Skip to main content
Log in

A nonparametric approach to measuring the sensitivity of an asset’s return to the market

  • Research Article
  • Published:
Annals of Finance Aims and scope Submit manuscript

Abstract

In the market model the return on an asset is modeled as a linear function of the return on a market index with slope parameter beta. The coefficient beta is often used as a measure of the sensitivity of the asset’s return to the market and to measure the component of the variance of the return that is explained by the market. However, both of these interpretations require the additional assumption that the error term in the market model has mean 0 conditional on the return on the market index, an assumption that is often difficult to verify in practice. In this paper, a nonparametric version of the market model is proposed that does not require such an assumption. This nonparametric model replaces the beta coefficient of the market model with a “beta curve” describing the relationship between the asset’s return and that of the market locally near a given value of the market return. The proposed model is applied to stock returns, as well as to returns on mutual funds. Corresponding tests of the market model are given and it is shown that the nonparametric model often provides an improvement over the standard parametric market model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Blattberg, R.C., Gonedes, N.J.: A comparison of the stable and student distributions as statistical models for stock prices. J Bus 47, 244–280 (1974)

  • Bradfield, D.: Investment basics XLVI. On estimating the beta coefficient. Invest Anal J 57, 47–53 (2003)

  • Davison, A.C., Hinkley, D.V.: Bootstrap Methods and Their Application. Cambridge: Cambridge University Press (1997)

  • Doksum, K., Samarov, A.: Nonparametric estimation of global functionals and a measure of the explanatory power of covariates in regression. Ann Stat 23, 1443–1473 (1995)

  • Doksum, K., Blyth, S., Bradlow, E., X-L, M., Zhao, H.: Correlation curves as local measures of variance explained by regression. J Am Stat Assoc 89, 571–582 (1994)

  • Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. New York: Chapman and Hall (1993)

  • Fama, E.F.: Risk, return and equilibrium: some clarifying comments. J Finance 23, 29–40 (1968)

  • Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J Finance 25, 383–417 (1970)

  • Fama, E.F.: A note on the market model and the two-parameter model. J Finance 28, 1181–1185 (1973)

  • Fan, J., Gijbels, I.: Local Polynomial Modelling and Its Applications. London: Chapman and Hall (1990)

  • Gray, J.B., French, D.W.: Empirical comparisons of distributional models for stock index returns. J Bus Finance Account 17, 451–459 (1990)

  • Haley, M.R.: Gaussiand logistic adaptations of smoothed safety first. Ann Finance 10, 333–345 (2014)

  • Härdle, W.: Applied Nonparametric Regression. Cambridge: Cambridge University Press (1990)

  • Hastie, T.J., Tibshirani, R.J.: Generalized Additive Models. London: Chapman and Hall (1990)

  • Jensen, M.C.: Random walks: reality or myth—comment. Financ Anal J 23, 77–85 (1967)

  • Lee, T.C.M.: Smoothing spline parameter selection for smoothing splines: a simulation study. Comput Stat Data Anal 42, 138–148 (2003)

  • Lintner, J.: The valuation of risk assets and the selection of risky investments in stock portfolios and capital budget. Rev Econ Stat 47, 13–37 (1952)

  • Markowitz, H.: Portfolio selection. J Finance 7, 77–91 (1952)

  • Markowitz, H.M.: Portfolio Selection: Efficient Diversification of Investments. New York: Wiley (1959)

  • Menardi, G., Lisi, F.: Are performance measures equally stable? Ann Finance 8, 553–570 (2012)

  • Modigliani, F., Pogue, G.A.: An introduction to risk and return: concepts and evidence. Financ Anal J 30, 68–80 (1974)

  • Mossin, J.: Equlibrium in a capital asset market. Econometrica 34, 768–783 (1966)

    Article  Google Scholar 

  • Paparoditis, E., Politis, D.N.: Bootstrap hypothesis testing in regression models. Stat Probab Lett 74, 356–365 (2005)

  • Praetz, P.D.: The distribution of share price changes. J Bus 45, 49–55 (1972)

  • R Core Team: R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing (2015). http://www.R-project.org/

  • Ragozin, D.L.: Error bounds for derivative estimates based on spline smoothing of exact or noisy data. J Approx Theory 37, 335–355 (1983)

  • Ramsey, J., Ripley, B.: pspline: Penalized Smoothing Splines. (2013) http://CRAN.R-project.org/package=pspline, r package version 1.0-16

  • Roll, R.: Bias in fitting the sharpe model to time series data. J Financ Quant Anal 4, 271–289 (1969)

  • Ruppert, D., Wand, M.P., Holst, U., Hössjer, O.: Local polynomial variance-function estimation. Technometrics 39, 262–273 (1997)

    Article  Google Scholar 

  • Sharpe, W.F.: A simplified model for portfolio analysis. Manag Sci 9, 277–293 (1963)

  • Sharpe, W.F.: Capital asset prices: a theory of market equilibrium under conditions of risk. J Finance 19, 425–442 (1964)

  • Sharpe, W.F.: Risk, market sensitivity and diversification. Financ Anal J 28, 74–79 (1972)

  • Simonoff, J.S.: Smoothing Methods in Statistics. New York: Springer (1996)

  • Stapleton, R.C., Subrahmanyam, M.G.: The market model and capital asset pricing theory: a note. J Finance 38, 1637–1642 (1983)

  • Tuzov, N., Viens, F.: Mutual fund performance: false discoveries, bias, and power. Ann Finance 7, 137–169 (2011)

  • Wahba, G.: Spline Models for Observational Data. Philadelphia: Society for Industrial and Applied Mathematicsl (1990)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Thomas A. Severini.

Additional information

This work was supported by National Science Foundation Grant DMS-1308009.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Severini, T.A. A nonparametric approach to measuring the sensitivity of an asset’s return to the market. Ann Finance 12, 179–199 (2016). https://doi.org/10.1007/s10436-016-0277-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10436-016-0277-5

Keywords

JEL Classification

Navigation