410 THE HANDBOOK OF PORTFOLIO MATHEMATICS
These values given by (12.11) are shown in the table below.Observed Calculated
Play# (12.05) (12.10)2 0.25 0.200066
3 0.25 0.236646
4 0.25 0.268515
5 0.3125 0.296278
6 0.3125 0.320466
7 0.367188 0.341538
8 0.367188 0.359896
9 0.367188 0.375889
10 0.389648 0.389822
11 0.389648 0.40196
12 0.413818 0.412535
13 0.413818 0.421748
14 0.436829 0.429774
15 0.436829 0.436767
16 0.436829 0.442858
17 0.447441 0.448165
18 0.447441 0.452789
19 0.459791 0.456817
20 0.459791 0.460326
21 0.459791 0.463383
22 0.466089 0.466046
23 0.466089 0.468367
24 0.47383 0.470388
25 0.47383 0.472149
26 0.482092 0.473683This fitted line now, Equation (12.10), is shown superimposed as the
solid line over Figure 12.1, now as Figure 12.2.
Now that we have our three parameters, I can determine for, say, aqof
300, by plugging in these values into (12.10), that my risk of ruin [RR(.6)] is
.484059843.
At aqof 4,000 I arrive at nearly the same number. Obviously, the hori-
zontal asymptote is very much in this vicinity.
The asymptote of such a line is determined, as pointed out earlier by
(12.09), since the line given by (12.10) is a smooth one.
Let’s go back to our real-world example now, the single scenario set of
10 scenarios. Fitting to our earlier case of a single scenario set with 10 sce-
narios, whereby we were able to calculate theRR(.6) values forq=1...19,