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Real-Estate Risk Effects on Financial Institutions’ Stock Return Distribution: a Bivariate GARCH Analysis

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Abstract

This paper examines two relationships using the bivariate generalized autoregressive conditionally heteroskedastic (GARCH) methodology. First, the relationship between equity returns of commercial banks, savings and loans (S&Ls) and life insurance companies (LICs), and those of the real-estate investment trusts (REITs), a proxy for the real-estate sector performance. Second, the relationship between conditional volatilities of the stock returns of these financial intermediaries (FIs) and that of REITs. The former relationship allows the spillover of returns between the real-estate and the financial intermediation sector to be analyzed. The latter allows an investigation of the prevalence, direction and strength of inter-sectoral risk transmission to be carried out. Several interesting results are obtained. First, the equity returns of the FIs considered follow a GARCH process and should be modeled accordingly. Second, as found in the literature, returns on REITs should be modeled using the Fama-French multiple factor model. However, this model has to be extended to incorporate a GARCH error structure. Third, all FI returns considered are highly sensitive to REIT returns and the effects are both statistically and economically significant. This is an indication that shocks to REITs returns spillover to the former markets. Fourth, spillover of increased volatility in the real-estate sector to S&Ls and LICs is significant but not to commercial banks.

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Notes

  1. Most recently, this problem has manifested itself with the turmoil in the sub-prime mortgage markets resulting in huge losses to commercial and investment banks, hedge funds, and others, and threatening the stability of the world financial system. As fallout from this crisis, in December 2007, Washington Mutual, the nations’ largest thrift, slashed its dividend payment, laid off more than 3,000 workers, and set aside $1.6 billions for loan losses in the quarter. Similarly, Citigroup had to take a $7.5 billion investment from Abu Dhabi to shore up its finances. Freddie Mac and Fannie Mae also announced sales of $6 billion and $7 billion in preferred stocks, respectively, to deal with the turmoil. Source: CNNFN.Com December 11, 2007.

  2. Government sponsored enterprises (GSEs, Fannie Mae, Ginnie Mae, Freddie Mac), mortgage bankers and brokers, and real-estate agents and brokers also play a role in the mortgage market. GSEs specialize in loan securitization by creating mortgage-pass-through securities. Mortgage bankers and brokers originate loans, fund them, and subsequently bundle and sell them to GSEs and institutional investors. Real-estate agents and brokers bring buyers and sellers of properties together and suggest financing alternatives. In some markets, these firms buy property to sell at a later date. Nationally, there are currently about two million active real-estate agents and brokers associated with about 100,000 firms (White 2006). All of these institutions are affected by changes in the real-estate market, though in different ways and by different degrees, depending on the extent and the nature of their real-estate exposure and market activity.

  3. Elyasiani and Mansur (1998) review the literature on interest rate sensitivity of U.S. bank stock returns. For a comparative analysis of interest rate sensitivities of US, German and Japanese bank stock returns see Elyasiani and Mansur (2003). For a study on rate sensitivity of European bank stock returns, see Stevenson (2002a).

  4. E.g., interest only loans were about 23% of all mortgages in the first half of 2005 (Olszowy 2006).

  5. To avoid spurious results due to misspecification, the Augmented Dickey–Fuller and Phillips–Perron tests of stationarity are performed. The findings indicate that all variables, except long-term interest rate, follow an integrated process of order zero (I (0)), and thus are considered to be stationary. The long-term interest rate series follows an I (1) process, but its first difference series DI is an I (0) process and is, therefore, used in estimation.

  6. For further explanation of GARCH models and their properties see Elyasiani and Mansur (1998, 2003).

  7. There is a debate on whether the performance of REITs follows that of the real estate market closely. Some authors have indicated that REITs tend to behave like stocks, rather than real estate. For example, Peterson and Hsieh (1997) use the model employed by Fama and French (1993) to analyze monthly returns on NYSE, ASE, and NASDAQ traded REITs. They find that over the 1976–1992 period, the risk premiums between REITs and equity markets were indeed significantly related. Alternative measures of real estate sector performance include the Morgan Stanley Capital International and the SNL US REIT indexes. However, data on these indexes are available only sine 1995 and 1989, respectively, and do not cover most of our sample period. The OLS estimates of the coefficients for the latter index are positive and significant for banks and S&Ls but insignificant for the LICs. We would like to thank Ms. Elizabeth Schoen of the SNL Financial LC for providing these data.

  8. As yet an additional step, a likelihood ratio (LR) test is also conducted to determine the validity of the proposed model. In the LR test statistic specified below, L refers to the log of the likelihood function. The χ2 value of -1.14 rejects the bidirectional causality model in favor of the maintained model (Eqs. 1–6). \({\text{LR}} = - 2{\left[ {\ln {\left( {{\text{L}}_{{{\text{urrestricted}}}} } \right)} - \ln {\left( {{\text{L}}_{{{\text{restricted}}}} } \right)}} \right]} \sim \chi ^{2} {\left( {{\text{1 degree of freedom}}} \right)}\).

  9. Mueller (2002) does not specify the status of the real-estate cycle in 2003 and 2004. However, based on his assessment of the real-estate cyclical behavior in the 1990s and 2000s, it can be argued that the downward trend in the real-estate financial cycle continued in 2003 and 2004. This conclusion can be supported by the following market conditions: (a) the demand for office spaces in 1999 and 2000 were higher than the long term U.S. trend, due to the growth of the technology industry. With the technology bubble burst in 2001, annual office employment declined by over 1% and office demand declined by over 2% (Mueller 2002). This contributed adversely to the “physical market cycle”; (b) Muller predicted that the office employment would return to its average growth rate by 2004 and that the overall office demand would follow suit by the middle of the decade; (c) there is a substantial lag between the historical movement in the office physical market and the corresponding movements in the financial cycle. This lag amounted to 1 to 5 years over the last 20 years (Mueller 2002). Due to the built-in lag between the physical and financial cycles and the obvious decline in the demand for physical assets in early 2000, it is reasonable to conclude that the downward financial trend that started in 2001 continued into 2003 and 2004.

  10. Chan et al. (1990) develop a model and identify a set of pre-specified macroeconomic risk factors that explain REIT returns. Their findings suggest that REITs behave in a similar fashion to small capitalization stocks, rather than large capitalization stocks. Furthermore, the term and risk premiums and changes in industrial production are found to be important factors in explaining the average variation in REIT returns.

  11. The advantage of using monthly data is that the noise caused by settlement and clearing delays, which are found to be a significant determinant of returns in high frequency data, is less influential.

  12. Alford (1992) and Lie and Lie (2002) find that SIC-based portfolio disaggregation maintains much of the portfolio characteristics compared to non-industry grouping techniques based on size or profitability.

  13. Presence of multicollinearity is investigated by examining the Conditional Index (CI) values calculated for Eqs. 1 and 3. The CI values are 15.37 and 1.88, respectively. A Conditional Index is defined as the ratio of ((Maximum Eigenvalue)/(Minimum Eigenvalue))1/2. Although statistical significance of the Conditional Indexes can not be tested, index values greater than 30 are understood to indicate severe multicollinearity (Gujarati 2003, p. 362). The index values calculated for our model suggest that multicollinearity is not a serious problem. It is notable, however, that given the magnitude and significance of the correlation coefficient between contemporaneous market and REITs portfolio returns (ρ = .54), it is difficult to completely separate their effects on the bank portfolio returns.

  14. Schrand (1997) estimates the market and interest rate sensitivities over three time periods: flattening of the yield curve, steepening of the yield curve, and all periods. For the period of steepening of the yield curve, and all period, the market beta is found to be greater than one.

  15. The model is also estimated using lagged values of R 2 and lagged values of long-term interest rate. Results are generally similar, though the magnitude of the beta coefficient for the market becomes larger and the directions of the volatility spillover from REITs to banks and S&Ls become positive and significant, though they are small in magnitude. The lagged specification is consistent with the work of Campbell and Hamao (1992), Bailey and Chung (1995), Badrinath et al. (1995), Stevenson et al. (2007), and Elyasiani et al. (2007) and may be the more empirically suitable model. Along the same lines, Devaney (2001) specifies a GARCH-M model in which the mean excess return of the REIT portfolio is described as a function of a lagged interest rate, a lagged conditional volatility of interest rate, and a dummy variable for the 1986 tax law change. Similarly, Stevenson et al. (2007) model the excess returns of the UK property companies using a GARCH-M specification in which the mean excess return is defined as a function of the current market return, lagged first difference of interest rate, and two dummy variables that identify an increase and a decrease in the base interest rate.

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Acknowledgements

Earlier versions of the paper were presented at the Financial Management Association meeting of 2007 and the Mid Atlantic Regional Conference at Villanova University in 2007. The authors would like to thank Mingming Zhou and Shawn Howton, the respective discussants for comments and suggestions. Thanks are due also to Paul Asabere, Yan Hu, and Arun Upadhyay for comments and suggestions and to Yan Hu for excellent research assistance. Any remaining errors are ours.

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Authors and Affiliations

  1. Fox School of Business and Management, Temple University, Philadelphia, PA, USA

    Elyas Elyasiani

  2. Widener University, Chester, PA, USA

    Iqbal Mansur

  3. University Center, Saginaw Valley State University, Saginaw, MI, USA

    Jill L. Wetmore

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  1. Elyas Elyasiani
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Elyasiani, E., Mansur, I. & Wetmore, J.L. Real-Estate Risk Effects on Financial Institutions’ Stock Return Distribution: a Bivariate GARCH Analysis. J Real Estate Finan Econ 40, 89–107 (2010). https://doi.org/10.1007/s11146-008-9125-3

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