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

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112 ACTA WASAENSIA200Aggr. Inventories Market Maker Parameter Set I0−200−400−600−8000 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2x 10 4Aggr. Inventories Market Maker Parameter Set II5000−500−1000−1500−2000−25000 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2x 10 4Aggr. Inventories Market Maker Parameter Set III200010000−1000−2000−30000 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2x 10 4Aggr. Inventories Market Maker Parameter Set IV10005000−500−1000−15000 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2x 10 4Figure 9. Aggregate inventories of the market maker over 20,000 observations.
ACTA WASAENSIA 113Consider next the market maker inventories in figure 9. The results of the augmentedDickey-Fuller tests together with the deterministic trend parameters are depicted intable 13. None of the series are bounded, as they contain either a unit root (Parametersets I, IV) or have a significant deterministic trend (Parameter sets II, III), consistentwith the before mentioned possibility of building up unbounded inventories.Consider finally the wealth dynamics for the different kind of traders. Figure 10 plotsthe aggregate wealth of the chartist and fundamentalist subpopulations over time.It is immediately evident that chartists loose their money to fundamentalists for allparameter sets considered. While one may be tempted to conclude that this will causechartists to die out in course of time, this need not necessarily be so for at least threereasons:1. Since traders are constantly changing between a chartist and a fundamentaliststrategy, traders may well recover losses experienced while using a chartist strategyfrom the profits made when trading as fundamentalists.2. It is reasonable to assume that fundamentalism is costlier then chartism in thesense that figuring out the true value of an asset requires more resources thanjust following a trend. These costs might just offset the profits fundamentalistsmake relative to the losses of chartists.3. If the costs of market entry are lower for chartists than for fundamentalists, itis reasonable to assume that bankrupt chartists are replaced by new chartistsentering the market.The wealth dynamics of the marketmaker is depicted in figure 11. In all four casesmarket makers incur losses at close to constant rates, which appear harder to justifythan those of the chartists because market makers don’t change strategy. However,the market maker could well charge a fee from his trading partners in order to repairhis losses, for example in form of a bid-ask spread as is common practice in financialmarkets.
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ACTA WASAENSIA 52.10.1 Cross-Sectio
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ACTA WASAENSIA 7A5 Matlabcodeforerr
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10 ACTA WASAENSIAIn chapter 5 I sha
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12 ACTA WASAENSIA2 Statistical Prop
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14 ACTA WASAENSIA2.2 Absence of Ser
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ACTA WASAENSIA 17The survival or ta
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ACTA WASAENSIA 19& Scheinkman (1987
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ACTA WASAENSIA 212.6 Long Range Dep
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ACTA WASAENSIA 23A particular class
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26 ACTA WASAENSIAMultiscaling may t
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28 ACTA WASAENSIAHansen (1982) to c
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30 ACTA WASAENSIAThe leverage hypot
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32 ACTA WASAENSIAIn order to aviod
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34 ACTA WASAENSIAon a risk-adjusted
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36 ACTA WASAENSIAplanations for the
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38 ACTA WASAENSIAspeculative prices
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40 ACTA WASAENSIAindex α is howeve
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42 ACTA WASAENSIAThis approach has
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44 ACTA WASAENSIAimplying exponenti
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46 ACTA WASAENSIA3.2.4 Descriptive
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48 ACTA WASAENSIAdivisible version
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50 ACTA WASAENSIAto check their ade
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52 ACTA WASAENSIAvolatility into th
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54 ACTA WASAENSIAfeedback of the co
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56 ACTA WASAENSIAarriving at the fo
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58 ACTA WASAENSIAat iteration k com
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60 ACTA WASAENSIAmultipliers in the
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Page 64 and 65: 64 ACTA WASAENSIAmarkets dominated
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Page 72 and 73: 72 ACTA WASAENSIASethi (1996) exten
Page 74 and 75: 74 ACTA WASAENSIASummation over all
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Page 80 and 81: 80 ACTA WASAENSIAWe wish to obtain
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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
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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
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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
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162 ACTA WASAENSIAMikosch, T. (2003
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164 ACTA WASAENSIAPindyck, R. S. (1
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166 ACTA WASAENSIASethi, R. (1996).
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168 ACTA WASAENSIATesfatsion, L. &
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170 ACTA WASAENSIAAAppendixA1Matlab
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172 ACTA WASAENSIA87 % a3 = 1; %imp
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174 ACTA WASAENSIA181 nmout = max([
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176 ACTA WASAENSIA275276 figure;277
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178 ACTA WASAENSIA45 end464748 % In
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180 ACTA WASAENSIA454647 % Check wh
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182 ACTA WASAENSIAA4Matlab code for
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184 ACTA WASAENSIA888990 % create P
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186 ACTA WASAENSIA4344 nobs = 500;
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188 ACTA WASAENSIAA6Matlab code for
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190 ACTA WASAENSIA9192 for t = 1:T
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192 ACTA WASAENSIA185 fcash = fcash
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194 ACTA WASAENSIA279280281 %Output
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196 ACTA WASAENSIA(4.20), and o(τ
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198 ACTA WASAENSIAas t = n k P ˙
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200 ACTA WASAENSIA nBn˙f2 = v B n
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202 ACTA WASAENSIA f˙1−c1 = v B
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204 ACTA WASAENSIAlocal stability o
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