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Those illustrations employed the random \ number generator in the Fortran program, Stable.exe provided by Nolan (1997). \ This notebook replicates that random number generator, permitting the faster \ creation of a wider variety of possible portfolios. This notebook is in the \ experimental phase. Enhancements are expected in the future. Monitoring the \ author's Web site for updates is suggested.\n\nThe procedure is as follows:\n\ \n1. Generate several series of stable quasi-random variables using the \ method of McCulloch (1998) based on Chambers, Mallows and Stuck (1976) using \ any value for \[Alpha] \[Subset] [1,2]; \[Beta] \[Subset] [-1,1].\n3. Form a \ portfolio from three sets, each with \[Alpha] = 2, \[Beta] = 0 (asset201, \ asset 202, asset 203), draw the efficient frontier.\n4. Repeat the process, \ each time with a different \[Alpha]. (numbering sets 141, 142,143 for \ \[Alpha] = 1.4 asset1, \[Alpha] = 1.4 asset2, \[Alpha] = 1.4 asset3, etc.)\n\n\ The goal is to show the change in the \"efficient\" frontier as \[Alpha] \ declines. "]], "Text", PageWidth->PaperWidth], Cell[TextData[{ "Stable Random variates are computed using Chambers, Mallows and Stuck \ (1976) using the form and notation of McCulloch (1998) p.373, Adler, et al, \ editors\n\nIt is necessary to use ", StyleBox["Mathematica", FontSlant->"Italic"], "'s Finance Essentials Pack to plot the efficient frontiers" }], "Text", PageWidth->PaperWidth], Cell[CellGroupData[{ Cell[BoxData[{ \(CleanSlate[]\), "\n", \($Post := If[MatrixQ[#], MatrixForm[#], #] &\), "\n", \(\(Off[General::spell1];\)\), "\n", \(\(Off[General::spell];\)\), "\n", \(<< Finance`Examples`\), "\n", \(<< Statistics`ContinuousDistributions`\), "\n", \(<< Statistics`MultiDescriptiveStatistics`\), "\[IndentingNewLine]", \(<< LinearAlgebra`MatrixManipulation`\), "\[IndentingNewLine]", \(<< Graphics`\)}], "Input", PageWidth->PaperWidth, InitializationCell->True], Cell[BoxData[ \(CleanSlate[]\)], "Output", PageWidth->PaperWidth] }, Open ]], Cell["\<\ We require u and v to be two iid uniform (0,1) quasi-random variables. We \ begin with two vectors of 1000 uniform random variables to serve as u and v\ \>", "Text", PageWidth->PaperWidth], Cell[BoxData[ \(\(\(fxran := \[IndentingNewLine]\((u = Table[Random[], {1000}]; \n v = Table[Random[], {1000}]; \[IndentingNewLine]w = Log[1/u]; \n\[CapitalPhi] = \[Pi]\ \((v - .5)\); \n z = \(Cos[\((1 - \[Alpha])\)\ \[CapitalPhi]] + \[Beta]\ Tan[\(\[Pi]\ \ \[Alpha]\)\/2]\ Sin[\((1 - \[Alpha])\)\ \[CapitalPhi]]\)\/\(w\ Cos[\ \[CapitalPhi]]\); \[IndentingNewLine]\(Sin[\[Alpha]\ \[CapitalPhi]] + \[Beta]\ \ Tan[\(\[Pi]\ \[Alpha]\)\/2]\ Cos[\[Alpha]\ \[CapitalPhi]]\)\/Cos[\ \[CapitalPhi]]\ z\^\(\((1 - \ \[Alpha])\)\/\[Alpha]\))\);\)\(\[IndentingNewLine]\)\)\)], "Input", PageWidth->PaperWidth], Cell[CellGroupData[{ Cell[BoxData[{ \(\(asset201 = fxran /. {\[Alpha] \[Rule] 2, \[Beta] \[Rule] 0};\)\), "\[IndentingNewLine]", \(TableForm[{Mean[asset201], Max[asset201], Min[asset201], Variance[asset201]}, TableHeadings \[Rule] {{"\", "\", "\", \*"\"\<\!\(\ \[Sigma]\^2\)\>\""}}]\)}], "Input", PageWidth->PaperWidth], Cell[BoxData[ InterpretationBox[GridBox[{ {"\<\"Mean\"\>", \(-0.031602038560272536`\)}, {"\<\"Max\"\>", "5.383539311487518`"}, {"\<\"Min\"\>", \(-4.698630166074511`\)}, {"\<\"\\!\\(\[Sigma]\\^2\\)\"\>", "2.0570308616792534`"} }, RowSpacings->1, ColumnSpacings->3, RowAlignments->Baseline, ColumnAlignments->{Left}], TableForm[ {-.031602038560272536, 5.3835393114875183, -4.6986301660745111, 2.0570308616792534}, TableHeadings -> {{"Mean", "Max", "Min", "\!\(\[Sigma]\^2\)"}}]]], "Output", PageWidth->PaperWidth] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[{ \(\(asset202 = fxran /. {\[Alpha] \[Rule] 2, \[Beta] \[Rule] 0};\)\), "\[IndentingNewLine]", \(TableForm[{Mean[asset202], Max[asset202], Min[asset202], Variance[asset202]}, TableHeadings \[Rule] {{"\", "\", "\", \*"\"\<\!\(\ \[Sigma]\^2\)\>\""}}]\)}], "Input", PageWidth->PaperWidth], Cell[BoxData[ InterpretationBox[GridBox[{ {"\<\"Mean\"\>", "0.06290793064282374`"}, {"\<\"Max\"\>", "3.7352592210560007`"}, {"\<\"Min\"\>", \(-3.6046531734812257`\)}, {"\<\"\\!\\(\[Sigma]\\^2\\)\"\>", "1.9003297982344507`"} }, RowSpacings->1, ColumnSpacings->3, RowAlignments->Baseline, ColumnAlignments->{Left}], TableForm[ {.062907930642823739, 3.7352592210560007, -3.6046531734812257, 1.9003297982344507}, TableHeadings -> {{"Mean", "Max", "Min", "\!\(\[Sigma]\^2\)"}}]]], "Output", PageWidth->PaperWidth] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[{ \(\(asset203 = fxran /. {\[Alpha] \[Rule] 2, \[Beta] \[Rule] 0};\)\), "\[IndentingNewLine]", \(TableForm[{Mean[asset203], Max[asset203], Min[asset203], Variance[asset203]}, TableHeadings \[Rule] {{"\", "\", "\", \*"\"\<\!\(\ \[Sigma]\^2\)\>\""}}]\)}], "Input", PageWidth->PaperWidth], Cell[BoxData[ InterpretationBox[GridBox[{ {"\<\"Mean\"\>", \(-0.013941722190131468`\)}, {"\<\"Max\"\>", "4.2035796760192135`"}, {"\<\"Min\"\>", \(-4.741274467293494`\)}, {"\<\"\\!\\(\[Sigma]\\^2\\)\"\>", "2.1601465587117366`"} }, RowSpacings->1, ColumnSpacings->3, RowAlignments->Baseline, ColumnAlignments->{Left}], TableForm[ {-.013941722190131468, 4.2035796760192135, -4.7412744672934943, 2.1601465587117366}, TableHeadings -> {{"Mean", "Max", "Min", "\!\(\[Sigma]\^2\)"}}]]], "Output", PageWidth->PaperWidth] }, Open ]], Cell["\<\ We now form a portfolio of these three assets, each having returns that are \ normally distributed\ \>", "Text", PageWidth->PaperWidth], Cell[BoxData[ \(\(pflo20 = Transpose[{asset201, asset202, asset203}];\)\)], "Input", PageWidth->PaperWidth], Cell["\<\ We now have three assets, each with 1000 return observations, forming a \ portfolio for which we can compute a return vector (meanvect) and covariance \ matrix (cov), determine efficient set portfolios and plot.\ \>", "Text", PageWidth->PaperWidth], Cell[CellGroupData[{ Cell[BoxData[ \(MeanVector[pflo20] // MatrixForm\)], "Input", PageWidth->PaperWidth], Cell[BoxData[ InterpretationBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ 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Cell[BoxData[ TagBox[\(\[SkeletonIndicator] Graphics \[SkeletonIndicator]\), False, Editable->False]], "Output", PageWidth->PaperWidth] }, Open ]], Cell["\<\ We now require a stable NON NORMAL draw where \[Alpha] = 1.4 and \[Beta] = 0 \ \ \>", "Text", PageWidth->PaperWidth], Cell[BoxData[{ \(\(asset141 = fxran /. {\[Alpha] \[Rule] 1.4, \[Beta] \[Rule] 0};\)\), "\[IndentingNewLine]", \(\(TableForm[{Mean[asset141], Max[asset141], Min[asset141], Variance[asset141]}, TableHeadings \[Rule] {{"\", "\", "\", \*"\"\<\!\(\ \[Sigma]\^2\)\>\""}}];\)\[IndentingNewLine]\)}], "Input", PageWidth->PaperWidth], Cell[CellGroupData[{ Cell[BoxData[{ \(\(asset142 = fxran /. {\[Alpha] \[Rule] 1.4, \[Beta] \[Rule] 0};\)\), "\[IndentingNewLine]", \(TableForm[{Mean[asset142], Max[asset142], Min[asset142], Variance[asset142]}, TableHeadings \[Rule] {{"\", "\", "\", \*"\"\<\!\(\ \[Sigma]\^2\)\>\""}}]\)}], "Input", PageWidth->PaperWidth], Cell[BoxData[ InterpretationBox[GridBox[{ {"\<\"Mean\"\>", 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"32.10876928313791`"} }, RowSpacings->1, ColumnSpacings->3, RowAlignments->Baseline, ColumnAlignments->{Left}], TableForm[ {.039100189465817593, 115.53930065377553, -55.152860287157722, 32.10876928313791}, TableHeadings -> {{"Mean", "Max", "Min", "\!\(\[Sigma]\^2\)"}}]]], "Output", PageWidth->PaperWidth] }, Open ]], Cell["\<\ We now form a portfolio of these three assets, each having returns that are \ stable distributed with \[Alpha] = 1.4, \[Beta] = 0\ \>", "Text", PageWidth->PaperWidth], Cell[BoxData[ \(\(pflo14 = Transpose[{asset141, asset142, asset143}];\)\)], "Input", PageWidth->PaperWidth], Cell["\<\ We now have three assets, each with 1000 return observations, forming a \ portfolio for which we can compute a return vector (meanvect) and covariance \ matrix (cov), determine efficient set portfolios and plot.\ \>", "Text", PageWidth->PaperWidth], Cell[CellGroupData[{ Cell[BoxData[ \(MeanVector[pflo14] // MatrixForm\)], "Input", PageWidth->PaperWidth], Cell[BoxData[ 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are \ normally distributed\ \>", "Text", PageWidth->PaperWidth], Cell[BoxData[ \(\(pflo15 = Transpose[{asset151, asset152, asset153}];\)\)], "Input", PageWidth->PaperWidth], Cell["\<\ We now have three assets, each with 1000 return observations, forming a \ portfolio for which we can compute a return vector (meanvect) and covariance \ matrix (cov), determine efficient set portfolios and plot.\ \>", "Text", PageWidth->PaperWidth], Cell[CellGroupData[{ Cell[BoxData[ \(MeanVector[pflo15] // MatrixForm\)], "Input", PageWidth->PaperWidth], Cell[BoxData[ InterpretationBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0.07453168188798746`"}, {"0.10416198035194393`"}, {"0.41150957867909643`"} }], "\[NoBreak]", ")"}], MatrixForm[ {.074531681887987464, .10416198035194393, \ .41150957867909643}]]], "Output", PageWidth->PaperWidth] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ \(cov15 = CovarianceMatrix[pflo15]\)], "Input", PageWidth->PaperWidth], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", 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