence that [[normal distributions]] require, most of the time it’s close enough: the information is chaotic — as traders say, “noisy” — in the immediate term, here the dissimilarities between trader motivations are most pronounced, but over a large aggregation of trades and a longer period a “signal” emerges. This is what [[Black-Scholes option pricing model|Black-Scholes]], volatility and convexity models track: as long as all traders all use the same aggregated market information — and the market works hard to ensure they do — a “normal” probabilistic model<ref>I am working hard not to use the intimidating term [[stochastic]]” here by the way.</ref> works fairly well. It’s not a bad ''model''.