Acta Univ. Agric. Silvic. Mendelianae Brun. 2017, 65(6), 1889-1894 | DOI: 10.11118/actaun201765061889

Comparing Entropy and Beta as Measures of Risk in Asset Pricing

Galina Deeva
Department of Finance, Faculty of Economics and Administration, Masaryk University, Lipová 41a, 602 00 Brno, Czech Republic

The paper establishes entropy as a measure of risk in asset pricing models by comparing its explanatory power with that of classic capital asset pricing model's beta to describe the diversity in expected risk premiums. Three different non-parametric estimation procedures are considered to evaluate financial entropy, namely kernel density estimated Shannon entropy, kernel density estimated Rényi entropy and maximum likelihood Miller-Madow estimated Shannon entropy. The comparison is provided based on the European stock market data, for which the basic risk-return trade-off is generally negative. Kernel density estimated Shannon entropy provides the most efficient results not dependent on the choice of the market benchmark and without imposing any prior model restrictions.

Keywords: entropy, risk measure, beta, asset pricing
Grants and funding:

The support of the Masaryk University internal grant MUNI/A/1039/2016 is gratefully acknowledged.

Published: December 7, 2017  Show citation

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Deeva, G. (2017). Comparing Entropy and Beta as Measures of Risk in Asset Pricing. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis65(6), 1889-1894. doi: 10.11118/actaun201765061889
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References

  1. ASLANIDIS, N., CHRISTIANSEN, C. and SAVVA, C. 2016. Risk-return trade-off for European stock markets. International Review of Financial Analysis, 46: 84 - 103 DOI: 10.1016/j.irfa.2016.03.018 Go to original source...
  2. BACKUS, D., CHERNOV, M. and ZIN, S. 2014. Sources of entropy in representative agent models. The Journal of Finance, 69(1): 51 - 99. DOI: 10.1111/jofi.12090 Go to original source...
  3. CAMPBELL, J. Y. 2015. Emerging Trends: Asset Pricing. In: SCOTT, R. an KOSSLYN, S. (Eds.) Emerging Trends in the Social and Behavioral Sciences. John Wiley & Sons, Inc. Go to original source...
  4. CLAUSIUS, R. 1870. On a mechanical theorem applicable to heat. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 40(1870): 122 - 127. DOI: 10.1080/14786447008640370 Go to original source...
  5. MAASOUMI, E. and RACINE, J. 2002. Entropy and predictability of stock market returns. Journal of Econometrics, 107(1 - 2): 291 - 312. DOI: 10.1016/S0304-4076(01)00125-7 Go to original source...
  6. MILLER, G. A. 1955. Note on the Bias of Information Estimates. In: QUASTLER, H. (Ed.) Information Theory in Psychology II-B. Glencoe, IL: Free Press. pp. 95 - 100.
  7. ORMOS, M. and ZIBRICZKY, D. 2014. Entropy-Based Financial Asset Pricing. PLoS ONE, 9(12): e115742. DOI: 10.1371/journal.pone.0115742 Go to original source...
  8. PANINSKI, L. 2003. Estimation of entropy and mutual information. Neural Computation, 15(6): 1191 - 1253. DOI: 10.1162/089976603321780272 Go to original source...
  9. RÉNYI, A. 1961. On measures of information and entropy. In: Proceedings of the fourth Berkeley Symposium on Mathematics, Statistics and Probability. Vol. 1. Berkeley, Calif.: University of California Press, pp. 547 - 561.
  10. SHANNON, C. 1948. A Mathematical Theory of Communication. Bell System Technical Journal, 27: 379 - 423. DOI: 10.1002/j.1538-7305.1948.tb01338.x Go to original source...
  11. XU, J. P., ZHOU, X. Y. and WU, D. D. 2011. Portfolio selection using λ mean and hybrid entropy. Annals of Operations Research, 185(1): 213 - 229. DOI: 10.1007/s10479-009-0550-3 Go to original source...
  12. ZHOU, R. X., CHEN, L. M. and QIU, W.H. 2006. The entropy model of American bond option pricing. Mathematics in Practice and Theory, 36: 59 - 64.

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