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Determinants of systematic risk in the US Restaurant industry

Author

Listed:
  • Sung Y. Park

    (Chung-Ang University, South Korea)

  • Sang Hyuck Kim

    (Gachon University, South Korea)

Abstract

To compare previous studies, this study re-examines the determinants of systematic risk in the restaurant industry. To estimate systematic risk, the authors specify flexible models that take care of serial dependence, autoregressive conditional heteroskedasticity and non-normality of the time series data. Using the estimated systematic risk, they analyse the determinants of risk using a quantile regression approach. The empirical results show that a firm’s liquidity ratio, efficiency ratio, debt leverage ratio and size are the main determinants of systematic risk in the restaurant industry. Moreover, it turns out that the effects of liquidity, debt leverage and efficiency decrease as the considered risk levels increase.

Suggested Citation

  • Sung Y. Park & Sang Hyuck Kim, 2016. "Determinants of systematic risk in the US Restaurant industry," Tourism Economics, , vol. 22(3), pages 621-628, June.
  • Handle: RePEc:sae:toueco:v:22:y:2016:i:3:p:621-628
    DOI: 10.5367/te.2014.0432
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    References listed on IDEAS

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    1. Park, Sung Y. & Bera, Anil K., 2009. "Maximum entropy autoregressive conditional heteroskedasticity model," Journal of Econometrics, Elsevier, vol. 150(2), pages 219-230, June.
    2. David Ashton & Mark Tippett, 2006. "Mean Reversion and the Distribution of United Kingdom Stock Index Returns," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(9‐10), pages 1586-1609, November.
    3. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    5. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    6. Bowman, Robert G, 1979. "The Theoretical Relationship between Systematic Risk and Financial (Accounting) Variables," Journal of Finance, American Finance Association, vol. 34(3), pages 617-630, June.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    9. Woo Gon Kim & Bill Ryan & Silvio Ceschini, 2007. "Factors Affecting Systematic Risk in the US Restaurant Industry," Tourism Economics, , vol. 13(2), pages 197-208, June.
    10. Kurt Brannas & Niklas Nordman, 2003. "Conditional skewness modelling for stock returns," Applied Economics Letters, Taylor & Francis Journals, vol. 10(11), pages 725-728.
    11. David Ashton & Mark Tippett, 2006. "Mean Reversion and the Distribution of United Kingdom Stock Index Returns," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(9-10), pages 1586-1609.
    12. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
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    Cited by:

    1. Madhusmita Bhadra & Doyeon Kim, 2023. "Income elasticity of demand and stock market beta," International Finance, Wiley Blackwell, vol. 26(2), pages 225-240, August.

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