Testing the white noise hypothesis in high-frequency housing returns of the United States

Authors

  • Aviral Kumar Tiwari Montpellier Business School, Avenue des Moulins
  • Rangan Gupta Department of Economics, University of Pretoria, Pretoria
  • Juncal Cunado School of Economics, University of Navarra,
  • Xin Sheng Anglia Ruskin University

DOI:

https://doi.org/10.17811/ebl.9.3.2020.178-188

Abstract

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.

References

References

Anagnostidis, P., Varsakelis, C., and Emmanouilides, C.J. (2016). Has the 2008 Financial Crisis Affected Stock Market Efficiency? The Case of Eurozone. Physica A: Statistical Mechanics and its Applications, 447, 116–128.

Aye, G.C., Chang, T., Chen, W.-Y., Gupta, R., and Wohar, M.E. (2018). Testing the Efficiency of the Art Market Using Quantile-Based Unit Root Tests with Sharp and Smooth Breaks. The Manchester School, 86(4), 488-511.

Aye, G.C., Gil-Alana, L.A., and Gupta, R., and Wohar, M.E. (2017). The efficiency of the art market: Evidence from variance ratio tests, linear and nonlinear fractional integration approaches. International Review of Economics & Finance, 51(C), 283-294.

Balcilar, M., Gupta, R., and Miller, S.M. (2014). Housing and the Great Depression. Applied Economics, 46(24), 2966–2981.

Bańbura, M., Giannone, D., and Reichlin, L. (2011). Nowcasting. In M.P. Clements & D.F. Hendry (Eds.), The Oxford Handbook of Economic Forecasting, 193-224. Oxford: Oxford University Press, United Kingdom.

Bartels, R. (1982). The rank version of von Neumann’s ratio test for randomness. Journal of American Statistical Association, 77, 40–46.

Bollerslev, T., Patton, A., and Wang, W. (2016). Daily house price index: construction modelling and longer-run predictions, Journal of Applied Econometrics, 31, 1005-1025.

Brock, W.A., Dechert, W.D., Schieinkman, J.A., and LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric Reviews, 15, 197–235.

Canarella, G., Miller, S., and Pollard, S. (2012). Unit roots and structural change: An application to US housing price indices. Urban Studies, 49, 757–776.

Canarella, G., Gil-Alana, L.A., Gupta, R., and Miller, S.M. (Forthcoming). Persistence and Cyclical Dynamics of U.S. and U.K. House Prices: Evidence from Over 150 Years of Data. Urban Studies.

Case, K.E., Shiller, R.J., 1989. The Efficiency of the Market for Single Family Homes. American Economic Review 79, 125-137.

Case, K.E., Shiller, R.J., 1990. Forecasting Prices and Excess Returns in the Housing Market. Journal of the American Real Estate and Urban Economics Association 18, 253-273.

Charfeddine, L., Khediri, K-.B., Aye, G.C., and Gupta, R. (2018). Time-varying efficiency of developed and emerging bond markets: Evidence from long-spans of historical data. Physica A: Statistical Mechanics and its Applications, 505(C), 632-647.

Emirmahmutoglu, F., Balcilar, M., Apergis, N., Simo-Kengne, B.D., Chang, T., and Gupta, R. (2016). Causal Relationship between Asset Prices and Output in the US: Evidence from State-Level Panel Granger Causality Test. Regional Studies, 50(10), 1728-1741.

Escanciano, J.C., and Lobato, I.N. (2009). An Automatic Portmanteau Test for Serial Correlation. Journal of Econometrics, 151, 140–149.

Escanciano, J.C., and Velasco, C. (2006). Generalized Spectral Tests for the Martingale Difference Hypothesis. Journal of Econometrics, 134, 151–185.

Fama, E. (1965). The behaviour of stock market prices. Journal of Business, 38, 34–105.

Guntermann, K.L., Norrbin, S.C., 1991. Empirical Tests of Real Estate Market Efficiency. Journal of Real Estate Finance and Economics 4, 297-313.

Gupta, R., and Miller, S.M. (2012a). “Ripple effects” and forecasting home prices in Los Angeles, Las Vegas, and Phoenix. The Annals of Regional Science, 48(3), 763-782.

Gupta, R., and Miller, S.M. (2012b). The Time-Series Properties of House Prices: A Case Study of the Southern California Market. The Journal of Real Estate Finance and Economics, 44(3), 339-361.

Gupta, R., and Plakandaras, V. (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, 11(1), 152-165

Herath, S., and Maier (2015). Informational efficiency of the real estate market: A meta-analysis. Journal of Economic Research, 20, 117-168.

Hill, J.B. and Motegi, K. (2019). Testing the white noise hypothesis of stock returns. Economic Modelling, 76, 231-242.

Hong, Y. (1999). Hypothesis testing in time series via the empirical characteristic function: a generalized spectral density approach. Journal of the American Statistical Association, 94, 1201–1220.

Khuntia, S., and Pattanayak, J.K. (2018). Adaptive market hypothesis and evolving predictability of bitcoin. Economics Letters, 167, 26–28.

Leamer, E.E., 2015. Housing really is the business cycle: What survives the lessons of 2008-09? Journal of Money, Credit and Banking, 47(S1), 43-50.

Lim, K.-P., Brooks, R.D., and Kim, J.H. (2008). Financial Crisis and Stock Market Efficiency: Empirical Evidence from Asian Countries. International Review of Financial Analysis, 17, 571–591.

Ljung, G.M., and Box, G.E.P. (1978). On a measure of the lack of fit in time series models. Biometrika 65, 297–303.

Lo, A.W. (2004). The adaptive markets hypothesis: market efficiency from an evolutionary perspective. Journal of Portfolio Management, 30, 15-29.

Lo, A.W. (2005). Reconciling efficient markets with behavioral finance: the adaptive markets hypothesis. Journal of Investment Consulting, 7, 21-44.

Lo, A.W., and MacKinlay, A.C. (1988). Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test. Review of Financial Studies, 1, 41–66.

Nyakabawo, W. V., Miller, S. M., Balcilar, M., Das, S. and Gupta, R., 2015. Temporal Causality between House Prices and Output in the U.S.: A Bootstrap Rolling-window Approach. North American Journal of Economics and Finance, 33(1), 55-73.

Plakandaras, V., Gupta, R., Gil-Alana, L.A., and Wohar, M.E. (2019). Are BRICS exchange rates chaotic? Applied Economics Letters, 26(13), 1104-1110.

Samuelson P.A. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial Management Review, 6, 41–9.

Shao, X. (2011). A bootstrap-assisted spectral test of white noise under unknown dependence. Journal of Econometrics, 162(2), 213-224.

Tiwari, A.K., Aye, G.C., and Gupta, R. (2019). Stock market efficiency analysis using long spans of Data: A multifractal detrended fluctuation approach. Finance Research Letters, 28, 398-411.

Wald, A., and Wolfowtiz, J. (1940). On a test whether two samples are form the same population. Annals of Mathematical Statistics, 11, 147–162.

Wright, J.H. (2000). Alternative Variance-Ratio Tests Using Ranks and Signs. Journal of Business and Economic Statistics, 18, 1–9.

Downloads

Published

13-12-2020

How to Cite

Tiwari, A. K., Gupta, R., Cunado, J., & Sheng, X. (2020). Testing the white noise hypothesis in high-frequency housing returns of the United States. Economics and Business Letters, 9(3), 178–188. https://doi.org/10.17811/ebl.9.3.2020.178-188