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Testing the White Noise Hypothesis in High-Frequency Housing Returns of the United States

Author

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  • Aviral Kumar Tiwari

    (Montpellier Business School, 2300, Avenue des Moulins, 34185, Montpellier Cedex 4 0002, France)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, South Africa)

  • Juncal Cunado

    (University of Navarra, School of Economics, Edificio Amigos, E-31080 Pamplona, Spain)

  • Xin Sheng

    (Lord Ashcroft International Business School, Anglia Ruskin University, Chelmsford, CM1 1SQ, U.K.)

Abstract

In the pure time-series sense, weak-form of efficiency of the housing market would imply unpredictability of housing returns. Given this, utilizing a daily dataset of aggregate housing market returns of the United States, we test whether housing market returns are white noise using the blockwise wild bootstrap in a rolling-window framework. We investigate the dynamic evolution of housing market efficiency and find that the white noise hypothesis is accepted in most windows associated with non-crisis periods. However, for some periods before the burst of the housing market bubbles, and during the subprime mortgage crisis, European sovereign debt crisis and the Brexit, the white noise hypothesis is rejected, indicating that the housing market is inefficient in periods of turbulence. Our results have important implications for economic agents.

Suggested Citation

  • Aviral Kumar Tiwari & Rangan Gupta & Juncal Cunado & Xin Sheng, 2019. "Testing the White Noise Hypothesis in High-Frequency Housing Returns of the United States," Working Papers 201952, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201952
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    1. Nyakabawo, Wendy & Miller, Stephen M. & Balcilar, Mehmet & Das, Sonali & Gupta, Rangan, 2015. "Temporal causality between house prices and output in the US: A bootstrap rolling-window approach," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 55-73.
    2. Charfeddine, Lanouar & Khediri, Karim Ben & Aye, Goodness C. & Gupta, Rangan, 2018. "Time-varying efficiency of developed and emerging bond markets: Evidence from long-spans of historical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 632-647.
    3. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    4. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2014. "Housing and the Great Depression," Applied Economics, Taylor & Francis Journals, vol. 46(24), pages 2966-2981, August.
    5. Rangan Gupta & Stephen Miller, 2012. "“Ripple effects” and forecasting home prices in Los Angeles, Las Vegas, and Phoenix," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 48(3), pages 763-782, June.
    6. Theodore Panagiotidis, 2005. "Market capitalization and efficiency. Does it matter? Evidence from the Athens Stock Exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 15(10), pages 707-713.
    7. Tim Bollerslev & Andrew J. Patton & Wenjing Wang, 2016. "Daily House Price Indices: Construction, Modeling, and Longer‐run Predictions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1005-1025, September.
    8. Escanciano, J. Carlos & Lobato, Ignacio N., 2009. "An automatic Portmanteau test for serial correlation," Journal of Econometrics, Elsevier, vol. 151(2), pages 140-149, August.
    9. Wright, Jonathan H, 2000. "Alternative Variance-Ratio Tests Using Ranks and Signs," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 1-9, January.
    10. Guntermann, Karl L & Norrbin, Stefan C, 1991. "Empirical Tests of Real Estate Market Efficiency," The Journal of Real Estate Finance and Economics, Springer, vol. 4(3), pages 297-313, September.
    11. Hill, Jonathan B. & Motegi, Kaiji, 2019. "Testing the white noise hypothesis of stock returns," Economic Modelling, Elsevier, vol. 76(C), pages 231-242.
    12. Aye, Goodness C. & Gil-Alana, Luis A. & Gupta, Rangan & Wohar, Mark E., 2017. "The efficiency of the art market: Evidence from variance ratio tests, linear and nonlinear fractional integration approaches," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 283-294.
    13. Clements, Michael P. & Hendry, David F. (ed.), 2011. "The Oxford Handbook of Economic Forecasting," OUP Catalogue, Oxford University Press, number 9780195398649.
    14. Georgios Bampinas & Stilianos Fountas & Theodore Panagiotidis, 2015. "The Day-of-the-Week Effect is Weak: Evidence from the European Real Estate Sector," Working Paper series 15-19, Rimini Centre for Economic Analysis.
    15. Gunther Maier & Shanaka Herath, 2010. "Efficiency Of The Real Estate Market: A Meta-Analysis," ERES eres2010_114, European Real Estate Society (ERES).
    16. Khuntia, Sashikanta & Pattanayak, J.K., 2018. "Adaptive market hypothesis and evolving predictability of bitcoin," Economics Letters, Elsevier, vol. 167(C), pages 26-28.
    17. Case, Karl E & Shiller, Robert J, 1989. "The Efficiency of the Market for Single-Family Homes," American Economic Review, American Economic Association, vol. 79(1), pages 125-137, March.
    18. Vasilios Plakandaras & Rangan Gupta & Luis A. Gil-Alana & Mark E. Wohar, 2019. "Are BRICS exchange rates chaotic?," Applied Economics Letters, Taylor & Francis Journals, vol. 26(13), pages 1104-1110, July.
    19. Tiwari, Aviral Kumar & Aye, Goodness C. & Gupta, Rangan, 2019. "Stock market efficiency analysis using long spans of Data: A multifractal detrended fluctuation approach," Finance Research Letters, Elsevier, vol. 28(C), pages 398-411.
    20. Anagnostidis, P. & Varsakelis, C. & Emmanouilides, C.J., 2016. "Has the 2008 financial crisis affected stock market efficiency? The case of Eurozone," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 116-128.
    21. Shao, Xiaofeng, 2011. "A bootstrap-assisted spectral test of white noise under unknown dependence," Journal of Econometrics, Elsevier, vol. 162(2), pages 213-224, June.
    22. Escanciano, J. Carlos & Velasco, Carlos, 2006. "Generalized spectral tests for the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 134(1), pages 151-185, September.
    23. Giorgio Canarella & Stephen Miller & Stephen Pollard, 2012. "Unit Roots and Structural Change," Urban Studies, Urban Studies Journal Limited, vol. 49(4), pages 757-776, March.
    24. Karl E. Case & Robert J. Shiller, 1990. "Forecasting Prices and Excess Returns in the Housing Market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 18(3), pages 253-273, September.
    25. Edward E. Leamer, 2015. "Housing Really Is the Business Cycle: What Survives the Lessons of 2008–09?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(S1), pages 43-50, March.
    26. Rangan Gupta & Stephen Miller, 2012. "The Time-Series Properties of House Prices: A Case Study of the Southern California Market," The Journal of Real Estate Finance and Economics, Springer, vol. 44(3), pages 339-361, April.
    27. Rangan Gupta & Vasilios Plakandaras, 2019. "Efficiency in BRICS Currency Markets Using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability," Journal of Economics and Behavioral Studies, AMH International, vol. 11(1), pages 152-165.
    28. Goodness C. Aye & Tsang Yao Chang & Wen†Yi Chen & Rangan Gupta & Mark Wohar, 2018. "Testing the Efficiency of the Art Market Using Quantile†Based Unit Root Tests with Sharp and Smooth Breaks," Manchester School, University of Manchester, vol. 86(4), pages 488-511, July.
    29. Lim, Kian-Ping & Brooks, Robert D. & Kim, Jae H., 2008. "Financial crisis and stock market efficiency: Empirical evidence from Asian countries," International Review of Financial Analysis, Elsevier, vol. 17(3), pages 571-591, June.
    30. Kian‐Ping Lim & Robert Brooks, 2011. "The Evolution Of Stock Market Efficiency Over Time: A Survey Of The Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 69-108, February.
    31. Furkan Emirmahmutoglu & Mehmet Balcilar & Nicholas Apergis & Beatrice D. Simo-Kengne & Tsangyao Chang & Rangan Gupta, 2014. "Causal relationship between asset prices and output in the US: Evidence from state-level panel Granger causality test," Working Papers 201411, University of Pretoria, Department of Economics.
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    Cited by:

    1. Goodness C. Aye & Christina Christou & Rangan Gupta & Christis Hassapis, 2024. "High-Frequency Contagion between Aggregate and Regional Housing Markets of the United States with Financial Assets: Evidence from Multichannel Tests," The Journal of Real Estate Finance and Economics, Springer, vol. 69(2), pages 253-276, August.

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    More about this item

    Keywords

    Blockwise wild bootstrap; Randomized block size; Serial correlation; Weak-form efficiency; White noise test; Daily US housing returns;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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