Abstract
In this paper we consider the capital asset pricing model under the multivariate normal distribution for modeling asset returns. We develop and implement local influence diagnostic techniques not based on likelihood displacement. The main interest is to consider the test of mean–variance efficiency of a given portfolio as objective function. Sensitivity of maximum likelihood estimators of slopes is also considered. For this purpose, first and second-order local influence measures are calculated under a case weights perturbation scheme. In particular, for our objective functions, which have nonzero first derivative at the critical point, the proposed second-order measures are scale invariant, unlike the normal curvature. The performance of the proposed measures is illustrated by using two real data sets as well as a simulation study and a simulated data set. Empirical results seem to indicate that the Wald test statistic is less sensitive to atypical returns than maximum likelihood estimators of the slopes. By the other hand, there is some evidence about the convenience of jointly using first and second-order influence measures, to detect months/days with outlying returns, principally in Wald test statistic.
Similar content being viewed by others
References
Amenc N, Le Sourd V (2003) Portfolio theory and performance analysis. Wiley, New York
Bartholdy J, Peare P (2003) Unbiased estimation of expected return using. Int Rev Financ Anal 12:69–81
Broquet C, Cobbaut R, Gillet R, van den Berg A (2004) Gestion de Portefeuille, 4th edn. De Boeck Université, Bruxelles
Cademartori D, Romo C, Campos R, Galea M (2003) Robust estimation of systematic risk using the t distribution in the Chilean stock markets. Appl Econ Lett 10:447–453
Cadigan NG, Farrell PJ (2002) Generalized local influence with applications to fish stock cohort analysis. Appl Stat 51:469–483
Campbell J, Lo A, MacKinlay A (1997) Econometrics of financial markets. Princeton University Press, Princeton
Chatterjee S, Hadi AS (1988) Sensitivity analysis in linear regression. Wiley, New York
Chen F, Zhu H, Lee S (2009) Perturbation selection and local influence analysis for nonlinear structural equation model. Psychometrika 74:493–516
Chen F, Zhu H, Song X, Lee S (2010) Perturbation selection and local influence analysis for generalized linear mixed models. J Comput Gr Stat 19:826–842
Cook RD (1986) Assessment of local influence. J R Stat Soc B 48:133–169
Cook RD, Weisberg S (1982) Residuals and influence in regression. Chapman and Hall, London
Díaz-García JA, Galea M, Leiva-Sánchez V (2003) Influence diagnostics for elliptical multivariate regression models. Commun Stat Theory Methods 32:625–641
Elsas R, El-Shaer M, Theissen E (2003) Beta and returns revisited evidence from the German stock market. J Int Financ Mark Inst Money 13:1–18
Escobar E, Meeker W (1992) Assessing influence in regression analysis with censored data. Biometrics 48:507–528
Fama E, French K (1992) The cross-section of expected stock returns. J Financ 47:427–465
Fung WK, Kwan CW (1997) A note on local influence based on normal curvature. J R Stat Soc Ser B 59:839–843
Galea M, Bolfarine H, de Castro M (2002) Local influence in comparative calibration models. Biom J 44:59–81
Galea M, Díaz-García J, Vilca F (2008) Influence diagnostics in the capital asset pricing model under elliptical distributions. J Appl Stat 35(2):179–192
Galea M, Paula GA, Bolfarine H (1997) Local influence in elliptical linear regression models. Statistician 46:71–79
Gibbons M, Ross S, Shanken J (1989) A test of the efficiency of a given portfolio. Econometrica 57:1121–1153
Giménez P, Galea M (2013) Influence measures on corrected score estimators in functional heteroscedastic measurement error models. J Multivar Anal 114:1–15
Hadi AS, Nyquist H (1999) Frechet distance as a tool for diagnosing multivariate data. Linear Algebra Appl 289:183–201
Lawrance AJ (1988) Regression transformation diagnostics using local influence. J Am Stat Assoc 83:1067–1072
Lee SY, Wang SJ (1996) Sensitivity analysis of structural equation models. Psychometrika 61:93–108
Lesaffre E, Verbeke G (1998) Local influence in linear mixed models. Biometrics 54:570–582
Levy H (2012) The capital asset pricing model in the 21st century: analytical, empirical, and behavioral perspectives. Cambridge University Press, New York
Lintner J (1965) The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. Rev Econ Stat 41:13–37
Liu S (2000) On local influence for elliptical linear models. Stat Pap 41:211–224
Liu S (2002) Local influence in multivariate elliptical linear regression models. Linear Algebra Appl 354:159–174
Magnus JR, Neudecker H (1979) The commutation matrix: some properties and applications. Ann Stat 7:381–394
Mossin J (1966) Equilibrium in capital asset market. Econometrica 35:768–783
Murray MK, Rice JW (1993) Differential geometry and statistics. Chapman and Hall, London
Poon WY, Poon YS (1999) Conformal normal curvature and assessment of local influence. J R Stat Soc Ser B 61:51–61
R Development Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org/
Ruppert D, Matteson D (2015) Statistics and data analysis for financial rngineering, with R examples, 2nd edn. Springer, New York
Sharpe W (1964) Capital asset prices: a theory of markets equilibrium under conditions of risk. J Financ 19:425–442
Shi L (1997) Local influence in principal components analysis. Biometrika 84:175–186
Shi X, Zhu H, Ibrahim JG (2009) Local influence for generalized linear models with missing covariates. Biometrics 65:1164–1174
Thomas W, Cook RD (1990) Assessing influence on predictions from generalized linear models. Technometrics 32:59–65
Uribe-Opazo M, Borssoi J, Galea M (2012) Influence diagnostics in Gaussian spatial linear models. J Appl Stat 39(3):615–630
van der Hart J, Slagter E, van Dijk D (2003) Stock selection strategies in emerging markets. J Empir Financ 10:105–132
Wu X, Luo Z (1993) Second-order approach to local influence. J R Stat Soc B 55:929–939
Zhao Y, Lee AH (1998) Influence diagnostics for simultaneous equations models. Aust N Z J Stat 40:345–357
Zhu HT, Lee SY (2001) Local influence for incomplete data models. J R Stat Soc B 63:111–126
Zhu HT, Ibrahim JG, Lee S, Zhang H (2007) Perturbation selection and influence measures in local influence analysis. Ann Stat 35:2565–2588
Zhu F, Shi L, Liu S (2015) Influence diagnostics in log-linear integer-valued GARCH models. AStA Adv Stat Anal 99:311–335
Zhu F, Liu S, Shi L (2016) Local influence analysis for Poisson autoregression with an application to stock transaction data. Stat Neerl 70:4–25
Acknowledgments
We would like to thank the Associate Editor and two referees for their helpful comments and suggestions, leading to improvement of the paper. Also, we acknowledge the partial financial support from Projects Fondecyt 1110318 and 1150325, Chile.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Galea, M., Giménez, P. Local influence diagnostics for the test of mean–variance efficiency and systematic risks in the capital asset pricing model. Stat Papers 60, 293–312 (2019). https://doi.org/10.1007/s00362-016-0838-8
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00362-016-0838-8
Keywords
- Local influence diagnostics
- First and second-order approaches
- Capital asset pricing model
- Wald test statistic