IDEAS home Printed from https://ideas.repec.org/p/zbw/irtgdp/2020020.html
   My bibliography  Save this paper

Long- and Short-Run Components of Factor Betas: Implications for Stock Pricing

Author

Listed:
  • Asgharian, Hossein
  • Christiansen, Charlotte
  • Hou, Ai Jun
  • Wang, Weining

Abstract

We propose a bivariate component GARCH-MIDAS model to estimate the long- and short-run components of the variances and covariances. The advantage of our model to the existing DCC-based models is that it uses the same form for both the variances and covariances and that it estimates these moments simultaneously. We apply this model to obtain long- and short-run factor betas for industry test portfolios, where the risk factors are the market, SMB, and HML portfolios. We use these betas in cross-sectional analysis of the risk premia. Among other things, we find that the risk premium related to the short- run market beta is significantly positive, irrespective of the choice of test portfolio. Further, the risk premia for the short-run betas of all the risk factors are significant outside recessions.

Suggested Citation

  • Asgharian, Hossein & Christiansen, Charlotte & Hou, Ai Jun & Wang, Weining, 2020. "Long- and Short-Run Components of Factor Betas: Implications for Stock Pricing," IRTG 1792 Discussion Papers 2020-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  • Handle: RePEc:zbw:irtgdp:2020020
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/230826/1/irtg1792dp2020-020.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ang, Andrew & Kristensen, Dennis, 2012. "Testing conditional factor models," Journal of Financial Economics, Elsevier, vol. 106(1), pages 132-156.
    2. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    3. Rasmus S. Pedersen & Anders Rahbek, 2014. "Multivariate variance targeting in the BEKK–GARCH model," Econometrics Journal, Royal Economic Society, vol. 17(1), pages 24-55, February.
    4. Cenesizoglu, Tolga & Reeves, Jonathan J., 2018. "CAPM, components of beta and the cross section of expected returns," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 223-246.
    5. Thomas Gilbert & Christopher Hrdlicka & Jonathan Kalodimos & Stephan Siegel, 2014. "Daily Data is Bad for Beta: Opacity and Frequency-Dependent Betas," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 4(1), pages 78-117.
    6. Tobias Adrian & Joshua Rosenberg, 2008. "Stock Returns and Volatility: Pricing the Short‐Run and Long‐Run Components of Market Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2997-3030, December.
    7. Boussama, Farid & Fuchs, Florian & Stelzer, Robert, 2011. "Stationarity and geometric ergodicity of BEKK multivariate GARCH models," Stochastic Processes and their Applications, Elsevier, vol. 121(10), pages 2331-2360, October.
    8. González, Mariano & Nave, Juan & Rubio, Gonzalo, 2018. "Macroeconomic determinants of stock market betas," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 26-44.
    9. Calvet, Laurent E. & Fisher, Adlai J., 2007. "Multifrequency news and stock returns," Journal of Financial Economics, Elsevier, vol. 86(1), pages 178-212, October.
    10. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    11. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2005. "There is a risk-return trade-off after all," Journal of Financial Economics, Elsevier, vol. 76(3), pages 509-548, June.
    12. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    13. Joao Gomes & Leonid Kogan & Lu Zhang, 2003. "Equilibrium Cross Section of Returns," Journal of Political Economy, University of Chicago Press, vol. 111(4), pages 693-732, August.
    14. Luc Bauwens & Christian M. Hafner & Diane Pierret, 2013. "Multivariate Volatility Modeling Of Electricity Futures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 743-761, August.
    15. Baele, Lieven & Londono, Juan M., 2013. "Understanding industry betas," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 30-51.
    16. José Rangel & Robert Engle, 2012. "The Factor–Spline–GARCH Model for High and Low Frequency Correlations," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 109-124.
    17. Christian Conrad & Karin Loch, 2015. "Anticipating Long‐Term Stock Market Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1090-1114, November.
    18. Engle, Robert F. & White (the late), Halbert (ed.), 1999. "Cointegration, Causality, and Forecasting: Festschrift in Honour of Clive W. J. Granger," OUP Catalogue, Oxford University Press, number 9780198296836.
    19. Bandi, F.M. & Perron, B. & Tamoni, A. & Tebaldi, C., 2019. "The scale of predictability," Journal of Econometrics, Elsevier, vol. 208(1), pages 120-140.
    20. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    21. Ghysels, Eric & Guérin, Pierre & Marcellino, Massimiliano, 2014. "Regime switches in the risk–return trade-off," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 118-138.
    22. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    23. Amado, Cristina & Teräsvirta, Timo, 2014. "Modelling changes in the unconditional variance of long stock return series," Journal of Empirical Finance, Elsevier, vol. 25(C), pages 15-35.
    24. Ravi Bansal & Robert Dittmar & Dana Kiku, 2009. "Cointegration and Consumption Risks in Asset Returns," Review of Financial Studies, Society for Financial Studies, vol. 22(3), pages 1343-1375, March.
    25. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    26. Turan G. Bali & Robert F. Engle & Yi Tang, 2017. "Dynamic Conditional Beta Is Alive and Well in the Cross Section of Daily Stock Returns," Management Science, INFORMS, vol. 63(11), pages 3760-3779, November.
    27. Bali, Turan G. & Engle, Robert F., 2010. "The intertemporal capital asset pricing model with dynamic conditional correlations," Journal of Monetary Economics, Elsevier, vol. 57(4), pages 377-390, May.
    28. Lewellen, Jonathan & Nagel, Stefan, 2006. "The conditional CAPM does not explain asset-pricing anomalies," Journal of Financial Economics, Elsevier, vol. 82(2), pages 289-314, November.
    29. De Santis, Giorgio & Gerard, Bruno, 1997. "International Asset Pricing and Portfolio Diversification with Time-Varying Risk," Journal of Finance, American Finance Association, vol. 52(5), pages 1881-1912, December.
    30. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    31. Chan, K C & Chen, Nai-Fu, 1988. " An Unconditional Asset-Pricing Test and the Role of Firm Size as an Instrumental Variable for Risk," Journal of Finance, American Finance Association, vol. 43(2), pages 309-325, June.
    32. Kim, Dongcheol, 1995. "The Errors in the Variables Problem in the Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 50(5), pages 1605-1634, December.
    33. Maio, Paulo & Santa-Clara, Pedro, 2017. "Short-Term Interest Rates and Stock Market Anomalies," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(3), pages 927-961, June.
    34. Fama, Eugene F. & French, Kenneth R., 1997. "Industry costs of equity," Journal of Financial Economics, Elsevier, vol. 43(2), pages 153-193, February.
    35. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    36. González, Mariano & Nave, Juan & Rubio, Gonzalo, 2012. "The Cross Section of Expected Returns with MIDAS Betas," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 47(1), pages 115-135, February.
    37. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    38. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    39. Hossein Asgharian & Björn Hansson, 2000. "Cross‐sectional analysis of Swedish stock returns with time‐varying beta: the Swedish stock market 1983–96," European Financial Management, European Financial Management Association, vol. 6(2), pages 213-233, June.
    40. Bali, Turan G., 2008. "The intertemporal relation between expected returns and risk," Journal of Financial Economics, Elsevier, vol. 87(1), pages 101-131, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou & Weining Wang, 2017. "Long- and Short-Run Components of Factor Betas: Implications for Equity Pricing," CREATES Research Papers 2017-34, Department of Economics and Business Economics, Aarhus University.
    2. González, Mariano & Nave, Juan & Rubio, Gonzalo, 2018. "Macroeconomic determinants of stock market betas," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 26-44.
    3. Cenesizoglu, Tolga & Reeves, Jonathan J., 2018. "CAPM, components of beta and the cross section of expected returns," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 223-246.
    4. Baele, Lieven & Londono, Juan M., 2013. "Understanding industry betas," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 30-51.
    5. Stefano Grassi & Francesco Violante, 2021. "Asset Pricing Using Block-Cholesky GARCH and Time-Varying Betas," Working Papers 2021-05, Center for Research in Economics and Statistics.
    6. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    7. Chiang, Thomas C. & Li, Huimin & Zheng, Dazhi, 2015. "The intertemporal risk-return relationship: Evidence from international markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 39(C), pages 156-180.
    8. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    9. Londono Yarce, J.M., 2011. "Essays on asset pricing," Other publications TiSEM 744a2ac5-7ada-4fa8-a7aa-e, Tilburg University, School of Economics and Management.
    10. Boguth, Oliver & Carlson, Murray & Fisher, Adlai & Simutin, Mikhail, 2011. "Conditional risk and performance evaluation: Volatility timing, overconditioning, and new estimates of momentum alphas," Journal of Financial Economics, Elsevier, vol. 102(2), pages 363-389.
    11. Cosemans, M. & Frehen, R.G.P. & Schotman, P.C. & Bauer, R.M.M.J., 2009. "Efficient Estimation of Firm-Specific Betas and its Benefits for Asset Pricing Tests and Portfolio Choice," MPRA Paper 23557, University Library of Munich, Germany.
    12. Belén Nieto & Alfonso Novales & Gonzalo Rubio, 2015. "Macroeconomic and Financial Determinants of the Volatility of Corporate Bond Returns," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 1-41, December.
    13. Turan G. Bali & Robert F. Engle & Yi Tang, 2017. "Dynamic Conditional Beta Is Alive and Well in the Cross Section of Daily Stock Returns," Management Science, INFORMS, vol. 63(11), pages 3760-3779, November.
    14. Ryuta Sakemoto, 2022. "Multi‐scale inter‐temporal capital asset pricing model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4298-4317, October.
    15. Luc Bauwens & Christian M. Hafner & Diane Pierret, 2013. "Multivariate Volatility Modeling Of Electricity Futures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 743-761, August.
    16. Manuel Monge & Luis A. Gil-Alana, 2020. "The Lithium Industry and Analysis of the Beta Term Structure of Oil Companies," Risks, MDPI, vol. 8(4), pages 1-17, December.
    17. Ang, Andrew & Chen, Joseph, 2007. "CAPM over the long run: 1926-2001," Journal of Empirical Finance, Elsevier, vol. 14(1), pages 1-40, January.
    18. Sebastien Valeyre & Sofiane Aboura & Denis Grebenkov, 2019. "The Reactive Beta Model," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 42(1), pages 71-113, March.
    19. Connor, Gregory & Suurlaht, Anita, 2013. "Dynamic stock market covariances in the Eurozone," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 353-370.
    20. Cristina Amado & Annastiina Silvennoinen & Timo Teräsvirta, 2018. "Models with Multiplicative Decomposition of Conditional Variances and Correlations," CREATES Research Papers 2018-14, Department of Economics and Business Economics, Aarhus University.

    More about this item

    Keywords

    long-run betas; short-run betas; risk premia; business cycles; component GARCH model; MIDAS;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:irtgdp:2020020. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/wfhubde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.