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A Time Series Analysis of U.K. Construction and Real Estate Indices

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Abstract

This study assess the nonlinear behavior of U.K. Construction and Real Estate indices. Standard unit root tests show that both time series are I(1) processes. However, the empirical results show that the returns series for both indices deviate from the null hypothesis of white noise. Moreover, we have found evidence of nonlinearity but strong evidence against chaos for the returns series. Further tests show that the source of nonlinearity is rather different. Hence, the Construction index returns series displays weak nonlinear forecastability, typical of nonlinear deterministic processes, whereas the Real Estate index could be characterized as a stationary process about a nonlinear deterministic trend.

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Notes

  1. It must be pointed out that any potential benefit derived from such knowledge may be short lived since it is likely to be competed away.

  2. Currently there is no options trading on the Real Estate and Construction Indices.

  3. http://www.ftse.com/Indices/UK_Indices/Downloads/AppendixB_Reference_Codes.pdf (accessed 10 November 2010).

  4. Ibid.

  5. FTSE Calculation methodology can be found at: http://www.ftse.com/Indices/UK_Indices/Downloads/uk_calculation.pdf (accessed 10 November 2010).

  6. Testing for one unit root in the first-differenced data allows us to strongly reject the null hypothesis of a second unit root. Results are available upon request.

  7. The evidence for Construction is weaker when using the Bartlett kernel.

  8. As Cushman (2002) points out, this formula differs from that in Bierens (1997) by taking the absolute value in the denominator.

  9. These test statistics, with the exception of T 1(m) and T 2(m), can be computed using the program EasyReg, written by Herman J. Bierens and freely available at the URL http://econ.la.psu.edu/~hbierens/EASYREG.HTM.

  10. is the information set at time t − 1.

  11. As noted by these authors, however, the robustness of the tests to GARCH cannot automatically be taken to extend to other types of heteroskedasticity.

  12. We are grateful to Professor Shitani for providing the computer code to compute the statistical tests concerning Lyapunov exponents, as described in Shintani and Linton (2004).

  13. Hereafter, FK test.

References

  • Abhyankar, A., Copeland, L. S., & Wong, W. (1995). Nonlinear dynamics in real-time equity market indices: Evidence from the U.K. Economic Journal, 105, 864–880.

    Article  Google Scholar 

  • Abhyankar, A., Copeland L. S., & Wong, W. (1997). Uncovering nonlinear structure in real-time stock-market indexes: The S&P 500, the DAX, the Nikkei 225, and the FTSE-100. Journal of Business & Economic Statistics, 15(1), 1–14.

    Google Scholar 

  • Andrews, D. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica, 59, 817–854.

    Article  Google Scholar 

  • Barnett, W. A., Gallant, A. R., & Hinich, M. J. (1997). A single-blind controlled competition among tests for nonlinearity and chaos. Journal of Econometrics, 82, 157–192.

    Article  Google Scholar 

  • Belaire-Franch, J. (2003). A note on resampling the integration across the correlation integral with alternative ranges. Econometric Reviews, 22, 337–349.

    Article  Google Scholar 

  • Belaire-Franch, J., & Opong, K. K. (2005). Some evidence of random walk behaviour of euro exchange rates using ranks and signs. Journal of Banking and Finance, 29, 1631–1643.

    Article  Google Scholar 

  • Bierens, H. (1997). Testing the unit root with drift hypothesis against nonlinear trend stationarity, with an application to the U.S. price level and interest rate. Journal of Econometrics, 81, 29–64.

    Article  Google Scholar 

  • Brealey, R. A. (1970). The distribution and independence of successive rates of return from the British equity market. Journal of Business Finance, 2, 29–40.

    Google Scholar 

  • Brooks, C., & Tsolacos, S. (2001a). Forecasting real estate returns using financial spreads. Journal of Property Research, 3, 235–248.

    Article  Google Scholar 

  • Brooks, C., & Tsolacos, S. (2001b). Linkages between property asset returns and interest rates: Evidence for the UK. Applied Economics, 33, 711–719.

    Google Scholar 

  • Cochrane, J. (1988). How big is the random walk in GDP? Journal of Political Economy, 96(5), 893–920.

    Google Scholar 

  • Campbell, B., & Dufour, J.-M. (1997). Exact nonparametric tests of orthogonality and random walk in the presence of a drift parameter. International Economic Review, 38, 151–173.

    Article  Google Scholar 

  • Connock, M. (2002). Is arbitrage possible in the housing market? Applied Economics Letters, 9, 91–93.

    Article  Google Scholar 

  • Cunningham, S. W. (1973). The predictability of british stock market prices. Applied Statistics, 22, 215–231.

    Google Scholar 

  • Cushman, D. O. (2002). Nonlinear trends and co-trending in canadian money demand. Studies in Nonlinear Dynamics & Econometrics, 6, 1–27.

    Google Scholar 

  • Davidson, R., & Flachaire, E. (2000). The wild bootstrap, tamed at last. Mimeo, GREQAM, Université de la Mediternée.

  • Dryden, M. M. (1970). A statistical study of U.K. share prices. Scottish Journal of Political Economy, 17, 369–389.

    Article  Google Scholar 

  • Finkenstadt, B., & Kuhbier, P. (1995). Forecasting nonlinear economic time series: A simple test to accompany the nearest neighbor approach. Empirical Economics, 20, 243–263.

    Article  Google Scholar 

  • Garino, G., & Sarno, L. (2004). Speculative bubbles in U.K. house prices: Some new evidence. Southern Economic Journal, 4, 777–795.

    Article  Google Scholar 

  • Gencay, R., & Dechert, W. (1992). An algorithm for the n Lyapunov exponents of an n-dimensional unknown dynamical system. Physica D, 59, 142–157.

    Google Scholar 

  • Hobijn, B., Franses, P. H., & Ooms, M. (1998). Generalizations of the kpss-test for stationarity. Report 9802/A, Econometric Institute, Erasmus University Rotterdam.

  • Hsieh, D. A. (1991). Chaos and nonlinear dynamics: Application to financial markets. The Journal of Finance, 46, 1839–1877.

    Article  Google Scholar 

  • Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52, 169–210.

    Article  Google Scholar 

  • Kaplan, D. T. (1994). Exceptional events as evidence for determinism. Physica D, 73(1–2), 38–48.

    Article  Google Scholar 

  • Kendall, M. G. (1953). The analysis of economic time-series. Part I: Prices. Journal of the Royal Statistical Society, 96, 11–25.

    Google Scholar 

  • Kleiman, R. T., Payne, J. E., & Sahu, A. P. (2002). Random walks and market efficiency: Evidence from international real estate markets. Journal of Real Estate Research, 24, 279–297.

    Google Scholar 

  • Kwiatkowski, D., Phillips, P., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54, 159–178.

    Article  Google Scholar 

  • Lee S., & Ward, C. W. R. (2000). Persistence of UK real estate returns: A markov chain analysis. Journal of Asset Management, 1(3), 217–230.

    Google Scholar 

  • Lee, T., White, H., & Granger, C. W. J. (1993). Testing for neglected nonlinearity in time series models. Journal of Econometrics, 56, 269–290.

    Article  Google Scholar 

  • Liu, C. Y., & He, J. (1991). A variance ratio test of random walks in foreign exchange rates. Journal of Finance, 46, 773–785.

    Article  Google Scholar 

  • Lo, A. W., & Mackinlay, A. C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. The Review of Financial Studies, 1, 41–66.

    Article  Google Scholar 

  • Lo, A. W., & Mackinlay, A. C. (1989). The size and power variance ratio test in finite samples: A Monte Carlo investigation. Journal of Econometrics, 40, 203–238.

    Article  Google Scholar 

  • Luger, R. (2003). Exact non-parametric tests for a random walk with unknown drift under conditional heteroscedasticity. Journal of Econometrics, 115, 259–276.

    Article  Google Scholar 

  • Macgregor, B. D., & Schwann, G. M. (2003). Common features in UK commercial real estate returns. Journal of Property Research, 20, 23–48.

    Google Scholar 

  • Newey, W., & West, K. (1994). Automatic lag selection in covariance estimation. Review of Economic Studies, 61, 631–654.

    Article  Google Scholar 

  • Ng, S., & Perron, P. (2001). Lag length selection and the construction of unit root tests with good size and power. Econometrica, 69, 1519–1554.

    Article  Google Scholar 

  • Nychka, D., Ellner, S., Gallant, A. R., & McCaHrey, D. (1992). Finding chaos in noisy system. Journal of the Royal Statistical Society, Series B, 54, 399–426.

    Google Scholar 

  • Opong, K. K., Mulholland, G., Fox, A. F., & Farahmand, K. (1999). The behaviour of some U.K. equity indices: An application of hurst and bds tests. Journal of Empirical Finance, 6, 267–282.

    Article  Google Scholar 

  • Pan, M.-S., Liu, Y. A., & Bastin, H. (1996). An examination of the short-term and long-term behaviour of foreign exchange rates. Financial Review, 31, 603–622.

    Google Scholar 

  • Payne, J. E., & Sahu, A. P. (2004). Random walks, co-integration, and the transmission of shocks across global real estate and equity markets. Journal of Economics and Finance, 28, 198–210.

    Article  Google Scholar 

  • Perron, P., & Ng, S. (1996). Useful modifications to unit root tests with dependent errors and their local asymptotic properties. Review of Economic Studies, 63, 435–465.

    Article  Google Scholar 

  • Phillips, P., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75, 335–346.

    Article  Google Scholar 

  • Poon, H., & Taylor, S. J. (1992). Stock return and volatility: An empirical study of U.K. stock market. Journal of Banking and Finance, 16, 37–59.

    Article  Google Scholar 

  • Psaradakis, Z. (2000). p-Value adjustments for multiple tests for nonlinearity. Studies in Nonlinear Dynamics and Econometrics, 4, 95–100.

    Google Scholar 

  • Rosenstein, M. T., Collins, J. J., & De Luca, C. J. (1993). A practical method for calculating largest Lyapunov exponents from small data sets. Physica D, 65, 117–134.

    Article  Google Scholar 

  • Scheinkman, J., & LeBaron, B. (1989). Nonlinear dynamics and stock returns. Journal of Business, 62(3), 311–337.

    Google Scholar 

  • Schreiber, T., & Schmitz, A. (1996). Improved surrogate data for nonlinearity tests. Physical Review Letters, 77, 635.

    Article  Google Scholar 

  • Shintani, M., & Linton, O. (2004). Nonparametric neural network estimation of Lyapunov exponents and a direct test for chaos. Journal of Econometrics, 120, 1–33.

    Article  Google Scholar 

  • Teräsvirta, T., Lin, C. F., & Granger, C. W. J. (1993). Power of the neural network linearity test. Journal of Time Series Analysis, 14(2), 209–220.

    Article  Google Scholar 

  • Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., & Farmer, J. D. (1992). Testing for nonlinearity in time series: The method of surrogate data. Physica D, 58, 77–94.

    Article  Google Scholar 

  • Whang, Y. J., & Linton, O. (1999). The asymptotic distribution of nonparametric estimates of the Lyapunov exponent for stochastic time series. Journal of Econometrics, 91, 1–42.

    Article  Google Scholar 

  • White, H. (1989). An additional hidden unit test for neglected nonlinearity in multilayer feedforward networks. In Proceedings of the international joint conference on neural networks, Washington, DC (pp. 451–455). New York: IEEE Press.

    Chapter  Google Scholar 

  • Willey, T. (1992). Testing for nonlinear dependence in daily stock indices. Journal of Economics and Business, 44, 63–76.

    Article  Google Scholar 

  • Wolf, A., Swift, J., Swinney, H., & Vastano, J. (1985). Determining Lyapunov exponents from a time series. Physica D, 16, 285–317.

    Article  Google Scholar 

  • Wright, J. H. (2000). Alternative variance-ratio tests using ranks and signs. Journal of Business & Economic Statistics, 18, 1–9.

    Google Scholar 

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Acknowledgement

We acknowledge an anonymous referee for his/her useful comments which helped to improve the paper.

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Authors and Affiliations

  1. Faculty of Economics, Department of Economic Analysis, University of Valencia, 46022, Valencia, Spain

    Jorge Belaire-Franch

  2. Department of Accounting and Finance, University of Glasgow, 65-71 Southpark Avenue, Glasgow, Scotland, G12 8LE

    Kwaku K. Opong

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  1. Jorge Belaire-Franch
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Correspondence to Jorge Belaire-Franch.

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Belaire-Franch, J., Opong, K.K. A Time Series Analysis of U.K. Construction and Real Estate Indices. J Real Estate Finan Econ 46, 516–542 (2013). https://doi.org/10.1007/s11146-011-9327-y

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