Skip to main content
SpringerLink
Log in

On Modelling and Forecasting Predictable Components in European Stock Markets

  • Published:
Computational Economics Aims and scope Submit manuscript

Abstract

This study investigates possible existence of predictable components in stock excess returns in eighteen European countries i.e. Austria, Croatia, Denmark, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Netherlands, Norway, Russia, Slovakia, Spain, Sweden, Switzerland, and United Kingdom. The excess return series for these countries are modeled using non Gaussian state space or unobserved component model that encompasses non normality and time varying volatility that might be present in the series. While statistically significant evidence of non-normality and volatility persistence does exist in most series, statistically significant persistent predictable component also prevails in Austria, France, Germany, Iceland, Ireland, Italy, Netherlands, Norway, Spain, Sweden, and Switzerland excess returns. However, the results on possible existence of predictable components in stock excess returns in Croatia, Denmark, Greece, Hungary, Russia, Slovakia, and United Kingdom are in sharp contrast. The efficiently estimated excess returns range between 0.002 % per month for Slovakia to 0.094 % per month for Russia stock excess returns. The characteristic exponent ranges between of 1.632 for Croatia to 1.917 for France showing non-normal behavior in these series although the characteristic exponent of 1.999 shows a normal behavior in Italian and Russian excess return series.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Studies by Fama (1991), Campbell (2003), and Ang and Bekaert (2007) among many others show that stock return predictability does exist.

  2. Mankiw and Shapiro (1986), Nelson and Kim (1993), Goetzman and Jorion (1993), Cavanagh et al. (1995), Stambaugh (1999), Lanne (2002), and Ferson and Harvey (1993) are some examples.

  3. A number of studies that includes Lima et al. (2013), Cremers and Weinbaum (2010), Handa and Tiwari (2006), Nitschka (2010), Rapach et al. (2013) studied stock return predictability in US stock market returns. Belo et al. (2014) showed the impact of labor market frictions on asset prices of the US firms. Lim and Hooy (2013) showed persistence and source of non-linear predictability in the stock markets of G7 countries. Wang et al. (2011) studied predictability of moving average rules for the China stock market. McMillan (2010) examined returns predictability using the present value model adjusted for the presence of bubbles. Shamsuddin and Kim (2010) studied Short-Horizon Return Predictability in International Equity Markets.

  4. The dataset used includes stock excess return series from the economies from Western Europe (France, Germany, Netherlands, United Kingdom and Ireland), Central Europe (Austria, Croatia, Hungary, and Switzerland), Southern Europe (Greece, Spain and Italy), Northern Europe(Denmark, Iceland, Sweden, and Norway), and Eastern European (Russia and Slovakia).

  5. AUSTRIA-DS MARKET, CROATIA CROBEX, DENMARK-DS MARKET, FRANCE CAC 40, DAX 30 DS, GREECE-DS MARKET, BUDAPEST (BUX), ICELAND SE ICEX 15, IRELAND-DS MARKET, MILAN MIBTEL-PRICE INDEX, AEX INDEX (AEX) DS-CALC, NORWAY-DS MARKET, RSF EE MT (RUR) INDEX, SLOVAKIA SAX 16, SPAIN-DS MARKET, SWEDEN-DS MARKET, SWITZ-DS MARKET, FTSE 100.

  6. While T-Bill rates are used as risk free rate for most countries included in the study, money market rates (MMR) are used as risk free rates for Austria, Croatia, Russia, Sweden, call money Switzerland, 12-month forward rates for Denmark, Germany, and Netherlands, deposit rates for Ireland, end of the period discount yield for Slovakia, and finally deposit rates for Norway.

References

  • Akgiray, V., & Booth, G. (1988). The stable law model of stock returns. Journal of Business Economics Statistics, 6, 51–57.

    Google Scholar 

  • Akgiray, V. (1989). Conditional heteroskedasticity in time series of stock returns: Evidence and forecasts. Journal of Business, 62, 55–80.

    Article  Google Scholar 

  • Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651–707.

    Article  Google Scholar 

  • Bekaert, G. (1995). Market integration and investment barriers in Emerging Equity markets. The World Bank Economic Review, 9, 75–107.

    Article  Google Scholar 

  • Belo, F., Lin, X., & Bazdresch, S. (2014). Labor hiring, investment, and stock return predictability in the cross section. Journal of Political Economy, 122(1), 129–177.

    Article  Google Scholar 

  • Bidarkota, P. V., & McCulloch, J. H. (2004). Testing for persistence in stock returns with GARCH-stable shocks. Quantitative Finance, 4, 256–265.

    Article  Google Scholar 

  • Bidarkota, P. V., & McCulloch, J. H. (1998). Optimal univariate inflation forecasting with symmetric stable shocks. Journal of Applied Econometrics, 13, 659–670.

    Article  Google Scholar 

  • Buckberg, E. (1995). Emerging stock markets and international asset pricing. The World Bank Economic Review, 9, 51–74.

    Article  Google Scholar 

  • Buckle, D. J. (1995). Bayesian inferences for stable distribution. Journal of American Statistical Association, 90, 605–613.

    Article  Google Scholar 

  • Campbell, J. Y. (2003). Consumption-Based Asset Pricing. In G. M. Constantinides, M. Harris, & R. Stulz (Eds.), Handbook of the Economics of Finance (Vol. 1B, pp. 803–888). Amsterdam: North-Holland.

    Google Scholar 

  • Campbell, J. Y., & Hentschel, L. (1992). No news is good news: An asymmetric model of changing volatility is stock returns. Journal of Financial Economics, 31, 281–318.

    Article  Google Scholar 

  • Cavanagh, C., Ellliot, G., & Stock, J. (1995). Inference in models with nearly integrated regressors. Econometric Theory, 11, 1131–1147.

    Article  Google Scholar 

  • Claessens, S., Dasgupta, S., & Glen, J. (1995). Returns behavior in emerging stock markets. The World Bank Economic Review, 9, 131–151.

    Article  Google Scholar 

  • Clark, P. (1973). A subordinated stochastic process model with finite variance for speculative prices. Econometrica, 41, 135–155.

    Article  Google Scholar 

  • Conrad, J., & Kaul, G. (1988). Time-variation in expected returns. Journal of Business, 61, 409–425.

    Article  Google Scholar 

  • Cremers, M., & Weinbaum, D. (2010). Deviations from put-call parity and stock return predictability. Journal of Financial and Quantitative Analysis, 45(2), 335–367.

    Article  Google Scholar 

  • De Vries, C. G. (1991). On the relation between GARCH and stable process. Journal of Econometrics, 48, 313–324.

    Article  Google Scholar 

  • Fama, E. F. (1991). Efficient capital markets: II. The Journal of Finance, XLVI, 1575–1617.

    Article  Google Scholar 

  • Fama, E. F. (1965). Mandelbrot and stable phypothesis. Journal of Business, 36, 420–429.

    Article  Google Scholar 

  • Ferson, W. E., & Harvey, C. R. (1993). The risk and predictability of international equity returns. Review of Financial Studies, 6(3), 527–566.

    Article  Google Scholar 

  • Goetzman, W. N., & Jorion, P. (1993). Testing the predictive power of dividend yields. Journal of Finance, 48, 663–679.

    Article  Google Scholar 

  • Goose, D., & Kroner, K. F. (1995). The relationship between GARCH and symmetric stable processes, finding the source of tails in financial data. Journal of Empirical Finance, 2, 225–251.

    Article  Google Scholar 

  • Hamilton, J. D., & Susmel, R. (1994). Autoregressive conditional heteroskedasticity and changes in regime. Journal of Econometrics, 64, 307–333.

    Article  Google Scholar 

  • Handa, P., & Tiwari, A. (2006). Does stock return predictability imply improved asset allocation and performance? Evidence from the U.S. stock market (1954–2002). Journal of Business, 79(5), 2423–2468.

    Article  Google Scholar 

  • Hansen, B. E. (1992). The likelihood ratio test under nonstandard conditions: Testing the Markov switching model of GNP. Journal of Applied Econometrics, 7(supplement), S61–S82.

    Article  Google Scholar 

  • Harvey, C. (1995). The risk exposure of the emerging equity markets. The World Bank Economic Review, 9, 19–50.

    Article  Google Scholar 

  • Harvey, C. (1989). Forecasting, structural time series models and the Kalman filter. Cambridge: Cambridge University Press.

    Google Scholar 

  • Haque, M., Hassan, M., & Neal, C. (2004). An empirical examination of stability, predictability, and volatility of Middle Eastern and African emerging stock markets. The Review of Middle East Economics and Finance, 2, 19–42.

    Article  Google Scholar 

  • Haque, M., Hassan, M., & Varela, O. (2001). Stability, risk premia and predictability in Latin American emerging stock markets. Quarterly Journal of Business and Economics, 40, 23–44.

    Google Scholar 

  • Jensen, D. W., & de Vries, C. G. (1991). On the frequency of large stock returns: Putting booms and busts into perspective. Review of Economics and Statistics, 73, 18–24.

    Article  Google Scholar 

  • Lanne, M. (2002). Testing the predictability of stock returns. Review of Economics and Statistics, 84, 407–415.

    Article  Google Scholar 

  • Lima, K.-P., Luob, W., & Kim, J. H. (2013). Are US stock index returns predictable? Evidence from automatic autocorrelation-based tests. Applied Economics, 45, 953–962.

    Article  Google Scholar 

  • Lim, K.-P., & Hooy, C.-W. (2013). Non-linear predictability in G7 stock index returns. The Manchester School, 81(4), 620–637.

    Article  Google Scholar 

  • Mandelbrot, B. (1963). The variation of certain speculative prices. Journal of Business, 36, 394–419.

    Article  Google Scholar 

  • Mankiw, N. G., & Shapiro, M. D. (1986). Do we reject too often? Small sample properties of tests of rational expectations models. Economic Letters, 20, 139–145.

    Article  Google Scholar 

  • Mantegna, R. N., & Stanley, H. E. (1995). Scaling behavior in the dynamics of an economic index. Nature, 376, 46–49.

    Article  Google Scholar 

  • McCulloch, J. H. (1997). Measuring tail thickness in order to estimate the stable index \(\alpha \): A critique. Journal of Business and Economic Statistics, 15(1), 74–81.

    Google Scholar 

  • McCulloch, J. H. (1996). Numerical approximation of the symmetric stable distribution and density. In R. Adler, R. Feldman, & M. S. Taqqu (Eds.), A practical guide to heavy tails: Statistical techniques for analyzing heavy tailed distributions. Boston: Birkhauser.

    Google Scholar 

  • McMillan, D. G. (2010). Present value model, bubbles and returns predictability: Sector-level evidence. Journal of Business Finance & Accounting, 37(5 & 6), 668–686.

    Google Scholar 

  • Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59, 347–370.

    Article  Google Scholar 

  • Nelson, C. R., & Kim, M. J. (1993). Predictable stock returns: The role of small sample bias. Journal of Finance, 48, 641–661.

    Article  Google Scholar 

  • Nitschka, T. (2010). International evidence for return predictability and the implications for long-run covariation of the G7 stock markets. German Economic Review, 11(4), 527–544.

    Article  Google Scholar 

  • Oh, C.S. (1994). Estimation of time-varying term premia of U.S. Treasury securities: using a STARCH model with stable distributions. Unpublished Ph.D. dissertation, The Ohio State University.

  • Pagan, A. R., & Schwert, G. W. (1990). Alternative models for conditional stock volatility. Journal of Econometrics, 45, 267–290.

    Article  Google Scholar 

  • Pesaran, M. H., & Timmermann, A. (1995). Predictability of stock returns: Robustness and economic significance. Journal of Finance, 50, 1201–1228.

    Article  Google Scholar 

  • Rapach, D. E., Strauss, J. K., & Zhou, G. (2013). International stock return predictability: What is the role of the United States? The Journal of Finance, LXVIII(4), 1633–1661.

    Article  Google Scholar 

  • Shamsuddin, A., & Kim, J. H. (2010). Short-Horizon return predictability in international equity markets. The Financial Review, 45, 469–484.

    Article  Google Scholar 

  • Stambaugh, R. (1999). Predictive regressions. Journal of Financial Economics, 54, 375–421.

    Article  Google Scholar 

  • Wang, Z., Zeng, Y., Pan, H., & Li, P. (2011). Predictability of moving average rules and nonlinear properties of stock returns: Evidence from the China stock market. New Mathematics and Natural Computation, 7(2), 267–279.

    Article  Google Scholar 

  • Watson, M. (1986). Univariate detrending methods with stochastic trends. Journal of Monetary Economics, 18, 49–75.

    Article  Google Scholar 

  • Xu, Y. (2004). Small levels of predictability and large economic gains. Journal of Empirical Finance, 11, 247–275.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Business Administration, Gulf University of Science and Technology, N1-128, Block 5, Building 1, Masjid Al Aqsa Street, Mubarak Al Abdullah Area, West Mishref, Kuwait City, Kuwait

    Khurshid M. Kiani

Authors
  1. Khurshid M. Kiani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khurshid M. Kiani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kiani, K.M. On Modelling and Forecasting Predictable Components in European Stock Markets. Comput Econ 48, 487–502 (2016). https://doi.org/10.1007/s10614-015-9510-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10614-015-9510-y

Keywords

JEL Classification

Search

Navigation