A high-frequency analysis of the interactions between REIT return and volatility

被引:8
|
作者
Zhou, Jian [1 ]
机构
[1] Univ Guelph, Coll Business & Econ, Real Estate & Housing, Guelph, ON N1G 2W1, Canada
关键词
High-frequency data; Leverage effect; Volatility feedback effect; Causality; REIT; UNIT-ROOT TEST; ASYMMETRIC VOLATILITY; GRANGER CAUSALITY; STOCK RETURNS; MODEL; BREAK; NEWS;
D O I
10.1016/j.econmod.2016.03.022
中图分类号
F [经济];
学科分类号
02 ;
摘要
This paper makes the first attempt in the real estate literature to test the two hypotheses depicting the interactions between return and volatility - the leverage effect and volatility feedback effect. By employing high frequency data, we find that both leverage and volatility feedback effects are at work and highly persistent in the U.S. REIT market. The leverage effect dominates the volatility feedback effect. More importantly, both effects are found nonlinear - a feature matching the tendency of the financial market to often change its behavior. Further analysis suggests that the nonlinearity arises from multiple sources (e.g. regime switching, structural breaks, and outliers). Our findings are robust to different data sampling frequencies. All in all, they lead to a better understanding of the recent movement of REIT volatility and have profound implications for asset pricing. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:102 / 108
页数:7
相关论文
共 50 条
  • [1] Forecasting REIT volatility with high-frequency data: a comparison of alternative methods
    Zhou, Jian
    APPLIED ECONOMICS, 2017, 49 (26) : 2590 - 2605
  • [2] High-frequency return and volatility spillovers among cryptocurrencies
    Sensoy, Ahmet
    Silva, Thiago Christiano
    Corbet, Shaen
    Tabak, Benjamin Miranda
    APPLIED ECONOMICS, 2021, 53 (37) : 4310 - 4328
  • [3] A high-frequency analysis of return and volatility spillovers in the European sovereign bond market
    O'Sullivan, Conall
    Papavassiliou, Vassilios G.
    EUROPEAN JOURNAL OF FINANCE, 2021,
  • [4] Forecasting the return distribution using high-frequency volatility measures
    Hua, Jian
    Manzan, Sebastiano
    JOURNAL OF BANKING & FINANCE, 2013, 37 (11) : 4381 - 4403
  • [5] An examination of the REIT return–implied volatility relation: a frequency domain approach
    Anoruo E.
    Murthy V.N.R.
    Journal of Economics and Finance, 2017, 41 (3) : 581 - 594
  • [6] Do high-frequency measures of volatility improve forecasts of return distributions?
    Maheu, John M.
    McCurdy, Thomas H.
    JOURNAL OF ECONOMETRICS, 2011, 160 (01) : 69 - 76
  • [7] Volatility analysis in high-frequency financial data
    Wang, Yazhen
    Zou, Jian
    WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, 2014, 6 (06) : 393 - 404
  • [8] EQUITY RISK: MEASURING RETURN VOLATILITY USING HISTORICAL HIGH-FREQUENCY DATA
    Chow, Alan
    Lahtinen, Kyre
    STUDIES IN BUSINESS AND ECONOMICS, 2019, 14 (03) : 60 - 71
  • [9] Measuring High-Frequency Causality Between Returns, Realized Volatility, and Implied Volatility
    Dufour, Jean-Marie
    Garcia, Rene
    Taamouti, Abderrahim
    JOURNAL OF FINANCIAL ECONOMETRICS, 2012, 10 (01) : 124 - 163
  • [10] Multiscale Volatility Analysis for Noisy High-Frequency Prices
    Leung, Tim
    Zhao, Theodore
    RISKS, 2023, 11 (07)