Add training workflow, datasets, and runbook

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Chapter 28: Mathematical Applications
TABLE 28-3.
Distance weighting factors.
465
Option
Distonce
from
Stock Price
Distance
Weighting Factor
January 30
January 35
April 35
April 40
TABLE 28-4.
Option's implied volatility.
.091 (3/33)
.061 (2/33)
.061 (2/33)
.212 (7 /33)
.41
.57
.57
.02
Volume Distance Option's Implied
Option Factor Factor Volotility
January 30 .25 .41 .34
January 35 .45 .57 .28
April 35 .275 .57 .30
April40 .025 .02 .38
Implied = .25 x .41 x .34 + .45 x .57 x .28 + .275 x .57 x .30 + .025 x .02 x .38
volatility. .25 x .41 + .45 x .57 + .275 x .57 + .025 x .02
= .298
ual option's implied volatilities. Rather, it is a composite figure that gives the most
weight to the heavily traded, near-the-money options, and very little weight to the
lightly-traded (5 contracts), deeply out-of-the-money April 40 call. This implied
volatility is still a form of standard deviation, and can thus be used whenever a stan­
dard deviation volatility is called for.
This method of computing volatility is quite accurate and proves to be sensitive
to changes in the volatility of a stock. For example, as markets become bullish or
bearish (generating large rallies or declines), most stocks will react in a volatile man­
ner as well. Option premiums expand rather quickly, and this method of implied
volatility is able to pick up the change quickly. One last bit of fine-tuning needs to be
done before the final volatility of the stock is arrived at. On a day-to-day basis, the
implied volatility for a stock - especially one whose options are not too active may
fluctuate more than the strategist would like. A smoothing effect can be obtained by