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High-Frequency Volatility Forecasting of US Housing Markets

Author

Listed:
  • Mawuli Segnon

    (University of Münster)

  • Rangan Gupta

    (University of Pretoria)

  • Keagile Lesame

    (University of Pretoria)

  • Mark E. Wohar

    (University of Nebraska at Omaha)

Abstract

We propose a logistic smooth transition autoregressive fractionally integrated [STARFI (p, d)] process for modeling and forecasting US housing price volatility. We discuss the statistical properties of the model and investigate its forecasting performance by assuming various specifications for the dynamics underlying the variance process in the model. Using a unique database of daily data on price indices from ten major US cities, and the corresponding daily Composite 10 Housing Price Index, and also a housing futures price index, we find that using the Markov-switching multifractal (MSM) and FIGARCH frameworks for modeling the variance process helps improving the gains in forecast accuracy.

Suggested Citation

  • Mawuli Segnon & Rangan Gupta & Keagile Lesame & Mark E. Wohar, 2021. "High-Frequency Volatility Forecasting of US Housing Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 62(2), pages 283-317, February.
    • Handle: RePEc:kap:jrefec:v:62:y:2021:i:2:d:10.1007_s11146-020-09745-w
    DOI: 10.1007/s11146-020-09745-w
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    1. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2015. "The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US," Applied Economics, Taylor & Francis Journals, vol. 47(22), pages 2259-2277, May.
    2. Lux, Thomas, 2008. "The Markov-Switching Multifractal Model of Asset Returns: GMM Estimation and Linear Forecasting of Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 194-210, April.
    3. Calvet, Laurent & Fisher, Adlai, 2001. "Forecasting multifractal volatility," Journal of Econometrics, Elsevier, vol. 105(1), pages 27-58, November.
    4. Weron, Rafał, 2002. "Estimating long-range dependence: finite sample properties and confidence intervals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(1), pages 285-299.
    5. Christian Conrad & Berthold R. Haag, 2006. "Inequality Constraints in the Fractionally Integrated GARCH Model," Journal of Financial Econometrics, Oxford University Press, vol. 4(3), pages 413-449.
    6. Laurent E. Calvet, 2004. "How to Forecast Long-Run Volatility: Regime Switching and the Estimation of Multifractal Processes," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 49-83.
    7. W. Miles, 2008. "Boom–Bust Cycles and the Forecasting Performance of Linear and Non-Linear Models of House Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 36(3), pages 249-264, April.
    8. Engsted, Tom & Pedersen, Thomas Q., 2014. "Housing market volatility in the OECD area: Evidence from VAR based return decompositions," Journal of Macroeconomics, Elsevier, vol. 42(C), pages 91-103.
    9. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    10. Case, Karl E. & Quigley, John M. & Shiller, Robert J., 2013. "Wealth Effects Revisited 1975-2012," Critical Finance Review, now publishers, vol. 2(1), pages 101-128, July.
    11. Shiller, Robert J., 1998. "Macro Markets: Creating Institutions for Managing Society's Largest Economic Risks," OUP Catalogue, Oxford University Press, number 9780198294184.
    12. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    13. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    14. 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.
    15. Ling, Shiqing & McAleer, Michael, 2002. "NECESSARY AND SUFFICIENT MOMENT CONDITIONS FOR THE GARCH(r,s) AND ASYMMETRIC POWER GARCH(r,s) MODELS," Econometric Theory, Cambridge University Press, vol. 18(3), pages 722-729, June.
    16. Douc, Randal & Roueff, François & Soulier, Philippe, 2008. "On the existence of some processes," Stochastic Processes and their Applications, Elsevier, vol. 118(5), pages 755-761, May.
    17. Bougerol, Philippe & Picard, Nico, 1992. "Stationarity of Garch processes and of some nonnegative time series," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 115-127.
    18. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    19. Liu, Ruipeng & Di Matteo, T. & Lux, Thomas, 2007. "True and apparent scaling: The proximity of the Markov-switching multifractal model to long-range dependence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(1), pages 35-42.
    20. 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.
    21. Clive W.J. Granger, 1999. "Outline of forecast theory using generalized cost functions," Spanish Economic Review, Springer;Spanish Economic Association, vol. 1(2), pages 161-173.
    22. William Miles, 2015. "Regional House Price Segmentation and Convergence in the US: A New Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 50(1), pages 113-128, January.
    23. Bork, Lasse & Møller, Stig V., 2015. "Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection," International Journal of Forecasting, Elsevier, vol. 31(1), pages 63-78.
    24. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    25. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    26. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    27. Henderson, J. Vernon & Ioannides, Yannis M., 1987. "Owner occupancy: Investment vs consumption demand," Journal of Urban Economics, Elsevier, vol. 21(2), pages 228-241, March.
    28. Benoit Mandelbrot & Adlai Fisher & Laurent Calvet, 1997. "A Multifractal Model of Asset Returns," Cowles Foundation Discussion Papers 1164, Cowles Foundation for Research in Economics, Yale University.
    29. Rangan Gupta & Anandamayee Majumdar, 2015. "Forecasting US real house price returns over 1831-2013: evidence from copula models," Applied Economics, Taylor & Francis Journals, vol. 47(48), pages 5204-5213, October.
    30. Haiwei Chen, 2017. "Real Estate Transfer Taxes and Housing Price Volatility in the United States," International Real Estate Review, Global Social Science Institute, vol. 20(2), pages 207-219.
    31. Kui-Wai Li, 2012. "A study on the volatility forecast of the US housing market in the 2008 crisis," Applied Financial Economics, Taylor & Francis Journals, vol. 22(22), pages 1869-1880, November.
    32. William Miles, 2008. "Volatility Clustering in U.S. Home Prices," Journal of Real Estate Research, American Real Estate Society, vol. 30(1), pages 73-90.
    33. Giraitis, Liudas & Kokoszka, Piotr & Leipus, Remigijus, 2000. "Stationary Arch Models: Dependence Structure And Central Limit Theorem," Econometric Theory, Cambridge University Press, vol. 16(1), pages 3-22, February.
    34. Wendy Nyakabawo & Rangan Gupta & Hardik A. Marfatia, 2018. "High Frequency Impact Of Monetary Policy And Macroeconomic Surprises On Us Msas, Aggregate Us Housing Returns And Asymmetric Volatility," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 204-229, December.
    35. Stefan Lundbergh & Timo Teräsvirta, 1999. "Modelling Economic High-Frequency Time Series," Tinbergen Institute Discussion Papers 99-009/4, Tinbergen Institute.
    36. Yu Zhou & Donald Haurin, 2010. "On the Determinants of House Value Volatility," Journal of Real Estate Research, Taylor & Francis Journals, vol. 32(4), pages 377-396, January.
    37. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    38. Montañés, A. & Olmos, L., 2013. "Convergence in US house prices," Economics Letters, Elsevier, vol. 121(2), pages 152-155.
    39. Felix Chan & Michael McAleer, 2002. "Maximum likelihood estimation of STAR and STAR-GARCH models: theory and Monte Carlo evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 509-534.
    40. John Elder & Sriram Villupuram, 2012. "Persistence in the return and volatility of home price indices," Applied Financial Economics, Taylor & Francis Journals, vol. 22(22), pages 1855-1868, November.
    41. Ahdi N. Ajmi & Vassilios Babalos & Fotini Economou & Rangan Gupta, 2014. "Real Estate Markets and Uncertainty Shocks: A Variance Causality Approach," Working Papers 201436, University of Pretoria, Department of Economics.
    42. Kazakevičius, Vytautas & Leipus, Remigijus, 2002. "On Stationarity In The Arch(∞) Model," Econometric Theory, Cambridge University Press, vol. 18(1), pages 1-16, February.
    43. Norman Miller & Liang Peng, 2006. "Exploring Metropolitan Housing Price Volatility," The Journal of Real Estate Finance and Economics, Springer, vol. 33(1), pages 5-18, August.
    44. Gordon W. Crawford & Michael C. Fratantoni, 2003. "Assessing the Forecasting Performance of Regime‐Switching, ARIMA and GARCH Models of House Prices," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 31(2), pages 223-243, June.
    45. Joseph Fairchild & Jun Ma & Shu Wu, 2015. "Understanding Housing Market Volatility," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(7), pages 1309-1337, October.
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    Cited by:

    1. Afees A. Salisu & Ahamuefula E. Ogbonna & Elie Bouri & Rangan Gupta, 2024. "Climate Risks and Prediction of Sectoral REITs Volatility: International Evidence," Working Papers 202434, University of Pretoria, Department of Economics.
    2. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2022. "Forecasting realized volatility of international REITs: The role of realized skewness and realized kurtosis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 303-315, March.
    3. 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.
    4. Jiqian Wang & Rangan Gupta & Oğuzhan Çepni & Feng Ma, 2023. "Forecasting international REITs volatility: the role of oil-price uncertainty," The European Journal of Finance, Taylor & Francis Journals, vol. 29(14), pages 1579-1597, September.
    5. Bonato, Matteo & Çepni, Oğuzhan & Gupta, Rangan & Pierdzioch, Christian, 2021. "Do oil-price shocks predict the realized variance of U.S. REITs?," Energy Economics, Elsevier, vol. 104(C).
    6. Rangan Gupta & Damien Moodley, 2023. "Housing Search Activity and Quantiles-Based Predictability of Housing Price Movements in the United States," Working Papers 202335, University of Pretoria, Department of Economics.

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    More about this item

    Keywords

    US housing prices; GARCH processes; MSM processes; Model confidence set;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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