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Illiquidity and risk of commercial timberland assets in the United States

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  • Mei, Bin

Abstract

Using the BDS independent test and the bootstrapping method, this paper examines the relationship between return and risk of various timberland investment vehicles and the holding period. Results from the BDS test reject the null hypothesis of independent and identically distributed (i.i.d.) returns and results from the simulation indicate that the average quarterly return remains almost constant and thus independent of the holding period but the average quarterly risk (standard deviation) varies among different timberland investment vehicles. For private-equity timberland assets, the average periodic risk increases with the holding period, whereas for public-equity timberland assets, it stays relatively constant. Overall, there is some evidence that private-equity timberland returns as measured by various NCREIF timberland indices tend not to be independent and identically distributed, a violation of the key assumption for the modern portfolio theory.

Suggested Citation

  • Mei, Bin, 2015. "Illiquidity and risk of commercial timberland assets in the United States," Journal of Forest Economics, Elsevier, vol. 21(2), pages 67-78.
    • Handle: RePEc:eee:foreco:v:21:y:2015:i:2:p:67-78
    DOI: 10.1016/j.jfe.2015.01.003
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    References listed on IDEAS

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    Cited by:

    1. Mei, Bin, 2019. "Timberland investments in the United States: A review and prospects," Forest Policy and Economics, Elsevier, vol. 109(C).
    2. Mei, Bin & Clutter, Michael L., 2020. "Return and information transmission of public and private timberland markets in the United States," Forest Policy and Economics, Elsevier, vol. 113(C).
    3. Restrepo, Hector & Zhang, Weiyi & Mei, Bin, 2020. "The time-varying role of timberland in long-term, mixed-asset portfolios under the mean conditional value-at-risk framework," Forest Policy and Economics, Elsevier, vol. 113(C).

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    More about this item

    Keywords

    Forest Investment; Real estate; Valuation; Volatility;
    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
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • R32 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other Spatial Production and Pricing Analysis

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