Price and volatility linkages between international REITs and oil markets☆
Introduction
There is now widespread evidence that benefits can be derived by including real estate in mixed-asset portfolios (Hoesli et al., 2004; MacKinnon and Al Zaman, 2009; Hoesli and Reka, 2013; Bouri et al., 2018). But investing in the real estate market can be problematic because of the high unit value and illiquidity associated with properties. Hence, it is not surprising that the importance of the securitized real estate market, i.e., Real Estate Investment Trusts (REITs), which are exchange-traded funds that earn most of their income from investments in real estate, has grown substantially during the past decades, with a total market capitalization of US $ 1.7 trillion (Global REITs Market, EY Global Real Estate Report, 2016). Though the United States (US) continues to remain the leader in the REITs market (with a market capitalization of US $ 1.15 trillion), the number of countries now offering REITs as an investment vehicle has almost doubled in the last 10 years, and currently stands at $ 37 trillion. The ability of the REITs sector to attract investment capital is not surprising, since it is accessible to all investors irrespective of the portfolio size. Given the well-accepted importance of REITs in investment portfolios, an important question for investors is to understand what shocks drive this market. In addition, given the well-established role played by the real estate sector in the recent global financial crisis, and with REITs data available at high-frequency without measurement errors (unlike the housing market), as well as it being a good proxy for the overall real estate sector (Akinsomi et al., 2016), the early detection of the path that the sector takes following shocks, is a question of equal importance to policymakers as well (Gupta and Marfatia, 2018; Gupta et al., 2019).
In this regard, studies have primarily analyzed the role of monetary policy and macroeconomic news shocks in affecting the REITs market (see for example, Bredin et al., 2007, Bredin et al., 2011, Xu and Yang (2011), Claus et al. (2014), Kroencke et al. (2016), Marfatia et al. (2017), Nyakabawo et al. (2018)). With REITs shares trading as common stocks, and the large literature that exists involving the analysis of the importance of oil shocks on movements in prices and/or returns and volatility of international equity markets (see for example, Degiannakis et al. (2018), and Smyth and Narayan (2018) for detailed reviews), the lack of similar studies on REITs is quite perplexing. The two papers that we could find in this regard are that of Huang and Lee (2009) and Nazlioglu et al. (2016).1 On one hand, Huang and Lee (2009) adopted the autoregressive conditional jump intensity model proposed by Chan and Maheu (2002) to capture the characteristics of the time-varying jump (i.e., sudden rather than smooth structural breaks) phenomenon, and investigated the influence of expected-and unexpected crude oil fluctuations on an overall REITs index of the US. The analytical results revealed that REITs returns rise in response to increase in expected oil price and provide a good partial hedge. Moreover, this paper also showed that oil has more impact on REITs than common stocks and the bond market. On the other hand, Nazlioglu et al. (2016) examined the role of oil price and volatility on the first and second-moments of six REITs categories of the US: Residential, Hotel, Healthcare, Retail, Mortgage and Warehouse/Industrial REITs. Econometrically, this study proposed a new causality approach by augmenting the Toda and Yamamoto (1995) method with a Fourier approximation to capture gradual or smooth shifts, which in turn does not require a prior knowledge regarding the number, dates, and form of structural breaks. Using this test, these authors find uni-directional causality running from oil prices to all REITs, except for the mortgage REITs, with the causality running in the opposite direction in the latter case. In addition, based on a causality-in-variance test of Hafner and Herwartz (2006), Nazlioglu et al. (2016) indicate bi-directional volatility transmission between the oil market and all REITs. In sum these two studies showed significant impact of oil price on the first- and second-moments of US REITs, and also indicated of possible feedbacks.
The results from the works of Huang and Lee (2009) and Nazlioglu et al. (2016) point out that in the wake of the recent financialization of the oil market (Bahloul et al., 2018), the link between oil and financial markets, with the latter also including the REITs sector, has intensified. In other words, movements in these two markets are likely to affect each other, at the levels of both price and volatility, due to portfolio allocations carried out by investors (Tiwari et al., 2018). In addition, given that the price of a share in a (real estate) company is equal to the expected present value of discounted future cash flows (Huang et al., 1996), oil price shocks can affect REITs prices directly by affecting current and future cash flows or indirectly by affecting interest rates that are used to discount the future cash flows (Kaminska and Roberts-Sklar, 2018). Moreover, both oil and real estate markets are likely to be driven by common shocks associated with output, inflation and interest rate (Breitenfellner et al., 2015), resulting in indirect linkages in the first and second moments of oil and REITs.
Against this backdrop, we aim to extend the limited literature on the causal impact of prices and volatility involving the REITs and oil markets concentrated only on the US economy, to an international dimension (involving 19 countries) in the wake of the massive growth in the REITs sector worldwide, as discussed above. In this regard, based on data availability, we analyze multiple REITs markets that are at their different stages of development, and corresponds to the mature (US), the established (Australia, Belgium, Canada, France, Germany, Hong Kong, Japan, The Netherlands, New Zealand, Singapore and the UK), and the emerging (Ireland, Italy, Malaysia, Mexico, South Africa, Spain and Turkey) categories.
To achieve our objectives, from an econometric modelling perspective, we use the Fourier-based version of the Toda and Yamamoto (1995) test of causality in prices (as developed by Nazlioglu et al. (2016)), and the modified Hafner and Herwartz (2006) test of causality-in-variance with Fourier approximations (due to Pascalau et al. (2011) and Li and Enders (2018)). Both these models account for structural shifts, incorporated as gradual processes, in the relationships involving the movements in the first- and second moments of oil and REITs markets. Accounting for regime changes is of crucial importance, realizing that (high-frequency) data related to financial and commodity markets are subject to structural changes, and more importantly, the inability to model structural breaks would result in incorrect inferences (Kim et al., 2007; Salisu and Fasanya, 2013; Gil-Alana et al., 2016).
To the best of our knowledge, this is the first attempt to analyze price and volatility spillovers between the oil and international REITs markets based on tests of Granger causality with structural shifts.2 Our paper can be considered to be an extension of the work of Huang and Lee (2009) from the perspective of going beyond the US, and looking at both first and second moment using methodological advancements to the standard tests of Granger causality for price and volatility. When compared to Nazlioglu et al. (2016), again we provide an international perspective, though unlike them we do not look at sector-specific REITs (due to lack of data across these countries) and concentrate on overall REITs. While like Nazlioglu et al. (2016) we use a first-moment test of causality accounting for smooth regime changes, we, unlike them, provide methodological innovation in accounting for structural breaks in a smooth manner when analyzing volatility spillover. In sum our analysis extends the literature in two dimension: international evidence and methodology. The remainder of the paper is organized as follows: Section 2 discusses the methodologies for testing causality in prices and volatility. Section 3 presents the data and its properties, as well as the results from the tests of causality. Finally, Section 4 concludes and draws implications of our results.
Section snippets
Testing for price transmission with structural changes
In order to test for price transmission, we start with the basic “causality” model developed by Granger (1969). Granger define VAR(p) model aswhere yt includes endogenous variables, γ is a vector of intercept terms, Π = (Π1, …, Πp)′ are parameters and ut are white-noise residuals. In our setting, yt involves oil prices and international REITs. The null hypothesis of no Granger causality (Ho : Π1 = … = Πp = 0) can be tested by the Wald statistic which has the chi-square
Data
Our analysis utilizes daily observations of REITs indices of nineteen countries (Australia, Belgium, Canada, France, Germany, Hong Kong, Ireland, Italy, Japan, Malaysia, Mexico, The Netherlands, New Zealand, Singapore, South Africa, Spain, Turkey, the UK, and the US), and the oil price. The REITs data is sourced from the DataStream database of Thomson Reuters, with the real estate data corresponding to the S&P REITs indices for each country. As for the oil prices, we use the daily price of
Conclusions
The rapid growth of REITs in recent years has made it an important portfolio option. Furthermore, the role of the real estate sector in driving the recent financial crisis is also well-accepted. Just like any other investment vehicle, as the REITs market grows in size and impact, it becomes important for investors and policy makers to understand the outside drivers that impact the dynamics of that asset group. As commodity markets become more financialized (Henderson et al., 2014; Adams and
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2022, Finance Research LettersCitation Excerpt :Evidence of bidirectional spillovers is identified between REITs and both oil and gold markets. Results for oil are in line with the findings of Nazlioglu et al. (2020), who report that oil prices are predictive of REIT prices. Given that gold and oil are net contributors of spillovers to REIT markets in a low-volatility regime, REITs are net receivers of spillovers from commodity markets.
Quantile connectedness and spillovers analysis between oil and international REIT markets
2022, Finance Research LettersCitation Excerpt :The REIT market of Australia appears to be the most interconnected with other markets in both lower and upper quantiles, followed by that of France and the US, whereas the contribution from oil to REIT markets is more pronounced in the left tail than in the right tail. This suggests that oil prices have predictive power for REIT markets, which corroborates Nazlioglu et al. (2020) findings. Moreover, in the lower quantile, the oil market and the REIT markets of Japan, UK, US, Italy, Canada, Netherland, and Belgium are net transmitters of return spillovers.
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2021, Energy EconomicsCitation Excerpt :At this stage, it is important to highlight that we study the role of oil shocks for predicting REITs variance and volatility rather than at the predictive value of movements of the oil price per se because of the possible differential impact of oil shocks as highlighted by Kilian’s (2009) line of reasoning that “Not All Oil Shocks are Alike”. In addition, using oil shocks instead of oil-price movements avoids the issue of endogeneity associated with the predictors, given the evidence of bidirectional causality between oil prices and REITs markets (Nazlioglu et al., 2016, 2020)). Note that, in general, there is a lack of studies involving REITs and oil markets.
The impact of disaggregated oil shocks on state-level real housing returns of the United States: The role of oil dependence
2021, Finance Research LettersCitation Excerpt :Recent studies have highlighted the significant role of oil price and/or oil shocks on the movements of the house and real estate price and/or returns of the United States (US) within a single- or multi-country set-up that includes the US (Chan et al., 2011; Breitenfellner et al., 2015; Antonakakis et al., 2016; Nazlioglu et al., 2016, 2020; Agnello et al., 2017; Killins et al., 2017; Aye et al., 2019; Sheng et al., 2021).
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The first author gratefully acknowledges that his work is a part of the project supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under grant number 215K086. In addition, we would like to thank two anonymous referees for many helpful comments. However, any remaining errors are solely ours.