This study is among the first to examine the price, volatility and covariance dynamics between securitized real estate spot and futures markets. It provides a distinctive and yet complementary perspective on the predictability of real estate spot return and spot volatility based on the information from the spot market alone. The results show that for the EPRA/NAREIT Europe index, the spot market tends to lead its futures market in the long run during the sample period, which can be attributed to a rather illiquid real estate futures market in sharp contrast with a voluminous spot market. Furthermore, we find the V-shaped asymmetric effect of the basis on the futures market volatility, which represents the primary channel of strong volatility transmission between securitized real estate spot and futures markets during the whole sample and the post-crisis period. This sheds light on the hedging effectiveness for the REIT index.
The important role of futures market in the price discovery process has received considerable attention in the literature. In a static sense, the price discovery implies the existence of equilibrium prices, while in a dynamic sense, the price discovery process describes how price information is produced and transmitted across related markets. The notion of cointegration and related error correction models has been widely used to incorporate the nonstationarity of asset prices while investigating the price discovery in futures markets (e.g., Brenner and Kroner, 1995, Kavussano et al., 2008, Uhrig-Homburg and Wagner, 2009 and Yang et al., 2001). In addition to such price information transmission, another important issue is the volatility transmission between futures markets and underlying spot markets. Ross (1989) points out that price volatility is directly related to the rate of infor-mation flow. Hence, proponents of futures trading might expect more pronounced volatility spillover from futures to spot markets as evidence for more informationally efficient futures markets. By contrast, opponents of futures trading could interpret this spillover as evidence that speculative futures trading unduly influences underlying spot prices and causes excessive spot market volatility (e.g., Zhong et al., 2004).
This study explores the price discovery process and information transmission channels in the real estate market through a comprehensive analysis of the price, volatility and covariance dynamics between securitized real estate spot and futures markets. The study contributes to the literature in the following aspects. First, to the best of our knowledge, this study is among the first to examine the dynamic relationships between securitized real estate spot and futures markets. Although real estate is a major capital asset in the world, with the size of the capitalization larger than that of either the common stock or bond markets, real estate futures have a relatively short history.2 For example, S&P/Case-Shiller home price index futures contracts were introduced in May 2006 and IPD UK property index futures were introduced in February 2009. These futures are all related to housing price indices and only monthly spot market data are available. In addition, these futures exhibit little daily price variations, implying their low liquidity. In this study, we focus on a major securitized real estate futures based on the EPRA/NAREIT Europe Index (“EPRA” hereafter). Compared to the housing price index futures, the securitized real estate index futures market is relatively more liquid and volatile, and importantly, daily underlying spot market data are readily available.3 6Hence, this allows us to directly examine price discovery and volatility transmission between real estate spot and futures market, which remains largely unexplored in the literature. A related work is done by Wong et al. (2007), which focuses on the return and volatility transmission between the over-the-counter real estate forward (pre-sale) market and the spot market in Hong Kong. Finally, an examination of price and volatility dynamics between real estate spot and futures markets provides a distinctive and yet complementary perspective on the predictability of real estate spot return and spot volatility based on the information from the spot market alone.
Second, we apply a relatively new bivariate asymmetric ECM-GARCH-BEKK model (Yang et al., 2012) to simultaneously incorporate both the volatility spillover between real estate spot and futures markets and the asymmetric basis effects on returns, volatilities and covariances. As discussed in Yang et al. (2012), the model is motivated by the theoretical argument of Kogan et al. (2009) that the asymmetric basis effect on futures volatility is positive (negative) when the basis is positive (negative). It also extends the GARCH-X model specification which allows for investigating the asymmetric basis effect (e.g., Kavussano et al., 2008). The model sheds light on the hedging effectiveness for the REIT index, as discussed in Liang et al. (1998). Noteworthy, we find that such asymmetric basis effect represents the only channel of volatility transmission between securitized real estate spot and futures markets during both the whole sample and the post-crisis period, suggesting the potential model misspecification in previous studies.
Third, we utilize a recursive cointegration technique developed by Hansen and Johansen (1999) to examine the time-varying price discovery performance, which is particularly revealing for nascent real estate futures markets. There are at least two empirical issues in modeling the price discovery process of the futures market in the literature. The first issue is the ambiguity about the length of time an emerging futures market would take to function well in its price discovery process, where Uhrig-Homburg and Wagner (2009) show that it could span from a few months to several years.4 The second issue is existence of a potential structural break in the sample period due to the recent 2007–2008 global financial crisis. The recursive cointegration analysis allows us to address two issues directly.
The rest of this paper is organized as follows. Section 2 describes the data and Section 3 discusses the empirical methodology. Section 4 presents empirical findings, and finally, Section 5 concludes.
