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2013 | 1 | 94-110
Tytuł artykułu

Dependence of Stock Returns in Bull and Bear Markets

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Despite of its many shortcomings, Pearson’s rho is often used as an association measure for stock returns. A conditional version of Spearman’s rho is suggested as an alternative measure of association. This approach is purely nonparametric and avoids any kind of model misspecification. We derive hypothesis tests for the conditional rank-correlation coefficients particularly arising in bull and bear markets and study their finite-sample performance by Monte Carlo simulation. Further, the daily returns on stocks contained in the German stock index DAX 30 are analyzed. The empirical study reveals significant differences in the dependence of stock returns in bull and bear markets.
Wydawca
Czasopismo
Rocznik
Tom
1
Strony
94-110
Opis fizyczny
Daty
otrzymano
2013-10-17
zaakceptowano
2013-12-20
online
2013-12-31
Twórcy
  • Chair for Applied Stochastics and Risk Management,
    Helmut Schmidt University, Hamburg, Germany, frahm@hsu-hh.de
  • University of Cologne, Germany
Bibliografia
  • [1] A. Ang and J. Chen (2002), ‘Asymmetric correlations of equity portfolios’, J. Financ. Econ. 63, pp. 443–494.[Crossref]
  • [2] U. Cherubini, E. Luciano, and W. Vecchiato (2004), Copula Methods in Finance, John Wiley.
  • [3] J. Dobric and F. Schmid (2005), ‘Nonparametric estimation of the lower tail dependence λL in bivariate copulas’, J.Appl. Stat. 32, pp. 387–407.[Crossref]
  • [4] P. Doukhan, J.D. Fermanian, and G. Lang (2009), ‘An empirical central limit theorem with applications to copulasunder weak dependence’, Stat. Inference Stoch. Process. 12, pp. 65–87.[Crossref]
  • [5] P. Embrechts, A.J. McNeil, and D. Straumann (2002), ‘Correlation and dependence in risk management: propertiesand pitfalls’, in: M. Dempster, ed., ‘Risk Management: Value at Risk and Beyond’, Cambridge University Press.
  • [6] C.B. Erb, C.R. Harvey, and T.E. Viskanta (1994), ‘Forecasting international equity correlations’, Financ. Anal. J. 50,pp. 32–45.[Crossref]
  • [7] I. Fortin and C. Kuzmics (2002), ‘Tail-dependence in stock return pairs’, Int. J. Intell. Syst. Account. Finance Manag.11, pp. 89–107.[Crossref]
  • [8] G. Frahm, M. Junker, and R. Schmidt (2005), ‘Estimating the tail-dependence coefficient: properties and pitfalls’,Insurance Math. Econom. 37, pp. 80–100.[Crossref]
  • [9] P. Hall, J.L. Horowitz, and J. Bing-Yi (1995), ‘On blocking rules for the bootstrap with dependent data’, Biometrika82, pp. 561–574.
  • [10] Y. Hong, J. Tu, and G. Zhou (2007), ‘Asymmetries in stock returns: statistical tests and economic evaluation’, Rev.Financ. Stud. 20, pp. 1547–1581.[Crossref]
  • [11] H. Hult and F. Lindskog (2002), ‘Multivariate extremes, aggregation and dependence in elliptical distributions’, Adv.in Appl. Probab. 34, pp. 587–608.
  • [12] P. Jaworski and M. Pitera (2013), ‘On spatial contagion and multivariate GARCH models’, Appl. Stoch. Models Bus.Ind. DOI: 10.1002/asmb.1977.[Crossref]
  • [13] H. Joe (1997), Multivariate Models and Dependence Concepts, Chapman & Hall.
  • [14] M. Junker and A. May (2005), ‘Measurement of aggregate risk with copulas’, Econom. J. 8, pp. 428–454.
  • [15] A. Juri and M. Wüthrich (2002), ‘Copula convergence theorems for tail events’, Insurance Math. Econom. 30, pp.405–420.[Crossref]
  • [16] H.R. Künsch (1989), ‘The jackknife and the bootstrap for general stationary observations’, Ann. Statist. 17, pp.1217–1241.[Crossref]
  • [17] A.J. McNeil, R. Frey, and P. Embrechts (2005), Quantitative Risk Management, Princeton University Press.
  • [18] R.B. Nelsen (2006), An Introduction to Copulas, Springer, second edition.
  • [19] A.J. Patton (2004), ‘On the out-of-sample importance of skewness and asymmetric dependence for asset allocation’,J. Financ. Econometrics 2, pp. 130–168.[Crossref]
  • [20] D.N. Politis (2003), ‘The impact of bootstrap methods on time series analysis’, Statist. Sci. 18, pp. 219–230.[Crossref]
  • [21] J.P. Romano and M. Wolf (2005), ‘Stepwise multiple testing as formalized data snooping’, Econometrica 73, pp. 1237–1282.
  • [22] F. Schmid and R. Schmidt (2006), ‘Multivariate extensions of Spearman’s rho and related statistics’, Statist. Probab.Lett. 77, pp. 407–416.[WoS]
  • [23] P. Silvapulle and C.W.J. Granger (2001), ‘Large returns, conditional correlation and portfolio diversification: avalue-at-risk approach’, Quant. Finance 1, pp. 542–551.[Crossref]
  • [24] A. Sklar (1959), ‘Fonctions de répartition à n dimensions et leurs marges’, Publ. Inst. Statist. Univ. Paris 8, 229–231.
  • [25] A.W. van der Vaart (1998), Asymptotic Statistics, Cambridge University Press.
  • [26] B. Vaz de Melo Mendes (2005), ‘Asymmetric extreme interdependence in emerging equity markets’, Appl. Stoch.Models Bus. Ind. 21, pp. 483–498.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.doi-10_2478_demo-2013-0005
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