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504 Bibliographie BACHELIER, L. (1900) : “Théorie de la spéculation”, Annales Scientifiques de l’Ecole Normale Supérieure 17, 21–86. BACRY, E., J. DELOUR ET J. F. MUZY (2001) : “Multifractal random walk”, Physical Review E 64(26103). BACRY, E. ET J. F. MUZY (2002) : “Log-infinitely divisible multifractal processes”, Document de Travail 0207094, Cond-mat. BASLE COMMITTEE ON BANKING SUPERVISION (1996) : “Amendement to the Capital Accord to Incorporate Market Risks”. BASLE COMMITTEE ON BANKING SUPERVISION (2001) : “The New Basel Capital Accord”. BAVIERA, R., L. BIFERALE, R. N. MANTEGNA ET A. VULPIANI (1998) : “Transient multiaffine behaviors in ARCH and GARCH processes”, International Workshop on Econophysics and Statistical Finance, Italy. BECK, U. (2001) : La société du risque, Aubier. BERNOULLI, D. (1738) : “Specimen theoriae novae de mensura sortis”, Commentarii Academiae Scientiarum Imperialis Petropolitanae . BINGHAM, N. H., C. M. GOLDIE ET J. L. TEUGEL (1987) : Regular Variation, Cambridge University Press. BLACK, F. (1976) : “Studies of stock price volatility changes”, Proceeding of the Business and Economic Statistics Section pp. 177–181. BLACK, F. ET M. SCHOLES (1973) : “The pricing of options and corporate liabilities”, Journal of Political Economy 81, 637–653. BLANCHARD, O. J. (1979) : “Speculative bubble, crashes and rational expectations”, Economics Letters 3, 387–396. BLANCHARD, O. J. ET M. W. WATSON (1982) : “Bubbles, rational expectations and speculative markets”, in Watchel, P. (ed.), Crisis in Economic and Financial Structure : Bubles, Bursts and Shocks, Lexington Books, Lexington. BLUM, P., A. DIAS ET P. EMBRECHTS (2002) : “The ART of dependence modelling : the latest advances in correlation analysis”, in Lane, M. (ed.), Alternative Risk Strategies, Risk Books, London, pp. 339–356. BOLLERSLEV, T. (1986) : “Generalized autoregressive conditional heteroskdasticity”, Journal of Econometrics 31, 307–327. BOLLERSLEV, T., R. F. ENGLE ET D. B. NELSON (1994) : “ARCH models”, in Engle, R. F. et D. Mc- Fadden (eds.), Handbook of Econometrics, Vol. IV, Elsevier, pp. 2959–3038. BOLLERSLEV, T., R. Y. CHOU ET K. F. KRONER (1992) : “ARCH modeling in finance : A review of the theory and empirical evidence”, Journal of Econometrics 52, 5–59. BOUCHAUD, J. P., A. MATACZ ET M. POTTERS (2001) : “The leverage effect in financial markets : retarded volatility and market panic”, Physical Review Letters 87(228701). BOUCHAUD, J. P., D. SORNETTE, C. WALTER ET J. P. AGUILAR (1998) : “Taming large events : Optimal portfolio theory for strongly fluctuating assets”, International Journal of Theoretical and Applied Finance 1, 25–41. BOUCHAUD, J. P., M. POTTERS ET M. MEYER (2000) : “Apparent multifractality in financial time series”, European Physical Journal B 13, 595–599. BOUCHAUD, J. P. ET M. POTTERS (2000) : Theory of Financial Risks : From Statistical Physics to Risk Management, Cambridge University Press.
Bibliographie 505 BOUCHAUD, J. P. ET R. CONT (1998) : “A Langevin approach to stock market fluctuations and crashes”, European Physical Journal B 6, 543–550. BOUYÉ, E., V. DURRLEMAN, A. NIKEGHBALI, G. RIBOULET ET T. RONCALLI (2000) : “Copulas for finance : A reading guide and some applications”, Document de Travail, Groupe de Recherche Opérationelle, Crédit Lyonnais. BRIEMAN, L. (1960) : “Investment policies for expanding businesses optimal in a long run sense”, Naval Research Logistics Quaterly 7, 647–651. BROCK, W. A. (1993) : “Pathways to randomness in the economy : Emergent nonlinearity and chaos in economics and finance”, Estudios Economicos 8. BROCK, W. A. ET B. LEBARON (1996) : “A dynamic structural model for stock return volatility and trading volume”, Review of Economics and Statistics 78, 94–110. BROCK, W. A. ET C. H. HOMMES (1997) : “Rational route to randomness”, Econometrica 65, 1059– 1095. BROWN, P., D. WALSH ET A. YUEN (1997) : “The interaction between order imbalance and stock price”, Pacific-Basin Finance Journal 5, 539–557. BROZE, L., C. GOURIÉROUX ET A. SZAFARZ (1990) : Reduced Forms of Rational Expectations Models, Harwood Academic Press. BURDA, Z., J. JURKIEWICZ, M. NOWAK, G. PAPP ET I. ZAHED (2001a) : “Free Lévy matrices and financial correlations”, Document de Travail 0103109, Cond-mat. BURDA, Z., J. JURKIEWICZ, M. NOWAK, G. PAPP ET I. ZAHED (2001b) : “Lévy matrices and financial covariances”, Document de Travail 0103108, Cond-mat. BURDA, Z., R. JANIK, J. JURKIEWICZ, M. NOWAK, G. PAPP ET I. ZAHED (2002) : “Free random Lévy matrices”, Physical Review. E 65(011106). CAMPBELL, J., A. W. LO ET C. MACKINLAY (1997) : The Econometrics of Financial Markets, Princetown University press. CASADESUS-MASANELL, R., P. KLIBANOFF ET E. OZDENOREN (2000) : “Maxmin expected utility through statewise combinations”, Economics Letters 66, 49–54. CAVES, C. M., C. A. FLUCHS ET R. SCHACK (2002) : “Quantum probabilities as Bayesian probabilities”, Physical Review A 65(022305). CHABAANE, A., E. DUCLOS, J. P. LAURENT, Y. MALEVERGNE ET F. TURPIN (2002) : “Looking for efficient portofolios : An empirical investigation”, Document de Travail. CHALLET, D., A. CHESSA, M. MARSILI ET Y. C. ZHANG (2001) : “From minority games to real financial makets”, Quantitative Finance 1, 168–176. CHALLET, D. ET Y. C. ZHANG (1997) : “Emergence of Cooperation and Organization in an Evolutionary Game”, Physica A 246, 407–418. CHAN, K. ET W.-M. FONG (2000) : “Trade size, order imbalance, and the volatility-volume relation”, Journal of Financial Economics 57, 247–273. CHATEAUNEUF, A. (1991) : “On the use of capacities in modeling uncertainty aversion and risk aversion”, Journal of Mathematical Economics 20, 343–369. CHEKHLOV, A., S. URYASEV ET M. ZABARANKIN (2000) : “Portfolio Optimization with Drawdown Constraints”, Document de Travail, ISE Dept., Univ. of Florida. CHERUBINI, U. ET E. LUCIANO (2000) : “Multivariate option pricing with copula”, Document de Travail, SSRN.
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UNIVERSITE DE NICE-SOPHIA ANTIPOLIS
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4 Table des matières 2.2.2 Bulles
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6 Table des matières 7.3.2 Tests n
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8 Table des matières 12.1.1 Distri
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10 Table des matières Références
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12 m’ont souvent été d’un gra
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14 Introduction pour espérer se co
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16 Introduction les distributions e
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18 Introduction
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Chapitre 1 Faits stylisés des rent
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1.1. Rappel des faits stylisés 23
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1.1. Rappel des faits stylisés 25
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1.2. De la difficulté de représen
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1.3. Modélisation des propriétés
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Chapitre 2 Modèles phénoménologi
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2.1. Bulles rationnelles multi-dime
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2.2. Des bulles rationnelles aux kr
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Chapitre 3 Distributions exponentie
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Empirical Distributions of Log-Retu
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concerning tail fatness can be form
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To fit our two data sets, section 4
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properties, stressing the problems
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Another problem lies in the determi
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In order to obtain a process with S
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and Sornette (1999) for instance. W
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1. The Pareto distribution: 79 Fu(x
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empirical analog FN(x), estimated f
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have been chosen to converge to 1 a
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5 Comparison of the descriptive pow
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ased on the fact that the quantity
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tail (positive or negative) of the
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Equation (46) depends on c only and
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B Minimum Anderson-Darling Estimato
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C Local exponent In this Appendix,
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D Testing non-nested hypotheses wit
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where α = (c ∗ ,d ∗ ). It can
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where E0[·] denotes the expectatio
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References Andersen, J.V. and D. So
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Gouriéroux C. and A. Monfort, 1994
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Rubinstein, M., 1973, The fundament
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(a) Independent Data Stretched-Expo
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(a) Dow Jones Positive Tail Negativ
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Nasdaq Dow Jones Pos. Tail Neg. Tai
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Dow Jones positive tail Dow Jones n
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Variation coefficient Variation coe
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Mean excess function Mean excess fu
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Hill‘s estimate b u Hill‘s esti
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Wilks statistic (doubled log−like
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Tail 1−F(x) and parameter b Tail
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1.4 1.2 1 0.8 0.6 0.4 0.2 0 1 2 3 4
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Chapitre 4 Relaxation de la volatil
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Volatility Fingerprints of Large Sh
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2 Long-range memory and distinction
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Figure 2: Measuring the conditional
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the system to the accumulation of m
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Appendix C: “Conditional response
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Baillie, R.T., 1996, Long memory pr
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Chapitre 5 Approche comportementale
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5.1. Prix d’un actif et excès de
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5.2. Modèles d’opinion contre mo
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5.4. Conséquences des phénomènes
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5.5. Conclusion 157 ce qui induit u
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Chapitre 6 Comportements mimétique
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RESEARCH PAPER Q UANTITATIVE F INAN
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A Corcos et al Q UANTITATIVE F INAN
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Deuxième partie Etude des proprié
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182 7. Etude de la dépendance à l
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192 8. Tests de copule gaussienne
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194 8. Tests de copule gaussienne 1
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200 8. Tests de copule gaussienne t
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202 8. Tests de copule gaussienne T
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204 8. Tests de copule gaussienne a
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206 8. Tests de copule gaussienne c
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208 8. Tests de copule gaussienne t
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210 8. Tests de copule gaussienne (
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212 8. Tests de copule gaussienne R
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228 8. Tests de copule gaussienne f
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236 8. Tests de copule gaussienne T
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238 9. Mesure de la dépendance ext
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Chapitre 10 La mesure du risque Dan
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10.1. La théorie de l’utilité 3
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10.2. Les mesures de risque cohére
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10.3. Les mesures de fluctuations 3
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10.4. Conclusion 361 et de manière
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Chapitre 11 Portefeuilles optimaux
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11.2. Prise en compte des grands ri
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11.3. Equilibre de marché 367 s’
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11.5. Annexe 369 Collective Origin
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11.5. Annexe 371 point λ = 1. Thus
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Chapitre 12 Gestion des risques gra
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12.1. Comprendre et gérer les risq
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12.2. Minimiser l’impact des gran
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Chapitre 13 Gestion de portefeuille
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VaR-Efficient Portfolios for a Clas
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dependence: from independence to co
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given a series of return {rt}t foll
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eads cV (u1, · · · , un) = 1
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THEOREM 4 (TAIL EQUIVALENCE FOR A S
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Independent Assets Comonotonic Asse
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3.2 Typical recurrence time of larg
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and finally w ∗ i = χ−1 i j
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where the ˆwi’s are solution of
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Choosing x > y > Aɛ, and applying
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Expanding fi(xi) around x ∗ i yie
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for all h ∈ AC. Thus, integrating
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B Asymptotic distribution of the su
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This yields × h+ Sc−2 − 2 dh
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References Acerbi, A. and D. Tasche
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Malevergne, Y. and D. Sornette, 200
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ln(T/T 0 ) T Figure 2: Logarithm
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Chapitre 14 Gestion de Portefeuille
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Multi-Moments Method for Portfolio
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choosen risk measure. Section 5 pre
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The variance of the return X of an
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This property is verified for all c
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µn=α of order n = 2, 4, 6 and 8.
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these two assets or portfolios. The
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Due to the homogeneity property of
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invested in the risky fund depends
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C. Then, if g1(X1), · · · , gn(X
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Differentiating with respect to x1,
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7.2 Transformation of the modified
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In the sequel, we will first evalua
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8.3 Non-symmetric assets In the cas
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y the variance will then increase).
Page 453 and 454: A Description of the data set We ha
Page 455 and 456: thus and finally 1 λ1 n−1 = w
Page 457 and 458: C Composition of the market portfol
Page 459 and 460: D Generalized capital asset princin
Page 461 and 462: This brings us back to the problem
Page 463 and 464: F.2 General case We now consider a
Page 465 and 466: Harvey, C.R. and A. Siddique, 2000,
Page 467 and 468: µ µ2 1/2 µ4 1/4 µ6 1/6 µ8 1/8
Page 469 and 470: Mean (10 −3 ) Variance (10 −3 )
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Page 473 and 474: w i w i 0.2 0.15 0.1 0.05 Mean−μ
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Page 477 and 478: Empirical Cumulative Distribution 1
Page 479 and 480: Z 2 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0
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Page 485 and 486: 10 0 10 −1 10 0 10 −1 10 −1 1
Page 487 and 488: κ 22 20 18 16 14 12 10 8 6 4 2 Exc
Page 489 and 490: Return μ 0.12 0.11 0.1 0.09 0.08 0
Page 491 and 492: Conclusions et Perspectives L’obj
Page 493 and 494: Conclusion 493 en univers gaussien
Page 495 and 496: Annexe A Evaluation de la conduite
Page 497 and 498: A.2. Eléments de contexte 497 bora
Page 499 and 500: A.4. Compétences acquises et ensei
Page 501 and 502: A.5. Conclusion 501 de la recherche
Page 503: Bibliographie ACERBI, C. (2002) :
Page 507 and 508: Bibliographie 507 DE FINETTI, B. (1
Page 509 and 510: Bibliographie 509 GRANGER, C. W. ET
Page 511 and 512: Bibliographie 511 LILLO, F. ET R. N
Page 513 and 514: Bibliographie 513 PATTON, A. (2001)
Page 515: Bibliographie 515 SUSMEL, R. (1996)
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