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Fractality in market risk structure: Dow Jones Industrial components case

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  • Kristoufek, Ladislav

Abstract

We examine the Dow Jones Industrial Average index components with respect to the capital asset pricing model (CAPM), specifically its scaling properties in the sense of different investment horizons. To do so, we use the novel methods of fractal regressions based on the detrended cross-correlation analysis and the detrending moving-average cross-correlation analysis. We report three standard groups of stocks – aggressive, defensive and market-following – which are rather uniformly represented. For most of the stocks, the β parameter of the CAPM does not vary significantly across scales. There are two groups of exceptions. One of aggressive stocks which are even more aggressive for short investment horizons. These do not provide portfolio diversification benefits but allow for high profits above the market returns and even more so for the short investment horizons. And the other group of more defensive stocks which become very defensive in the long term. These stocks do not deliver short term profits but can serve as strong risk diversifiers. Apart from these direct results, our analysis opens several interesting questions and future research directions, both technical and experimental, which we discuss in more detail.

Suggested Citation

  • Kristoufek, Ladislav, 2018. "Fractality in market risk structure: Dow Jones Industrial components case," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 69-75.
    • Handle: RePEc:eee:chsofr:v:110:y:2018:i:c:p:69-75
    DOI: 10.1016/j.chaos.2018.02.028
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    1. He, Ling-Yun & Chen, Shu-Peng, 2011. "A new approach to quantify power-law cross-correlation and its application to commodity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3806-3814.
    2. Barunik, Jozef & Kristoufek, Ladislav, 2010. "On Hurst exponent estimation under heavy-tailed distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3844-3855.
    3. Ferreira, Paulo & Kristoufek, Ladislav, 2017. "What is new about covered interest parity condition in the European Union? Evidence from fractal cross-correlation regressions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 554-566.
    4. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    5. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    6. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    7. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    8. Weron, Rafał, 2002. "Estimating long-range dependence: finite sample properties and confidence intervals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(1), pages 285-299.
    9. Ladislav Kristoufek, 2012. "Fractal Markets Hypothesis And The Global Financial Crisis: Scaling, Investment Horizons And Liquidity," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1-13.
    10. repec:pri:cepsud:91malkiel is not listed on IDEAS
    11. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    12. Kristoufek, Ladislav, 2014. "Leverage effect in energy futures," Energy Economics, Elsevier, vol. 45(C), pages 1-9.
    13. LeRoy, Stephen F, 1976. "Efficient Capital Markets: Comment," Journal of Finance, American Finance Association, vol. 31(1), pages 139-141, March.
    14. S. Shadkhoo & G. R. Jafari, 2009. "Multifractal detrended cross-correlation analysis of temporal and spatial seismic data," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 72(4), pages 679-683, December.
    15. Sergio Arianos & Anna Carbone, 2008. "Cross-correlation of long-range correlated series," Papers 0804.2064, arXiv.org, revised Mar 2009.
    16. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
    17. Zebende, G.F., 2011. "DCCA cross-correlation coefficient: Quantifying level of cross-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 614-618.
    18. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    19. Vassoler, R.T. & Zebende, G.F., 2012. "DCCA cross-correlation coefficient apply in time series of air temperature and air relative humidity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2438-2443.
    20. Reboredo, Juan Carlos & Rivera-Castro, Miguel A. & Zebende, Gilney F., 2014. "Oil and US dollar exchange rate dependence: A detrended cross-correlation approach," Energy Economics, Elsevier, vol. 42(C), pages 132-139.
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