- Page 6 and 7: 6 ACTA WASAENSIA4.2 Dynamic Models
- Page 8: 8 ACTA WASAENSIAAbstractPape, Bernd
- Page 11 and 12: ACTA WASAENSIA 11The traders in my
- Page 13 and 14: ACTA WASAENSIA 13somewhat. For exam
- Page 16 and 17: 16 ACTA WASAENSIA2.4 Heavy TailsThe
- Page 18 and 19: 18 ACTA WASAENSIAwhere x (i) denote
- Page 20 and 21: 20 ACTA WASAENSIAstocks.Engle (1982
- Page 22 and 23: 22 ACTA WASAENSIAThe parameters a a
- Page 24: 24 ACTA WASAENSIAinferences about s
- Page 27 and 28: ACTA WASAENSIA 27heaves up or down
- Page 29 and 30: ACTA WASAENSIA 29rather than the vo
- Page 31 and 32: ACTA WASAENSIA 31One should keep in
- Page 33 and 34: ACTA WASAENSIA 33asymmetric GARCH a
- Page 35 and 36: ACTA WASAENSIA 35size effect has la
- Page 37 and 38: ACTA WASAENSIA 373 The Search for t
- Page 39 and 40: ACTA WASAENSIA 39of returns would t
- Page 41 and 42: ACTA WASAENSIA 4113:r τ (t) = μ
- Page 43 and 44: ACTA WASAENSIA 43is, it does not ne
- Page 45 and 46: ACTA WASAENSIA 45generalize the VG
- Page 47 and 48: ACTA WASAENSIA 47to the fact that b
- Page 49 and 50: ACTA WASAENSIA 49TLF on daily retur
- Page 51 and 52: ACTA WASAENSIA 513.3 Modelling Time
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ACTA WASAENSIA 53and to impose a su
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in r 2 t : 96 {1 − α(L) − β(L
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ACTA WASAENSIA 573.4 Multifractal M
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ACTA WASAENSIA 59that volatility de
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4 Behavioral ExplanationsACTA WASAE
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ACTA WASAENSIA 63speculation (trade
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ACTA WASAENSIA 65Their view was sup
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ACTA WASAENSIA 67self-contained dif
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ACTA WASAENSIA 694.2 Dynamic Models
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ACTA WASAENSIA 71Chartists use a te
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ACTA WASAENSIA 73all traders, which
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ACTA WASAENSIA 75rather than genera
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ACTA WASAENSIA 77Iori (2002) comes
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ACTA WASAENSIA 79dependent probabil
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ACTA WASAENSIA 81If K is at least t
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ACTA WASAENSIA 83typesasinBeja&Gold
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ACTA WASAENSIA 854.3.1 The ModelThe
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ACTA WASAENSIA 87wherewehavedropped
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ACTA WASAENSIA 89by Lux & Marchesi,
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ACTA WASAENSIA 91of occurences of p
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ACTA WASAENSIA 930.4Logreturns Para
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ACTA WASAENSIA 950.8Chartist Index
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ACTA WASAENSIA 97Table 2. Median es
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ACTA WASAENSIA 99200Parameter Set I
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ACTA WASAENSIA 1011Parameter Set I
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ACTA WASAENSIA 103Table 6. Estimate
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ACTA WASAENSIA 10513Parameter Set I
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ACTA WASAENSIA 107Table 10. Results
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ACTA WASAENSIA 109& Joshi (2002) of
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ACTA WASAENSIA 111Table 12. Results
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ACTA WASAENSIA 113Consider next the
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ACTA WASAENSIA 1151000Aggregate Wea
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ACTA WASAENSIA 1175 Asset Allocatio
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ACTA WASAENSIA 119The condition dE
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ACTA WASAENSIA 121n B Bond Investor
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ACTA WASAENSIA 123for the populatio
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ACTA WASAENSIA 1251. α B ≤ 1,2.
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ACTA WASAENSIA 127Table 15. Probabi
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ACTA WASAENSIA 129Table 16. Summary
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ACTA WASAENSIA 1310.3Logreturns Equ
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ACTA WASAENSIA 1331500Traders Cash1
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ACTA WASAENSIA 1350.4Stock 10.4Stoc
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ACTA WASAENSIA 1376 ConclusionI hav
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ACTA WASAENSIA 139Ang, A. & J. Chen
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ACTA WASAENSIA 141Barndorff-Nielsen
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ACTA WASAENSIA 143Box, G. E. P. & G
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ACTA WASAENSIA 145Cheung, Y.-W., M.
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ACTA WASAENSIA 147Degiannakis, S. &
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ACTA WASAENSIA 149Fama,E.F.(1976).F
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ACTA WASAENSIA 151Friedman, B. M. &
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ACTA WASAENSIA 153Gray, J. B. & D.
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ACTA WASAENSIA 155Hsu, D.-A., R. B.
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ACTA WASAENSIA 157Kirman,A.P.&G.Tey
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ACTA WASAENSIA 159Loretan, M. & W.
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ACTA WASAENSIA 161Mandelbrot, B. &
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ACTA WASAENSIA 163Muzy, J. F., J. D
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ACTA WASAENSIA 165Rosenberg, B., K.
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ACTA WASAENSIA 167Stǎricǎ, C. & T
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ACTA WASAENSIA 169Weidlich, W. & G.
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ACTA WASAENSIA 17140 beta = 6; %rea
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ACTA WASAENSIA 173134135136 % Initi
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ACTA WASAENSIA 175228229 end % end
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ACTA WASAENSIA 177A2Matlab code for
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ACTA WASAENSIA 179A3Matlab code for
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ACTA WASAENSIA 18192 % Calculate #
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ACTA WASAENSIA 18343 %% Check wheth
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ACTA WASAENSIA 185A5Matlab code for
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ACTA WASAENSIA 18788 ltnc975 = (tau
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ACTA WASAENSIA 18944 nf=zeros(1,2);
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ACTA WASAENSIA 191138 dnmat(5,2) =
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ACTA WASAENSIA 193232233234 % Calcu
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ACTA WASAENSIA 195A7Derivation of m
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ACTA WASAENSIA 197such thatT + j T
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ACTA WASAENSIA 199A8ProofofProposit
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ACTA WASAENSIA 201A9ProofofProposit
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ACTA WASAENSIA 203read for the popu
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ACTA WASAENSIA 205which is again sm