Skip to main content
Log in

An asset return model capturing stylized facts

  • Published:
Mathematics and Financial Economics Aims and scope Submit manuscript

Abstract

In this paper, we seek a model for asset returns which reproduces several well-documented stylized facts:1. log returns are not Gaussian; 2. absolute log returns are serially correlated, but the log returns are not; 3. the Taylor effect. There are many attempts to deal with the first, using various log-Lévy models for the asset; some of these are successful in fitting the unconditional distribution of log returns, but cannot of course reproduce the second stylized fact. We propose to model the returns with a hidden two-state Markovian regime (as in J Appl Econ 13:217–244, 1998), conditional on the value of which the returns have different distributions. A key observation is that if the means of the returns in the different regimes are the same, then the log returns are automatically uncorrelated, so we fit to index data under this restriction. By choosing symmetric hyperbolic distributions for the conditional returns, we are able to fit well the unconditional distributions, the autocovariances of absolute returns and the Taylor effect. Moreover, we find that a common regime model explains simultaneously these statistics for the S&P500, FTSE, DAX, Nikkei and CAC40. Implications for investment and option pricing are discussed.

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.

Similar content being viewed by others

References

  1. Alizadeh A., Nomikos N.: A markov regime switching approach for hedging stock indices. J. Futures Mark. 24(7), 649–674 (2004)

    Article  Google Scholar 

  2. Barndorff-Nielsen O.E.: Normal inverse gaussian distributions and stochastic volatility modelling. Scand. J. Stat. 24(1), 1–13 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  3. Barndorff-Nielsen O.E., Shephard N.: Non-gaussian ornstein-uhlenbeck-based models and some of their uses in financial economics. J. R. Stat. Soc.: Ser. B (Stat. Methodol.) 63(2), 167–241 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  4. Barndorff-Nielsen O.E., Stelzer R.: Absolute moments of generalized hyperbolic distributions and approximate scaling of normal inverse gaussian lévy processes. Scand. J. Stat. 32(4), 617–637 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  5. Barndorff-Nielsen O.E., Mikosch T., Resnick S.: Lévy Processes-Theory and Applications. Birkhauser, Boston (2000)

    Google Scholar 

  6. Bollerslev T., Chou R.Y., Kroner K.F.: Arch modeling in finance: a review of the theory and empirical evidence. J. Econom. 52(1–2), 5–59 (1992)

    Article  MATH  Google Scholar 

  7. Campbell J., Lo A., Mackinlay C.: The Econometrics of Financial Markets. Princeton University Press, Princeton, NJ (1997)

    MATH  Google Scholar 

  8. Carr P., Geman H., Madan D.B., Yor M.: The fine structure of asset returns: an empirical investigation. J. Bus. 75(2), 305–332 (2002)

    Article  Google Scholar 

  9. Chakravarti I.M., Laha R.G., Roy J.: Handbook of Methods of Applied Statistics. Wiley, New York (1967)

    MATH  Google Scholar 

  10. Cont R.: Empirical properties of asset returns: stylized facts and statistical issues. Quant. Financ. 1, 223–236 (2001)

    Article  Google Scholar 

  11. Eberlein E., Keller U.: Hyperbolic distributions in finance. Bernoulli 1(3), 281–299 (1995)

    Article  MATH  Google Scholar 

  12. Fan J., Li R.: Variable selection via nonconcave penalized likelihood and its oracle properties. J. Am. Stat. Assoc. 96, 1348–1360 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  13. Granger C.W.J., Ding Z.: Some properties of absolute return: an alternative measure of risk. Annal. Econ. Stat. 40, 67–91 (1995)

    MathSciNet  Google Scholar 

  14. Granger, C.W.J., Spear, S., Ding, Z.: Stylized facts on the temporal and distributional properties of absolute returns: an update. Statistics and finance: an interface. In: Proceedings of the Hong Kong International Workshop on Statistics in Finance (2000)

  15. Hamilton J.D.: Rational expectations econometric analysis of changes in regime: an investigation of the term structure of interest rates. J. Econ. Dynam. Control 12, 385–423 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  16. Hamiltion J.D.: A new approch to the economic analysis of nonstationary time series and the business cycle. Econometrica 57(2), 357–384 (1989)

    Article  MathSciNet  Google Scholar 

  17. Lindgren G.: Markov regime models for mixed distributions and switching regressions. Scand. J. Stat. 5(2), 81–91 (1978)

    MathSciNet  MATH  Google Scholar 

  18. Madan D.B., Seneta E.: Chebyshev polynomial approximations and characteristic function estimation. J. R. Stat. Soc. Ser. B (Methodol.) 49(2), 163–169 (1987)

    MathSciNet  Google Scholar 

  19. Madan D.B., Seneta E.: The variance gamma (v.g.) model for share market returns. J. Bus. 63(4), 511–524 (1990)

    Article  Google Scholar 

  20. Maddala G., Rao C.R.: Handbook Of Statistics, Vol. 14: Statistical Methods In Finance. Elsevier Science Ltd, Amsterdam (1997)

    Google Scholar 

  21. Marsh I.W.: High-frequency markov switching models in the foreign exchange market. J. Forecast. 19, 123–134 (2000)

    Article  Google Scholar 

  22. Pagan A.: The econometrics of financial markets. J. Empir. Financ. 3(1), 15–102 (1996)

    Article  MathSciNet  Google Scholar 

  23. Pagan A.R., Schwert G.W.: Alternative models for conditional stock volatility. J. Econom. 45(1–2), 267–290 (1990)

    Article  Google Scholar 

  24. Rydén T., Teräsvirta T., Åsbrink S.: Stylized facts of daily return series and the hidden markov model. J. Appl. Econom. 13, 217–244 (1998)

    Article  Google Scholar 

  25. Schoutens, W.: The meixner process in finance. EURANDOM report 2001–2002 (2001)

  26. Shephard N.: Statistical aspects of ARCH and stochastic volatility. In: Cox, D.R., Hinkley, D.V., Barndorff-Nielsen, O.E. (eds) Time Series Models in Econometrics, pp. 1–67. Finance and Other Fields, Chapman & Hall, London (1996)

    Google Scholar 

  27. Silverman B.W.: On the estimation of a probability density function by the maximum penalized likelihood method. Ann. Stat. 10, 795–810 (1982)

    Article  MATH  Google Scholar 

  28. Taylor S.: Modelling Financial Time Series. Wiley, New York (1986)

    MATH  Google Scholar 

  29. Tyssedal J.S., Tjostheim D.: An autoregressive model with suddenly changing parameters and an application to stock market prices. J. R. Stat. Soc. Ser. C (Appl. Stat.) 37(3), 353–369 (1988)

    MathSciNet  Google Scholar 

  30. Watson G.N.: A Treatise on the Theory of Bessel Functions. Cambridge University Press, Cambridge (1966)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to L. C. G. Rogers.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rogers, L.C.G., Zhang, L. An asset return model capturing stylized facts. Math Finan Econ 5, 101–119 (2011). https://doi.org/10.1007/s11579-011-0050-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11579-011-0050-5

Keywords

Navigation