BERND PAPE Asset Allocation, Multivariate Position Based Trading ...

More documents

Recommendations

Info

180 ACTA WASAENSIA454647 % Check whether length of Y is integer multiple of NOBS4849 if mod(length(y),nobs)50 error(’The length of Y must be an integer multiple of NOBS.’)51 end525354 % Initialize output variables5556 tests = length(y)/nobs; %# estimates to be produced57 d = zeros(tests,1); %(test*1) estimates for d58 rejections = zeros(1,2); %(1*2) % rejections of H0 vs d0596061 % Generate independent variables for log-periodogramm regressions6263 m = floor(sqrt(nobs)); %# lowest Fourier freq. considered64 j = (1:m)’; %(m*1) Fourier frequency indexes65 lambda = 2*pi*j/nobs; %(m*1) Fourier frequencies66 ind = [ones(m,1) log(4*sin(lambda/2).^2)]; %(m*2) indep. var.676869 % Pre-calculate cosine factors for calculation of periodogram7071 cosine = cos(lambda*(1:nobs-1)); %(m*(nobs-1)) Hamilton p.158727374 % Start loop over # log-periodogram regressions to be performed7576 for test = 1:tests7778 %Calculate periodogram of sub-sample79 data = y((test-1)*nobs+1 : test*nobs); %(nobs*1) ts window80 acfproc = acf(data, nobs-1); %structure from calling acf81 rho = acfproc.ac; %((nobs-1)*1) autocorrelation coeff.’s82 I = var(data)/(2*pi)*(1+2*cosine*rho); %(m*1) periodogram8384 %Perform log-periodogram regression85 dep = log(I); %(m*1) dependent variable86 regression = ols(dep,ind); %structure from calling ols87 d(test) = -regression.beta(2); %(scalar) slope coefficient8889 end %end of loop over # log-periodogram regressions9091
ACTA WASAENSIA 18192 % Calculate # rejections of H0: d=0 using Lux (1996) p.7049394 sqdev = ind(:,2)-sum(ind(:,2))/m; %(m*1) squared deviations95 sigma = pi/sqrt(sqdev’*sqdev); %asympt. stdev. of d est.96 critd = norminv([0.5-ci/2 0.5+ci/2],0,sigma); %critical values97 rejections(1) = sum(d099100 % Calculate summary statistics101102 summary = [min(d) median(d) max(d)]; %lowest, median, higest d
Page 5 and 6:
ACTA WASAENSIA 52.10.1 Cross-Sectio
Page 7 and 8:
ACTA WASAENSIA 7A5 Matlabcodeforerr
Page 10 and 11:
10 ACTA WASAENSIAIn chapter 5 I sha
Page 12 and 13:
12 ACTA WASAENSIA2 Statistical Prop
Page 14:
14 ACTA WASAENSIA2.2 Absence of Ser
Page 17 and 18:
ACTA WASAENSIA 17The survival or ta
Page 19 and 20:
ACTA WASAENSIA 19& Scheinkman (1987
Page 21 and 22:
ACTA WASAENSIA 212.6 Long Range Dep
Page 23 and 24:
ACTA WASAENSIA 23A particular class
Page 26 and 27:
26 ACTA WASAENSIAMultiscaling may t
Page 28 and 29:
28 ACTA WASAENSIAHansen (1982) to c
Page 30 and 31:
30 ACTA WASAENSIAThe leverage hypot
Page 32 and 33:
32 ACTA WASAENSIAIn order to aviod
Page 34 and 35:
34 ACTA WASAENSIAon a risk-adjusted
Page 36 and 37:
36 ACTA WASAENSIAplanations for the
Page 38 and 39:
38 ACTA WASAENSIAspeculative prices
Page 40 and 41:
40 ACTA WASAENSIAindex α is howeve
Page 42 and 43:
42 ACTA WASAENSIAThis approach has
Page 44 and 45:
44 ACTA WASAENSIAimplying exponenti
Page 46 and 47:
46 ACTA WASAENSIA3.2.4 Descriptive
Page 48 and 49:
48 ACTA WASAENSIAdivisible version
Page 50 and 51:
50 ACTA WASAENSIAto check their ade
Page 52 and 53:
52 ACTA WASAENSIAvolatility into th
Page 54 and 55:
54 ACTA WASAENSIAfeedback of the co
Page 56 and 57:
56 ACTA WASAENSIAarriving at the fo
Page 58 and 59:
58 ACTA WASAENSIAat iteration k com
Page 60 and 61:
60 ACTA WASAENSIAmultipliers in the
Page 62 and 63:
62 ACTA WASAENSIALux & Ausloos (200
Page 64 and 65:
64 ACTA WASAENSIAmarkets dominated
Page 66 and 67:
66 ACTA WASAENSIAThe “representat
Page 68 and 69:
68 ACTA WASAENSIA(1983) 114 motivat
Page 70 and 71:
70 ACTA WASAENSIAIn the following w
Page 72 and 73:
72 ACTA WASAENSIASethi (1996) exten
Page 74 and 75:
74 ACTA WASAENSIASummation over all
Page 76 and 77:
76 ACTA WASAENSIAthe microscopic un
Page 78 and 79:
78 ACTA WASAENSIA& Winker (2003) an
Page 80 and 81:
80 ACTA WASAENSIAWe wish to obtain
Page 82 and 83:
82 ACTA WASAENSIAindividual markets
Page 84 and 85:
84 ACTA WASAENSIAreturn series. I l
Page 86 and 87:
86 ACTA WASAENSIASwitches between c
Page 88 and 89:
88 ACTA WASAENSIAfollowing necessar
Page 90 and 91:
90 ACTA WASAENSIATable 1. Parameter
Page 92 and 93:
92 ACTA WASAENSIAin stepwise search
Page 94 and 95:
94 ACTA WASAENSIA0.2Logreturns Para
Page 96 and 97:
96 ACTA WASAENSIA0.8Chartist Index
Page 98 and 99:
98 ACTA WASAENSIATable 3. Kurtosis
Page 100 and 101:
100 ACTA WASAENSIAthe most extreme
Page 102 and 103:
102 ACTA WASAENSIATable 4. Estimate
Page 104 and 105:
104 ACTA WASAENSIATable 8. Results
Page 106 and 107:
106 ACTA WASAENSIATable 9. Results
Page 108 and 109:
108 ACTA WASAENSIATable 11. Paramet
Page 110 and 111:
110 ACTA WASAENSIA1 x 105 Aggr. Inv
Page 112 and 113:
112 ACTA WASAENSIA200Aggr. Inventor
Page 114 and 115:
114 ACTA WASAENSIA5 x 105 Aggr. Wea
Page 116 and 117:
116 ACTA WASAENSIAOverall, we can a
Page 118 and 119:
118 ACTA WASAENSIAby Lux (1998). Th
Page 120 and 121:
120 ACTA WASAENSIAwhere s is a disc
Page 122 and 123:
122 ACTA WASAENSIAprocessing valuat
Page 124 and 125:
124 ACTA WASAENSIAProof. See append
Page 126 and 127:
126 ACTA WASAENSIA2.5Logarithmice T
Page 128 and 129:
128 ACTA WASAENSIA0.4Logreturns Ass
Page 130 and 131: 130 ACTA WASAENSIA0.4Logreturns Ass
Page 132 and 133: 132 ACTA WASAENSIATable 17. Probabi
Page 134 and 135: 134 ACTA WASAENSIATable 18. Median
Page 136 and 137: 136 ACTA WASAENSIAIn order to test
Page 138 and 139: 138 ACTA WASAENSIAReferencesAbhyank
Page 140 and 141: 140 ACTA WASAENSIABaillie, R. T. (1
Page 142 and 143: 142 ACTA WASAENSIABlack, F. & M. Sc
Page 144 and 145: 144 ACTA WASAENSIAButler, R. J., J.
Page 146 and 147: 146 ACTA WASAENSIACont, R. (2001).
Page 148 and 149: 148 ACTA WASAENSIAEmbrechts, P., C.
Page 150 and 151: 150 ACTA WASAENSIAFielitz, B. D. (1
Page 152 and 153: 152 ACTA WASAENSIAGhysels, E., A. C
Page 154 and 155: 154 ACTA WASAENSIAHeston, S. L. (19
Page 156 and 157: 156 ACTA WASAENSIAKaldor, N. (1939)
Page 158 and 159: 158 ACTA WASAENSIALiesenfeld, R. (1
Page 160 and 161: 160 ACTA WASAENSIALye, J. N. & V. L
Page 162 and 163: 162 ACTA WASAENSIAMikosch, T. (2003
Page 164 and 165: 164 ACTA WASAENSIAPindyck, R. S. (1
Page 166 and 167: 166 ACTA WASAENSIASethi, R. (1996).
Page 168 and 169: 168 ACTA WASAENSIATesfatsion, L. &
Page 170 and 171: 170 ACTA WASAENSIAAAppendixA1Matlab
Page 172 and 173: 172 ACTA WASAENSIA87 % a3 = 1; %imp
Page 174 and 175: 174 ACTA WASAENSIA181 nmout = max([
Page 176 and 177: 176 ACTA WASAENSIA275276 figure;277
Page 178 and 179: 178 ACTA WASAENSIA45 end464748 % In
Page 182 and 183: 182 ACTA WASAENSIAA4Matlab code for
Page 184 and 185: 184 ACTA WASAENSIA888990 % create P
Page 186 and 187: 186 ACTA WASAENSIA4344 nobs = 500;
Page 188 and 189: 188 ACTA WASAENSIAA6Matlab code for
Page 190 and 191: 190 ACTA WASAENSIA9192 for t = 1:T
Page 192 and 193: 192 ACTA WASAENSIA185 fcash = fcash
Page 194 and 195: 194 ACTA WASAENSIA279280281 %Output
Page 196 and 197: 196 ACTA WASAENSIA(4.20), and o(τ
Page 198 and 199: 198 ACTA WASAENSIAas t = n k P ˙
Page 200 and 201: 200 ACTA WASAENSIA nBn˙f2 = v B n
Page 202 and 203: 202 ACTA WASAENSIA f˙1−c1 = v B
Page 204 and 205: 204 ACTA WASAENSIAlocal stability o
show all

BERND PAPE Asset Allocation, Multivariate Position Based Trading ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?