The CAPM, National Stock Market Betas, and Macroeconomic Covariates: a Global Analysis

Abstract

Using global data on aggregate stock markets, this paper finds that the capital asset pricing model fares much better than suggested previously. At shorter time horizons, our results also show that the positive risk-reward relation can collapse during times of high volatility. Compared to other countries, we retrieve evidence of lower systematic risks across frontier equity portfolios. We find that countries characterized by higher levels of openness, exchange rate volatility, and larger economic size are exposed to higher systematic covariances with the world stock market. Conversely, we obtain an inverse link between international reserves and systematic risks in national equity.

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Fig. 1

Notes: * significant at 10%; ** significant at 5%; *** significant at 1%. MXWD (MSCI ACWI) is used as the world stock market portfolio. Red circles represent equity while green squares represent long-term government debt

Fig. 2

Notes: * significant at 10%; ** significant at 5%; *** significant at 1%. MXWO is used as the world stock market portfolio. Red circles represent equity while green squares represent long-term government debt

Fig. 3

Notes: Sample consists of developed national stock markets. Global portfolio employed is the MSCI All Country World Index (MXWD). Security market line (SML) plotted is both the empirical and theoretical one. SML has a slope of 0.004, consistent with the average monthly excess return on the world portfolio over the period. When national stock market beta is equal to 0, SML shows an average monthly return equal to 0.001, consistent with the average risk-free rate over the period

Fig. 4

Notes: * significant at 10%; ** significant at 5%; *** significant at 1%. MXWD (MSCI ACWI) is used as the world stock market portfolio for the period 1988-2017. MXWO proxies for the world stock market over the period 1968-1987. Red circles represent equity while green squares represent long-term government debt

Fig. 5

Notes: The horizontal axis marks the end of each 5-year period. The red dashed line marks the average return volatility over the entire sample period

Fig. 6

Notes: Blue circles, red squares, and green triangles correspond to developed, emerging, and frontier stock markets respectively. Annualized figures are shown

Fig. 7

Notes: Blue circles, red squares, and green triangles correspond to developed, emerging, and frontier stock markets respectively. The horizontal axis marks the final year of each non-overlapping 5-year period. Jensen’s alpha is the average return on the national stock market portfolio over and above that predicted by the CAPM

Fig. 8

Notes: Blue circles, red squares, and green triangles correspond to developed, emerging, and frontier stock markets respectively. 5-year alphas and betas are employed. Jensen’s alpha is the average return on the national stock market portfolio over and above that predicted by the CAPM. Parametric and non-parametric correlations between alpha and beta are close to zero

Notes

  1. 1.

    Early studies employed short samples with a handful of advanced economies to investigate the investment behavior of citizens of one country when facing an expanded international investment opportunity set or gains from international diversification (Grubel 1968; Lee 1969; Levy and Sarnat 1970; Miller and Whitman 1970; Grubel and Fadner 1971; Solnik 1974, 1977). The subsequent literature diverged by looking at differences in consumption profiles, multifactor models, conditional CAPM, and other variants (Korajczyk and Viallet 1989; Harvey 1991; Dumas and Solnik 1995). Tests were mostly inconclusive.

  2. 2.

    We confine our analysis to the traditional CAPM alone, as multi-factor models (i.e. augmented CAPMs) are known to diminish the dispersion in market portfolio betas, pushing them toward unity (Ahn et al. 2013; Fama and French 1992, 1996, 2015). Conditional CAPM estimation is also not feasible in our study given our data.

  3. 3.

    Alpha is the return above that predicted by the CAPM.

  4. 4.

    According to Pentecöte et al. (2015), new trade flows (extensive margin) tend to strengthen specialization and the decoupling of business cycles across countries.

  5. 5.

    This materializes unless there are significant cross-border (intermediate) input-output linkages running over heterogeneous industries. In this latter scenario, comovements are induced by countries using foreign heterogeneous goods as intermediate inputs in their sector of specialization.

  6. 6.

    Following Fama and MacBeth (1973), we also estimate month-by-month and year-by-year cross-section regressions, obtaining time series means of the intercepts and slopes for testing. This approach yielded similar results. Results are robust to Shanken correction and GMM approach to estimation.

  7. 7.

    See MSCI methodology for construction details.

  8. 8.

    Parametric Pearson correlations are similar.

  9. 9.

    The annualized rate is approximately 5 percent.

  10. 10.

    Gross rank correlation coefficient of -0.20 that is statistically significant at the 10 percent level.

  11. 11.

    Quite similar patterns are found over the shorter interval 1988-2017 too.

  12. 12.

    That is, Jensen’s alpha can be obtained as the intercept from time series regression (2).

  13. 13.

    Qualitatively, in a less refined preliminary approach, non-parametric gross (unconditional) correlation coefficients over the cross section mostly yielded similar results.

  14. 14.

    Economies in which it is easier to conduct business, as a result of greater political stability or less rigid regulation for example, are expected to have more pronounced global ties through higher levels of international portfolio investment, foreign direct investment etc. Importantly, better business environments are more conducive to success among domestic firms, that, building on their foundations, can go on to compete on the world stage with their exports. Over the cross section, we find a strong positive relation between market betas and ease-of-doing business rankings.

References

  1. Ahn S, Perez MF, Gadarowski C (2013) Two-Pass Estimation of risk premiums with multicollinear and Near-Invariant betas. J Empir Financ 20(C):1–17

    Google Scholar 

  2. Asness CS, Moskowitz TJ, Pedersen LH (2013) Value and momentum everywhere. J Financ 68(3):929–985

    Google Scholar 

  3. Bai H, Hou K, Kung H, Li EX, Zhang L (2019) The CAPM strikes back? an equilibrium model with disasters. J Financ Econ 131(2):269–298

    Google Scholar 

  4. Berk JB, van Binsbergen JH (2016) Assessing asset pricing models using revealed preference. J Financ Econ 119(1):1–23

    Google Scholar 

  5. Black F (1972) Capital market equilibrium with restricted borrowing. J Bus 45(3):444–455

    Google Scholar 

  6. Black F, Jensen MC, Scholes M (1972) The Capital Asset Pricing Model: Some Empirical Tests. In: Jensen M. C. (ed) Studies in the Theory of Capital Markets. Praeger, New York, pp 79–121

  7. Blume M, Friend I (1970) Measurement of portfolio performance under uncertainty. Am Econ Rev 60(4):607–636

    Google Scholar 

  8. Blume M, Friend I (1973) A new look at the capital asset pricing model. J Financ 28(1):19–33

    Google Scholar 

  9. Bordo MD, Helbling T (2003) Have National Business Cycles Become More Synchronized?. NBER Working Paper No. 10130

  10. Bruno V, Shin HS (2015) Cross-Border Banking and global liquidity. Rev Econ Stud 82(2):535–564

    Google Scholar 

  11. Burnside C, Eichenbaum M, Rebelo S (2004) Government guarantees and Self-Fulfilling speculative attacks. J Econ Theory 119(1):31–63

    Google Scholar 

  12. Buttiglione L, Lane PR, Reichlin L, Reinhart V (2014) Deleveraging? What Deleveraging?, Geneva Reports on the World Economy 16

  13. Caballero R, Krishnamurthy A (2004) Smoothing sudden stops. J Econ Theory 119(1):104–127

    Google Scholar 

  14. Calderon C, Chong A, Stein E (2007) Trade Intensity and Business Cycle Synchronization: Are Developing Countries Any Different? J Int Econ 71(1):2–21

    Google Scholar 

  15. Calomiris CW, Mamaysky H (2019) How news and its context drive risk and returns around the world. J Financ Econ 133(2):299–336

    Google Scholar 

  16. Campbell JY (2018) Financial decisions and markets: a course in asset pricing. Princeton University Press, Princeton

    Google Scholar 

  17. Campbell JY, Vuolteenaho T (2004) Bad beta, good beta. Am Econ Rev 94(5):1249–1275

    Google Scholar 

  18. Chinn MD, Forbes KJ (2004) A decomposition of global linkages in financial markets over time. Rev Econ Stat 86(3):705–722

    Google Scholar 

  19. Chutasripanich N, Yetman J (2015) Foreign exchange intervention: Strategies and effectiveness BIS Working Paper No. 499

  20. Cook D, Yetman J (2012) Expanding central bank balance sheets in emerging asia: a compendium of risks and some evidence. in are central bank balance sheets in asia too large?, 66, BIS Papers, pp 30–75

  21. De Truchis G, Keddad B (2016) Long-Run Comovements in east asian stock market volatility. Open Econ Rev 27(5):969–986

    Google Scholar 

  22. Dées S, Zorell N (2012) Business cycle synchronisation: Disentangling trade and financial linkages. Open Econ Rev 23(4):623–643

    Google Scholar 

  23. Dumas B, Solnik B (1995) The world price of foreign exchange risk. J Financ 50(2):445–479

    Google Scholar 

  24. Fama EF, French KR (1992) The Cross-Section of expected stock returns. J Financ 47(2):427–465

    Google Scholar 

  25. Fama EF, French KR (1993) Common risk factors in the returns on stocks and bonds. J Financ Econ 33(1):3–56

    Google Scholar 

  26. Fama EF, French KR (1996) Multifactor explanations of asset pricing anomolies. J Financ 51(1):55–84

    Google Scholar 

  27. Fama EF, French KR (2004) The capital asset pricing model: Theory and evidence. J Econ Perspect 18(3):25–46

    Google Scholar 

  28. Fama EF, French KR (2006) The value premium and the CAPM. J Financ 61(5):2163–2185

    Google Scholar 

  29. Fama EF, French KR (2015) A Five-Factor asset pricing model. J Financ Econ 116(1):1–22

    Google Scholar 

  30. Fama EF, French KR (2016) Dissecting anomalies with a Five-Factor model. Rev Financ Stud 29(1):69–103

    Google Scholar 

  31. Fama EF, MacBeth JD (1973) Risk, return, and equilibrium: Empirical tests. J Polit Econ 81(3):607–636

    Google Scholar 

  32. Frankel JA, Rose AK (1998) The endogeneity of the optimum currency area criteria. Economic Journal 108(449):1009–1025

    Google Scholar 

  33. Frazzini A, Pedersen LH (2014) Betting against beta. J Financ Econ 111 (1):1–25

    Google Scholar 

  34. Frees EW (1995) Assessing Cross-Sectional Correlation in Panel Data. J Econ 69(2):393–414

    Google Scholar 

  35. Friedman M (1937) The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance. J Amer Stat Assoc 32(200):675–701

    Google Scholar 

  36. Galstyan V, Velic A (2018) International investment patterns: The case of german sectors. Open Econ Rev 29(3):665–685

    Google Scholar 

  37. Gerlach-Kristen P, McCauley R, Ueda K (2016) Currency intervention and the global portfolio balance effect: Japanese lessons. J Jpn Int Econ 39(C):1–16

    Google Scholar 

  38. Grubel HG (1968) Internationally diversified portfolios: Welfare gains and capital flows. Am Econ Rev 58(5):1299–1314

    Google Scholar 

  39. Grubel HG, Fadner K (1971) The interdependence of international equity markets. J Financ 26(1):89–94

    Google Scholar 

  40. Harvey CR (1991) The world price of covariance risk. J Financ 46(1):111–157

    Google Scholar 

  41. Imbs JM (2004) Trade, finance, specialization, and synchronization. Rev Econ Stat 86(3):723–734

    Google Scholar 

  42. Imbs JM (2006) The real effects of financial integration. J Int Econ 68(2):296–324

    Google Scholar 

  43. Jagannathan R, McGrattan ER (1995) The CAPM debate. Federal Reserve Bank of Minneapolis Quarterly Review 19(4):2–17

    Google Scholar 

  44. Jordà O, Schularick M, Taylor AM, Ward F (2018) Global financial cycles and risk premiums. CEPR Discussion Paper No. 12969

  45. Kalemli-Ozcan S, Sorensen BE, Yosha O (2001) Economic integration, industrial specialization, and the asymmetry of macroeconomic fluctuations. J Int Econ 55(1):107–137

    Google Scholar 

  46. King M, Wadhwani S (1990) Transmission of volatility between stock markets. Rev Financ Stud 3(1):5–33

    Google Scholar 

  47. Korajczyk RA, Viallet CJ (1989) An empirical investigation of international asset pricing. Rev Financ Stud 2(4):553–585

    Google Scholar 

  48. Kose MA, Prasad ES, Terrones ME (2003) How Does Globalization Affect the Synchronization of Business Cycles?. Amer Econ Rev 93(2):57–62

    Google Scholar 

  49. Lakonishok J, Shapiro AC (1986) Systematic risk, total risk and size as determinants of stock market returns. J Bank Financ 10(1):115–132

    Google Scholar 

  50. Lane PR, Milesi-Ferretti GM (2007) The external wealth of nations mark II: Revised and extended estimates of foreign assets and liabilities, 1970-2004. J Int Econ 73(2):223–250

    Google Scholar 

  51. Lee CH (1969) A Stock-Adjustment analysis of capital movements: The united States-Canadian case. J Polit Econ 77(4):512–523

    Google Scholar 

  52. Levy H, Sarnat M (1970) International diversification of investment portfolios. Am Econ Rev 60(4):668–675

    Google Scholar 

  53. Lo AW (2017) Adaptive markets: Financial evolution at the speed of thought. Princeton University Press, Princeton

    Google Scholar 

  54. Miller NC, Whitman vNM (1970) A Mean-Variance Analysis of United States Long-Term Portfolio Foreign Investment. Q J Econ 84(2):175–196

    Google Scholar 

  55. Obstfeld M (2015) Trilemmas and Trade-Offs: Living with financial globalization. BIS Working Paper No. 480

  56. Obstfeld M, Shambaugh JC, Taylor AM (2004) Monetary sovereignty, exchange rates, and capital controls: The trilemma in the interwar period. IMF Staff Pap 51(1):75–108

    Google Scholar 

  57. Obstfeld M, Shambaugh JC, Taylor AM (2005) The trilemma in history: Tradeoffs among exchange rates, monetary policies, and capital mobility. Rev Econ Stat 87(3):423–438

    Google Scholar 

  58. Pentecöte J-S, Poutineau J-C, Rondeau F (2015) Trade integration and business cycle synchronization in the EMU: The negative effect of new trade flows. Open Econ Rev 26(1):61–79

    Google Scholar 

  59. Pesaran MH (2004) General Diagnostic Tests for Cross Section Dependence in Panels. CESifo Working Paper No. 1229

  60. Reinganum MR (1981) A new empirical perspective on the CAPM. J Financ Quant Anal 16(4):439–462

    Google Scholar 

  61. Rey H (2013) Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence. Proceedings, Economic Policy Symposium, Jackson Hole, Federal Reserve Bank of Kansas City, pp 285–333

  62. Roll R, Ross SA (1994) On the Cross-Sectional relation between expected returns and betas. J Financ 49(1):101–121

    Google Scholar 

  63. Solnik BH (1974) The international pricing of risk: an empirical investigation of the world capital market structure. J Financ 29(2):365–378

    Google Scholar 

  64. Solnik BH (1977) Testing international asset pricing: Some pessimistic views. J Financ 32(2):503–512

    Google Scholar 

  65. Stambaugh RF (1982) On the exclusion of assets from tests of the Two-Parameter model: a sensitivity analysis. J Financ Econ 10(3):237–268

    Google Scholar 

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Correspondence to Adnan Velic.

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We thank John Burger, Charles Calomiris, Scott Dressler, Vahagn Galstyan, Erasmus Kersting, George Tavlas, and two anonymous referees for very helpful comments and suggestions. Ryan Zalla, Bernard Zaritsky, Riley McCarten, Yanyao Shi, Ibrahim Annabi, and Rashaad Robinson provided diligent research assistance.

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Curran, M., Velic, A. The CAPM, National Stock Market Betas, and Macroeconomic Covariates: a Global Analysis. Open Econ Rev 31, 787–820 (2020). https://doi.org/10.1007/s11079-020-09579-2

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Keywords

  • Portfolios
  • Stock market
  • Cross-country
  • Systematic risk
  • Capital asset pricing model
  • Macroeconomic covariates

JEL Classification

  • F30
  • F31
  • F41
  • G15