On the global integration of REITs market returns: A multiresolution analysis

Ijasan, Kola; Tweneboah, George; Omane-Adjepong, Maurice; Owusu Junior, Peterson Article On the global integration of REITs market returns: A multiresolution analysis Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Ijasan, Kola; Tweneboah, George; Omane-Adjepong, Maurice; Owusu Junior, Peterson (2019) : On the global integration of REITs market returns: A multiresolution analysis, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 7, Iss. 1, pp. 1-18, https://doi.org/10.1080/23322039.2019.1690211 This Version is available at: https://hdl.handle.net/10419/270696 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 Cogent Economics & Finance ISSN: (Print) 2332-2039 (Online) Journal homepage: https://www.tandfonline.com/loi/oaef20 On the global integration of REITs market returns: A multiresolution analysis Kola Ijasan, George Tweneboah, Maurice Omane-Adjepong & Peterson Owusu Junior | To cite this article: Kola Ijasan, George Tweneboah, Maurice Omane-Adjepong & Peterson Owusu Junior | (2019) On the global integration of REITs market returns: A multiresolution analysis, Cogent Economics & Finance, 7:1, 1690211, DOI: 10.1080/23322039.2019.1690211 To link to this article: https://doi.org/10.1080/23322039.2019.1690211 © 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 21 Nov 2019. Submit your article to this journal Article views: 1724 View related articles View Crossmark data Citing articles: 7 View citing articles Page 1 of 18 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE On the global integration of REITs market returns: A multiresolution analysis Kola Ijasan, George Tweneboah, Maurice Omane-Adjepong and Peterson Owusu Junior Cogent Economics & Finance (2019), 7: 1690211 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE On the global integration of REITs market returns: A multiresolution analysis Kola Ijasan 1 *, George Tweneboah 2 , Maurice Omane-Adjepong 2 and Peterson Owusu Junior 2 Abstract: This paper explores dynamic correlation and interdependence of five global REIT markets using multivariate wavelet methods. United States, Hong Kong, Belgium, South Africa and Australia’s daily REITs returns are used as proxies for North America, Asia, Europe, Africa and the Oceania continents, respectively. Highlights from our results indicate the following: First, almost a perfect market integration at long-run periods for the REIT markets is observed. However, such strong market correlations dissipate significantly to moderate and weaker linkages at the medium, and short-run periods, thereby showing possible diversification opportunities at the latter scales. The finding also signifies that global REITs linkages increases with investment horizons. Second, an interdependent global market is found, where well-developed REITs tend to lead/lag relatively smaller or less developed market (especially Africa’s REIT) across scales. Third, there are prospects of gaining higher expected daily returns per unit of risk from Europe and Asia’s REITs than all other markets. Though not totally integrated, there exist conspicuous evidence against a segmented global REIT market. Our findings are useful for improving portfolio selection and minimising trading risk. ABOUT THE AUTHORS Kola Ijasan is a research scholar with special interests in Real Estate Corporate Finance and Valuation. He is a South Africa National Research Foundation Rated researcher with more than 15 years experience in real estate market analysis, investment and housing studies. G. Tweneboah is a Senior lecturer at the University of the Witwatersrand Business school with interests in His research has focused on time series modelling, exchange rate management, economic integration, financial market development, fiscal sustainability. M. Omane-Adjepong is a PhD candidate at Wits Business School. His interests are in financial modelling, statistical analysis and risk analysis. P. Owusu Junior is a PhD candidate at Wits Business School (Wits University). His interests are in financial econometrics with respect to emerging markets risk analysis. PUBLIC INTEREST STATEMENT There are many ways of investing in real estate, REITs are one of such ways. Investors and fund managers alike are continuously seeking ways of maximizing returns while minimizing risk. One of the ways of achieving this is through portfolio diversification. Research is needed to know where to invest (which market to invest). Investors would not want to invest in integrated markets because in such cases, anything that affect market 1 would also affect market 2. What this research does is to compare five global REITs to see if they are integrated. This research uses an advanced multiresolution analysis statistical method called wavelets to compare returns among the five global REITs. This paper offers greater diversification possibility for international investors in the short-tomedium runs; and an abundant diminished investment advantage in the long-run. Portfolio risk of international investors may also be minimised by incorporating the trading scale dynamics when designing global portfolios using the REIT markets. Ijasan et al., Cogent Economics & Finance (2019), 7: 1690211 https://doi.org/10.1080/23322039.2019.1690211 Page 2 of 18 Received: 04 October 2018 Accepted: 02 November 2019 First Published: 07 November 2019 *Corresponding author: Kola Ijasan, School of Construction of Economics & Management, University of the Witwatersrand, South Africa E-mail: [email protected] Reviewing editor: Walid Mensi, Finance and Accounting Department, University of tunisia el Manar, Tunis, Tunisia Additional information is available at the end of the article © 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Subjects: Multivariate Statistics; Statistics & Computing; Statistics for Business, Finance & Economics; Economics; Finance Keywords: Global REITs; multiresolution analysis; MODWT; scale; diversification JEL classification: C22; F21; G1; G32 1. Introduction A huge conundrum that continue to hang on the necks of funds managers and investment practitioners across the world borders on how integrated low risk and high returns have been. In order to achieve a harmonious integration, portfolio managers often track the spread of returns as frequently as possible. Investors are subsequently advised on which assets to buy based on this spread of returns. Even though volatility is measurable, actual prices of stocks are difficult to predict definitely. In an attempt to mitigate this situation, portfolio managers usually diversify their investments. On the international front, ideally, managers seek investments in markets that are well insulated from each other so that, in particular, the effects of a collapse in one market are not transmitted to investment holdings in other markets, i.e. ensuring that the markets do no comove. This has made the concept of market integration or segmentation one of the prime importance (Wilson & Okunev, 1996). The property of Real Estate Investment Trusts (REITs) as a hybrid of a real (properties) and a financial (shares) asset (Liow, Zhou, & Ye, 2015; Tirtiroglu, Nguyen, Tirtiroglu, & Wee, 2017) engender a close relations with other financial markets. However, other studies have propounded theoretical channels through which REITs could integrate with traditional financial markets. They include the “wealth effect”(Case & Shiller, 2003; Kapopoulos & Siokis, 2005; Quan & Titman, 1999), “credit-price effect”(Chen, 2001; Hui & Ng, 2012; Sim & Chang, 2006), “capital switching effect” (Lizieri & Satchell, 1997; Oikarinen, 2006), “financial contagion theory”(Tsai, 2015), among others. An argument beyond financial modelling is often the problem of contagion and shock transmission (Chiang, Jeon, & Li, 2007). According to Forbes and Rigobon (2002), contagion is defined as “significant increases in cross-market co-movement”; it is a significant increase in correlation between asset returns in different markets. Babecký et al. (2013) also argue that market integration enhances investment opportunities and allows higher returns at the same level of risk. This is the mainstay of risk management techniques (such as diversification) seek to attain. Investors want to allocate portfolios with different levels of risk and with minimal possibilities of shock and volatility transmission. Globally, REITs markets have been gaining remarkable attention. Global market capitalisation now stands at approximately US$1.7trillion, up from US$734billion in 2010 (Ernst and Young, 2017). Investors are beginning to consider the numerous benefits REITs present and the diversification potentials associated with it. The REIT markets have become an attractive trading avenue to add real estate exposure to a portfolio because of their historically high returns and high dividend payouts, but a review of extant literature by Stelk, Zhou, and Anderson (2017) document evidence that the link between REIT returns and physical real estate returns is mixed. A school of thought supports the notion that “risk reduction”is possible with portfolio holdings of REITs with stocks (see, Boudry, deRoos, & Ukhov, 2016; Chen, Ho, Lu, & Wu, 2005; Grissom, Kuhle, & Walther, 1987; Kuhle, 1987; Lee & Stevenson, 2005). On the flip side of the argument, some studies such as Wang, Erickson, and Chan (1995) and Glascock, Michayluk, and Neuhauser (2004) question the diversification potentials of REITs. They argue that REITs are highly correlated to stocks and bonds and hence diversification is not optimal. Physical real estate is unarguably a good investment; however, institutional or international investors have expressed various challenges that technically prevent them from investing directly in real estate, hence another benefit of REITs. There have been different methods and techniques adopted in the literature to explore whether or not diversifying into international REITs improves the efficiency of portfolio. Ijasan et al., Cogent Economics & Finance (2019), 7: 1690211 https://doi.org/10.1080/23322039.2019.1690211 Page 3 of 18 However, these studies provide limited insights, particularly because they do not account for time-scale dynamics in investor/investment behaviour. It is inherently possible that direction and magnitude of the links between REITs markets across the globe vary over the short-, medium-, and the long-terms (frequencies) because financial markets have many time-scales (Liow, Huang, & Song, 2019; Loh, 2013). In this light, this research discovers the dynamic correlations and interdependences of global REIT markets. With the help of multivariate wavelet methods, we examine the interrelationships among five global REITs markets. The multivariate wavelets approach is deemed appropriate as the wavelet transforms offer a particularly appealing data transformation, as a preliminary to data analysis. The research analyses daily REITs returns for United States, Hong Kong, Belgium, South Africa and Australia’s, as proxies for North America, Asia, Europe, Africa and the Oceania continents, respectively. Daily closing price data from 8 July 2013 to 3 November 2017 was used to generate daily returns series for the five REITs markets. The results are classified by regions. Our findings showed that global REITs provide greater diversification opportunity for international investors in the short-to-medium runs; and a bounteous diminished investment advantage in the long-run. The paper offers four key contributions. Aside adding to the literature on dynamic correlation, shock transmission, and contagion effect of international REITs markets, this research firstly answers the fundamental question of whether or not the inclusion of global REITs offers significant diversification benefit to investors. Secondly, using the Maximal Overlap Discrete Wavelet Transform (MODWT) in a wavelet multiple correlation (and cross-correlation) for the REITs market analysis overrides the limitations of the conventional coherence and the wavelet-based pairwise methods. Thirdly, from a return-to-variability ratio analysis, we answer the question of which regional REIT market(s) gives higher prospects of yielding greater daily returns with correspondingly lower riskbearing? And finally, we review to unveil any form of dynamic correlation across different timescales between international REIT markets, by situating a fast-embryonic Africa’s REIT, coupled with well-developed and/or scrutinised REITS in the international cross market analysis. We summarise results from the study as follows: (1) Evidence of almost a perfect market integration at long-run periods for the REIT markets is revealed. However, such strong market coherences dissipate significantly to moderate and weaker linkages at the medium-, and short-run periods. This signifies that REIT markets linkages increases with investment scales or horizons. As such, when designing international portfolios, investors’profile with respect to trading horizons must be duly incorporated. (2) The global market is characterised by interdependency, where the European REIT has greater likelihood to become a market leader or follower at short-, and long-run horizons; whereas the REIT markets of Asia and North America take a turn as leaders or followers in the medium-runs. This is suggestive of global linkages where developed REITs tend to lead/lag relatively smaller or less developed market, especially Africa’s REIT. (3) There exist prospects of gaining higher expected daily returns with correspondingly lower risk-bearing from Europe and Asia’s REIT markets. However, highly volatile and thus riskier markets (Africa and Oceania, respectively) are less compensated through expected returns on such assets. These findings proffer several practical implications for international investors. The rest of the study is structured as follows: section 2covers a brief review of the extant body of literature on REITs and co-movement of equities. In section 3, we present a description of the multiple wavelets approach and the stochastic properties of the data. Section 4captures the wavelet coherence analysis of the markets. The last section details the conclusion and policy implications of the findings. 2. Literature review There are many forms of REITs. Common amongst them include residential, equity, mortgage and private REITs. REITs are true total return investments. They provide high dividend yields along with moderate long-term capital appreciation. A common reference point in portfolio risk diversification using REITs is Kuhle (1987). The author concluded that the risk reduction capabilities of REITs were Ijasan et al., Cogent Economics & Finance (2019), 7: 1690211 https://doi.org/10.1080/23322039.2019.1690211 Page 4 of 18 lower than those of common stocks. This research has spurned many other similar researches. Reason for this is due to the fact that there is a common notion on the all-good nature of REITs, and so it is difficult to accept the fact that REITs might not be as safe as they have been sold to investors. There have been many studies on the correlation of real estate assets as part of a diversified investment portfolio. Many of these studies have used different methodologies for their analysis, and different proxies for what they referred to as real estate. Hartzell, Hekman, and Miles (1986) adopted capital appreciation return to evaluate the diversification benefits of US residential property investment in a portfolio context for 1973 to 1983. They concluded that aside inflation hedging, the inclusion of real estate assets in a portfolio offered “attractive diversification opportunities for stock and bond portfolios”. In a research by Liu, Hartzell, and Hoesli (1997) and Zhou and Clements (2010), both disproved the findings of Hartzell et al. (1986), and found evidence that property securities (Property Trusts, Real Estate Mutual Funds, Shares of Real Estate Operating/ Development Companies) provide a bad hedge against inflation. That means, the general assumption of inflation hedging characteristics of physical real estate cannot be generally assumed for securitised real estate. Grissom et al. (1987) found non-systematic risk diversification effects of between 83% and 90%, respectively, when the portfolio size is increased to include 10 real estate objects. This figure could even be as many as 400–500, in order to reduce the risk of a property portfolio down to the market level (Byrne & Lee, 2001). More recently, Reddy and Wong (2016) using intertemporal capital asset pricing model (ICAPM), reported that both the diversified and retail sector exhibited strong exposure to market risk, and short-, and long-term interest rates within the Australia REITs (A-REITs). With such assertions on the need for the inclusion of many properties, many investors are looking to global REITs as a medium for both risk reduction and portfolio risk elimination. The International Monetary Fund reports that more than 70% of global growth of REITs comes from emerging markets, while 65% of REITs tracked by the NAREIT comes from outside the USA. The Australian REITs (A-REITs) has seen mixed returns over the years. According to Newell and Peng (2009), even though the global financial crisis hits A-REITs very hard, the crisis has had a major impact on A-REITs, both in terms of stock market volatility and concerns over the structure of A-REITs. The authors further reported that the A-REITs market capitalisation reduced from A$132 billion to A$52 billion in December 2008. However, the market recovered in the 2009, and now constitutes around A $125 billion (10%) of the total Australian share market value (Reddy & Wong, 2016). Elsewhere, Akinsomi, Balcilar, Demirer, and Gupta (2017) explored the effect of gold market speculation on South African REITs. Historically, South Africa’s listed properties asset class has outperformed cash, equities and bonds for more than 10 years (SA REITs Association, 2015), however, using the Markov switching time-varying parameter (MS-TVP), Akinsomi et al. (2017) found that there is evidence indicating that the South African market entered into a long-run herding pattern, starting with the global financial crisis period in mid-2008; and that the herding pattern lasted until mid-2013. They also found that higher level of speculation in gold significantly contributes to herding in the South African REITs. Niskanen and Falkenbach (2010) examined the sensitivity of European REIT returns to returns in other asset classes (including equities, bonds, and commodities). Their results suggest that there is a“significant positive correlation”between European REITs and equities; although they found REIT correlation to fixed income securities to be negative. To reduce exposure due to instabilities within countries, international diversification of REITs investment is proposed. However, the question asked by this research is “are there any forms of interdependence or dynamic correlation between international markets”? Gordon and Canter (1999), in a study covering data from 1984 to 1997, concluded that the correlation coefficients of REITs and property stocks on a country-by-country basis for 14 countries are not stable over time. Chang and Chen (2014) noted that global financial crises can impose Ijasan et al., Cogent Economics & Finance (2019), 7: 1690211 https://doi.org/10.1080/23322039.2019.1690211 Page 5 of 18 contagion on markets, however, the results of this remain inconclusive. For example, Bond, Dungey, and Fry (2006) studied contagion in the REITs markets and property stock markets of Singapore, Hong Kong, Japan, Australia, and the United States. Using the 1997 financial crisis in East Asia as a reference point, they found evidence of contagion. This made them conclude that international diversification in global property investment provides only limited benefits. However, Hatemi-J and Roca (2011) investigated the US, UK, Japan, and Australia REIT markets during the 2008 subprime crisis, and find no evidence for contagion. In the Central and Eastern European CEE REITs market, Gilmore and McManus (2002) examined the short-, and long-term relationships between the US stock market and three CEE emerging markets (Hungary, Poland, and Czech Republic) with weekly data covering July 1995 to August 2001. They found that low short-term correlations between the CEE markets and the US existed. This is consistent with Gelos and Sahay (2001), Schotman and Zalewska (2006), and Wang and Moore (2008). Employing co-integration methods, Yunus and Swanson (2007) explored long-run and short-run causal relationships among four public property markets in the Asia-Pacific, and with similar market from the US, over the period from January 2000 to March 2006. They reported of a greater portfolio diversification benefits over the Hong Kong and Japanese markets for US investors at the long-run period. Their findings also pointed to a wide range of diversification opportunities at the short-run, as no significant linkages were identified for the US and the Asia-Pacific property markets. On the study of international REITs co-movement, Zhou (2010) considered six REITs (US, UK, Japan, Australia, Hong Kong, and Singapore), and later analysed each REIT with its country’s stock market. Asserting to be the maiden paper to have used bivariate wavelet method in REIT market analysis, the author found co-movement of international REIT returns to be frequency-dependent. His findings again revealed that larger REITs tend to become market leaders over smaller markets with varying degree across frequencies. Relatively current, Begiazi et al. (2016) used BEKK-GARCH and DCC-GARCH models to investigate return and volatility nexuses of two US REIT sub-sectors, and their linkages with Europe, Americas, and Asia-Pacific REIT markets. Their study concluded by surmising that investors should duly account for time-varying correlations and volatility across several world market jurisdictions in a situation to achieving optimal portfolio allocation. Using seven international REIT markets—France, Singapore, Japan, UK, Hong Kong, Australia, and US in a multiscale analysis, Zhou (2012) found strong evidence of scale-varying market linkages for the daily REIT returns, which covered periods from 1 January 1990 to 31 December 2009. The author concluded in favour of greater diversification benefits for investors in the short time horizons. However, his work on international REITs excluded the African continent which has produced one the world’s fastest-growing REIT market—South Africa’s REIT. Although Zhou (2012) claimed to be the first paper to have applied Maximal Overlap Discrete Wavelet Transform in REIT market analysis, yet the use of pairwise wavelet-based correlation method flooded the author’s work with 21 market-paired correlations. Similar pairwise wavelet multiple correlation approach was also used by Liow and Zhou (2016), as they studied cross market correlation dynamics among three public REITs (Hong Kong, Mainland China, and Taiwan in Greater China), and their respective linkages with US, UK and Japanese public REITs, using data from 1995 to 2015. The authors found scale-dependent market return and volatility linkages which give higher diversification opportunity at the short-run horizon. Chang and Chen (2014) noted that previous studies which have dealt with dynamic correlations in the presence of contagion using simple correlation coefficient analysis are fraught with limitations relating to volatility clustering, weights to return data points in estimations, and more importantly, an underlying Gaussian distribution assumption of asset returns and linear dependences. To mitigate against these apparent drawbacks, this paper applies the MODWT with Daubechies asymmetric wavelet filter in a multiresolution analysis (MRA) and includes an emerging Africa’s REIT in the international market analysis. With single diagrammatic representations, the wavelet Ijasan et al., Cogent Economics & Finance (2019), 7: 1690211 https://doi.org/10.1080/23322039.2019.1690211 Page 6 of 18 multiple correlation and cross-correlation methods appear to be robust and complement each other in finding dynamic international REITs market behaviour across time and frequency. These methods are explained in the next section. 3. Empirical methodology and data Beside the earlier mentioned methodological drawbacks, several empirical studies on (cross) market integration of REITs have widely resulted to the use of popularly known conventional methods such as correlation coefficient and co-integration analyses (Gordon & Canter, 1999; Gilmore & McManus, 2002; Yunus & Swanson, 2007; Niskanen & Falkenbach, 2010; etc.), GARCHtype models (Begiazi, Asteriou, & Pilbeam, 2016; Kyriaki et al., 2016; etc.) and intertemporal capital asset pricing model (Reddy & Wong, 2016). However, most of these methods do not possess timevarying and/or time-frequency properties to completely analyse market integration. For instance, the latter models are somehow designed to capture relationships in mainly two investment periodicities, short and long-terms, yet the behaviour of REIT markets look heterogenous across time-scales. This becomes more apparent as market traders seek to divulge varying trading strategies across different investment horizons to make higher returns with correspondingly lower risk. Hence, the use of wavelet multiple correlation and cross-correlation with an MODWT across seven time-scales is in the right perspective to capture the heterogeneous behaviour of these markets. 3.1. Wavelet multiple correlation and cross-correlation framework Following the work of Fernández-Macho (2012), which builds on the wavelet-based pairwise correlations, we introduce the multivariate case. Let the random variable Xt¼x1t;x2t; :::; xnt fg denote a multivariate data generating process (DGP), and let its correspond scale, λjbe given as Wjt ¼w1jt;w2jt; :::; wnjt  . Then, by the application of the Maximal Overlap Discrete Wavelet Transform (MODWT) to each xit, wavelet coefficients are derived from the process. The MODWT transforms realisations of a given market series into wavelet coefficients associated with variations over a collection of scales, and provides for a multiresolution analysis. Unlike other filtering methods, the MODWT does its transformations without making strong assumptions about the data generating process (DGP), thereby avoiding possible information loss. 1 The wavelet multiple correlation (WMC) denoted by the symbol ϕXðλjÞ, and explained by Fernández-Macho (2012) as a single collection of multiscale correlation, could be obtained from the DGP Xtthrough the subsequent procedure. Given each wavelet scale λj, the square root of the regression coefficient of determination is computed in a linear combination of wijt;i¼1; :::; n,by which a maximum value for such coefficient of determination is achieved. The coefficient of determination which relates to the regression having a dependent variable zi, over a defined set of predictors fzk;kÞig, is expressed as R2 i¼11=ρii, for which ρii represent i-th diagonal component of the inverse of correlation matrix P. Hence, we compute WMC as: φXðλjÞ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 11 max diag P1 j ; s(1) From (1), Pjdenote correlation matrix of Wjt ; whereas the optimal component in the diagonal of the argument is chosen with the max diag ( . ). By deducing from the regression of the dependent variable ziover the regressors in the system of equations, the derived R2 imeasure could be of the same value as the square correlation for the observed realisations of ziand it’s fitted realisations, z ^ j. Therefore, we may again express WMC as: φXðλjÞ¼Corr wijt;wijt ^  ¼ Cov wijt;wijt ^  ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi VarðwijtÞVarðwijt ^Þ q;(2) Ijasan et al., Cogent Economics & Finance (2019), 7: 1690211 https://doi.org/10.1080/23322039.2019.1690211 Page 7 of 18 integration is quite weak between the REITs markets especially in the short-term (of higher frequencies below wavelet scale 16). We only see cross-correlations beyond 0.74 from the medium-term (after wavelet scale 16). These suggest that information flow between the markets may be slow and hence shocks to one market are transmitted to and felts in other markets only after a month. This makes sense since our study period is off any specific crisis period where market tend to be otherwise highly correlated. Specifically, at the lowest scale (intraweek to fortnightly periods), there is conspicuous evidence of few significant pockets of weaker correlations, with Europe having the potential to lead or lag the remaining REIT markets. Correlation steadily increases from around 0.21 to somewhere around 0.61 at the intermediate scales (monthly to quarterly periods). The Asian and North American markets showed greater prospects of leading or lagging the global markets for these periods. Moreover, at the lower-to-medium scales, correlation turns to be statistically not different from zero for day 15 of trading, thereby giving greater opportunity for portfolio diversification. Similar evidence of non-significant coherencies dominates zones across the trading days, and are much glaring at scales below 32 (intraweek to monthly investment periods). Nonetheless, wavelet correlations recorded significant coefficients across all time lags for the highest scale (lowest frequency), with the European market being a potential leader or follower at this investment period, as seen also for the lower scales. This suggests that, any likely shocks that hit Europe’s REIT market may in-turn be transmitted down to REIT markets of Africa, North America, Oceania and Asia, and perhaps vice versa for these periods of investment. However, in terms of actual lead/lag, we find where Belgium (BEL) lags (at time 4) and the US leads (at time −4) all the other markets in the monthly and quarterly scale. That is to say, the two market have the maximum wavelet multiple correlations at these time. We mention again that short transmission are slow and persistent as they do not occur in the short-term (at high frequencies). The leading of the US can be attributed to the fact that the commercial property market was one of the hardest hit by the GFC and the quickest to rebound in the aftermath (Marzuki & Newell, 2017). The large size of US REITs market (EPRA, 2017,2017; Marzuki & Newell, 2017) is also a plausible explanations. This goes to reinforce why Belgium and Hong Kong only show potential of leading but never actually lead in the short-term. Nevertheless, the series potential lead and the eventual lag of Belgium is not surprising. Since the Belgian REITs debuted only two decades ago, it has seen significant growth. It now stand at the 5th largest REIT market in Europe by both market capitalisation and number of active REITs (EPRA, 2017; Marzuki & Newell, 2019). With respect to time-varying (frequency-dependent) our study corroborate (Anoruo & Murthy, 2017; Karatas, Unal, & Yilmaz, 2017; Liow & Angela, 2017; Liow, Zhou, Li, & Huang, 2019a; Liow, Zhou, Li, & Huang, 2019b; Zhou, 2012) which show that integration in REITs markets are dynamic in the frequency-domain. Ozun, Ertugrul, and Coskun (2018) also support our study in the sense of dynamic correlations the REITs market space. Our results align with Liow and Angela (2017) and Liow, Zhou, Li, & Huang (2019a) that REITs exhibit stronger links in the mediumto long-terms at lower frequencies. Finally, the importance of the US REITs markets in connection with as seen in this study is also highlighted by Liow (2008). In concluding our analysis, we provide key highlights from the results presented. With respect to the summary stochastic properties, the Asian and European REIT markets revealed themselves as prospective markets which offer greater expected daily returns per unit of risk. Moreover, the African REIT market is seen as the most volatile of all the global REIT markets, but turns to provide investors with comparatively lesser expected daily average returns with an associated higher risk-bearing phenomenon. Focusing on the coherence dynamics of the REIT markets, stretches of significantly high correlations were found to have been segmented at the highest scale (lowest frequency), and viewed Ijasan et al., Cogent Economics & Finance (2019), 7: 1690211 https://doi.org/10.1080/23322039.2019.1690211 Page 14 of 18 uniformly over all trading days. However, the evidence of strong correlations turns to disappear as we move downwards from the highest to lowest scales. Noticeable zones of non-significant correlations dominate the lower scales, at where the European market either leads or lags the remaining markets. Conspicuously moderate significant correlation zones are found within the medium scale (intraweek to monthly investment periods), with trading day 15 showing no sign of correlation. For this latter trading horizon, the North American and Asian markets have prospect of being leaders or followers of the REIT markets. In summary, our coherence analysis with lead-lag market relationships offers important diversification opportunity, as investors adopt varied trading strategies, aimed at gaining higher positive returns with minimised risk-bearing. Further, our results imply that diversification and policy measures should be frequency-dependent. This will ensure that different actions are directed time-scales where global REITs integration levels are weak vis-à-vis strong. 5. Conclusion and implications Using multivariate wavelet methods, we explore correlation and market interdependence of five global REIT markets across seven trading scales (or investment horizons). The daily REIT returns of the United States, Hong Kong, Belgium, South Africa and Australia are used to proxy for North America, Asia, Europe, Africa and the Oceania markets, respectively. Over here, we must emphasise that the inclusion of a fast-embryonic Africa’s REIT market, coupled with well-developed and/or scrutinised REITS in an investigation for global market integration and interdependence is a major contribution of this work. Another contribution could be aligned to the use of MODWT in a multiresolution analysis—thus, the adoption of wavelet multiple correlation and crosscorrelation. These methods complement each other for robustness purposes and address the setback of the overly used multivariate wavelet-based correlations, whose analysis involve several number of pairwise comparison. We therefore summarise our main findings as follows: (1) Higher expected daily market returns with correspondingly lower risk-bearing are much likely to be gained from investments in Europe and Asia’s REIT markets, whereas lesser compensations are to be rewarded for holding Africa’shighly volatile or riskier REIT. (2) Multiple correlations are found to have increase with time horizons, thus, moving from significant pockets of weaker correlations in the short-run, to moderate and stronger correlations across trading days (time lags) for the medium-, and long-runs, respectively. The evidence of significantly weaker correlations at the shorter scales, and the increasingly higher correlations at long-run scales could perhaps be attributed to ephemeral and macroeconomic factors. Whereas the latter factors cause correlations in market to increase, ephemeral variables exert influence on markets that often result in lower correlations. (3) The results also revealed that the European REIT market has a greater potential to lead or lag the global markets at the short-, and long-run investment horizons, whiles the Asian and North American markets show same prospects at intermediate periods. (4) Apparently, no significant correlation zones are found for trading day 15 of the medium to lower scales, hence, promising of an advantage to diversify and minimise portfolio risk. From the month and quarter wavelet scales we find the US and Belgium leading and lagging, respectively. These can be attributed to the largest size of the US REITs market as well as rebounding very strongly after the GFC. Belgium has also been a strong in the European REITs space by rising to 5th position by market size and active number of REITs. Generally, our results have several significant practical implications for investors and market makers. Thus, it offers greater diversification possibility for international investors in the short-, to medium-runs; and a copious diminished investment advantage in the longrun. Portfolio risk of international investors may also be minimised by incorporating the trading scale dynamics when designing global portfolios using the REIT markets. These imply that since integration levels in the REITs markets are frequency-dependent so should policy and investment decisions. On the policy side, this can forestall crisis in the REITs markets given that will ensure that different actions are directed time-scales where global REITs integration levels are weak vis-à-vis strong. Ijasan et al., Cogent Economics & Finance (2019), 7: 1690211 https://doi.org/10.1080/23322039.2019.1690211 Page 15 of 18 Acknowledgements The authors are much grateful to the South African National Research Foundation (NRF) for providing funds for the research data; and to Professors PolancoMartínez and Fernández-Macho for making their computing script on multivariate wavelet correlation easily available. We highly commend them for the teeming effort. Funding The authors received no direct funding for this research. Author details Kola Ijasan 1 E-mail: [email protected] George Tweneboah 2 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0001-7992-7340 Maurice Omane-Adjepong 2 E-mail: [email protected] Peterson Owusu Junior 2 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0001-6253-5770 1 School of Construction of Economics & Management, University of the Witwatersrand, Johannesburg, South Africa. 2 Wits Business School, University of the Witwatersrand Business School, Johannesburg, South Africa. Cover Image Source: Author. Citation information Cite this article as: On the global integration of REITs market returns: A multiresolution analysis, Kola Ijasan, George Tweneboah, Maurice Omane-Adjepong & Peterson Owusu Junior, Cogent Economics & Finance (2019), 7: 1690211. Note 1. See Section 4for brief explanation. Readers are also directed to see Percival and Walden (2000) for further details of the methodology. References Akinsomi, O., Balcilar, M., Demirer, R., & Gupta, R. (2017). The effect of gold market speculation on REIT returns in South Africa: A behavioral perspective. Journal of Economics and Finance,41(4), 774–793. doi:10.1007/ s12197-016-9381-7 Anoruo, E., & Murthy, V. N. (2017). An examination of the REIT return–Implied volatility relation: A frequency domain approach. Journal of Economics and Finance, 41(3), 581–594. doi:10.1007/s12197-016-9378-2 Babecký, J., Havránek, T., Matějů, J., Rusnák, M., Šmídková, K., & Vašíček, B. (2013). Leading indicators of crisis incidence: Evidence from developed countries. Journal of International Money and Finance,35,1–19. doi:10.1016/j.jimonfin.2013.01.001 Begiazi, K, Asteriou, D, & Pilbeam, K. (2016). A multivariate analysis of united states and global real estate investment trusts. International Economics and Economic Policy,13(3), 467–482. doi: 10.1007/ s10368-016-0349-z Begiazi, K., Asteriou, D., & Pilbeam, K. (2016). A multivariate analysis of United States and global real estate investment trusts. International Economics and Economic Policy,13, 467–482. doi:10.1007/s10368-016-0349-z Bond, S. A., Dungey, M., & Fry, R. (2006). A web of shocks: Crises across Asian real estate markets. The Journal of Real Estate Finance and Economics,32(3), 253–274. doi:10.1007/s11146-006-6800-0 Boudry, W. I., deRoos, J. A., & Ukhov, A. D. (2016). The role of REIT preferred and common stock in diversified portfolios. Cornell Hospitality Report,16(28), 3–12. Byrne, P. J., & Lee, S. L. (2001). Risk reduction and real estate portfolio size. Managerial and Decision Economics,22, 369–379. doi:10.1002/mde.1026 Case, K. E., & Shiller, R. J. (2003). Is there a bubble in the housing market? Brookings Papers on Economic Activity,2003(2), 299–362. doi:10.1353/ eca.2004.0004 Chang, G. D., & Chen, C. S. (2014). Evidence of contagion in global REITs investment. International Review of Economics and Finance,31, 148–158. doi:10.1016/j. iref.2013.12.005 Chen, H. C., Ho, K. Y., Lu, C., & Wu, C. H. (2005). Real estate investment trusts: An asset allocation perspective. Real estate special issue. Journal of Portfolio Management,31,46–54. doi:10.3905/ jpm.2005.593887 Chen, N.-K. (2001). Asset price fluctuations in Taiwan: Evidence from stock and real estate prices 1973 to 1992. Journal of Asian Economics,12(2), 215–232. doi:10.1016/S1049-0078(01)00083-5 Chiang, T. C., Jeon, B. N., & Li, H. (2007). Dynamic correlation analysis of financial contagion: Evidence from Asian markets. Journal of International Money and Finance,26(7), 1206–1228. doi:10.1016/j. jimonfin.2007.06.005 EPRA. (2017). Global REIT survey. European Public Real Estate Association website. Retrieved from http:// www.epra.com/application/files/9515/0366/6834/ Global-REIT-Survey-complete.pdf Ernst and Young. (2017). Global REIT markets. Retrieved from /ey-global-reit-markets/$FILE/ey-global-reitmarkets.pdf Fernández-Macho, J. (2012). Wavelet multiple correlation and cross-correlation: A multiscale analysis of Eurozone stock markets. Physica A,391, 1097–1104. doi:10.1016/j.physa.2011.11.002 Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: Measuring stock market comovements. Journal of Finance,57(5), 2223–2261. doi:10.1111/0022-1082.00494 Gelos, G., & Sahay, R. (2001). Financial market spillovers in transition economies. Economics of Transition,9(1), 53–86. Retrieved from https://ssrn.com/abstract= 264832 Gilmore, C. G., & McManus, G. M. (2002). International portfolio diversification: US and Central European equity markets. Emerging Markets Review,3,69–83. doi:10.1016/S1566-0141(01)00031-0 Glascock, J. L., Michayluk, D., & Neuhauser, K. (2004). The riskiness of REITs surrounding the October 1997 stock market decline. The Journal of Real Estate Finance and Economics,28(4), 339–354. doi:10.1023/ B:REAL.0000018786.39272.fa Gordon, J. N., & Canter, T. A. (1999). International real estate securities: A test of capital markets integration. The. Journal of Real Estate Portfolio Management,5(2), 161–170. Retrieved from http:// www.jstor.org/stable/24880721 Grissom, T. V., Kuhle, J. L., & Walther, C. H. (1987). Diversification works in real estate, too. The Journal of Portfolio Management,13(2), 66–71. doi:10.3905/ jpm.1987.409095 Hartzell, D., Hekman, J., & Miles, M. (1986). Diversification categories in investment real estate. Journal of American Real Estate and Urban Economics Ijasan et al., Cogent Economics & Finance (2019), 7: 1690211 https://doi.org/10.1080/23322039.2019.1690211 Page 16 of 18 Association,14(2), 230–254. doi:10.1111/15406229.00385 Hatemi-J, A., & Roca, E. (2011). How globally contagious was the recent US real estate market crisis? Evidence based on a new contagion test. Economic Modelling, 28(6), 2560–2565. doi:10.1016/j. econmod.2011.07.017 Hui, E. C. M., & Ng, I. M. H. (2012). Wealth effect, credit price effect, and the inter-relationships between Hong Kong’s property market and stock market. Property Management,30, 255–273. doi:10.1108/ 02637471211233864 Kapopoulos, P., & Siokis, F. (2005). Stock and real estate prices in Greece: Wealth versus ‘credit-price’effect. Applied Economics Letters,12(2), 125–128. doi:10.1080/1350485042000307107 Karatas, C., Unal, G., & Yilmaz, A. (2017). Co-movement and forecasting analysis of major real estate markets by wavelet coherence and multiple wavelet coherence. Chinese Journal of Urban and Environmental Studies,5(02), 1750010. doi:10.1142/ S2345748117500105 Kuhle, J. L. (1987). Portfolio diversification and return benefits-common stocks vs. Real estate investment trusts (REITs). The Journal of Real Estate Research,2 (2), 1–9. Lee, S., & Stevenson, S. (2005). The case for REITs in the mixed-asset portfolio in the short and long run. Journal of Real Estate Portfolio Management,11(1), 55–80. Liow, H. K., Huang, Y., & Song, J. (2019). Relationship between the United States housing and stock markets: Some evidence from wavelet analysis. The North American Journal of Economics and Finance, 50, 101033. doi:10.1016/j.najef.2019.101033 Liow, K., Zhou, X., Li, Q., & Huang, Y. (2019a). Dynamic interdependence between the US and the securitized real estate markets of the Asian-Pacific economies. Journal of Property Investment & Finance,37,9 2– 117. doi:10.1108/JPIF-07-2018-0048 Liow, K. H. (2008). Financial crisis and Asian real estate securities market interdependence: Some additional evidence. Journal of Property Research,25(2), 127–155. doi:10.1080/09599910802605400 Liow, K. H., & Angela, S. Y. (2017). Return and co-movement of major public real estate markets during global financial crisis: A frequency domain approach. Journal of Property Investment & Finance, 35(5), 489–508. doi:10.1108/JPIF-01-2017-0002 Liow,K.H.,&Zhou,X.(2016). Dynamics of multiscale correlations and co-movement across greater China public real estate markets and international linkages (IRES Working Paper Series No. IRES2016–007). Liow, K. H., Zhou, X., Li, Q., & Huang, Y. (2019b). Comovement between the US and the securitised real estate markets of the Asian-Pacific economies. Journal of Property Research,36(1), 27–58. doi:10.1080/09599916.2019.1568283 Liow, K. H., Zhou, X., & Ye, Q. (2015). Correlation dynamics and determinants in international securitized real estate markets. Real Estate Economics,43(3), 537–585. doi:10.1111/reec.2015.43.issue-3 Liu, C. H., Hartzell, D. J., & Hoesli, M. E. (1997). International evidence on real estate securities as an inflation hedge. Real Estate Economics,25(2), 193–221. doi:10.1111/reec.1997.25.issue-2 Lizieri, C., & Satchell, S. (1997). Interactions between property and equity markets: An investigation of linkages in the United Kingdom 1972–1992. The Journal of Real Estate Finance and Economics,15(1), 11–26. doi:10.1023/A:1007745204491 Loh, L. (2013). Co-movement of Asia-Pacific with European and US stock market returns: A cross-timefrequency analysis. Research in International Business and Finance,29,1–13. doi:10.1016/j. ribaf.2013.01.001 Marzuki, M. J., & Newell, G. (2017). The significance and performance of US commercial property in a post-GFC context. Journal of Property Investment & Finance,35, 575–588. doi:10.1108/JPIF-02-2017-0018 Marzuki, M. J., & Newell, G. (2019). The evolution of Belgium REITs. Journal of Property Investment & Finance,37, 345–362. doi:10.1108/JPIF-03-20190029 Newell, G, & Peng, H.-W. (2009). The impact of the global financial crisis on a-reits. Pacific Rim Property Research Journal,15(4), 453–470. doi: 10.1080/ 14445921.2009.11104291 Niskanen, J., & Falkenbach, H. (2010). REITs and correlations with other asset classes: A European perspective. Journal of Real Estate Portfolio Management,16(3), 227–240. Retrieved from http:// www.jstor.org/stable/24884576 Oikarinen, E. (2006). Price linkages between stock, bond and housing markets: Evidence from finnish data (Working Paper No. 1004). ETLA Discussion Papers website. Retrieved from https://www.econstor.eu/ handle/10419/63730 Ozun, A., Ertugrul, H. M., & Coskun, Y. (2018). A dynamic model for housing price spillovers with an evidence from the US and the UK markets. Journal of Capital Markets Studies,2,70–81. doi:10.1108/JCMS-012018-0002 Percival, D., & Walden, A. (2000). Wavelet methods for time series analysis. Cambridge University Press. Polanco-Martinez, J. M., & Fernández-Macho, F. J. (2014). Package W2CWM2C: Description, features, and applications. Computing in Science & Engineering,16 (6), 68–78. doi:10.1109/MCSE.2014.96 Quan, D. C., & Titman, S. (1999). Do real estate prices and stock prices move together? An international analysis. Real Estate Economics,27(2), 183–207. doi:10.1111/1540-6229.00771 Reddy, W., & Wong, W., (2016). Australian interest rate movements and A-REITs performance: A sectoral analysis. In AsRES 21st International Conference (pp. 1–11). Kualar Lumpur, Malaysia: Asian Real Estate Society. Schotman, P. C., & Zalewska, A. (2006). Non-synchronous trading and testing for market integration in Central European emerging markets. Journal of Empirical Finance,13(4–5), 462–494. doi:10.1016/j. jempfin.2006.04.002 Sim, S.-H., & Chang, B.-K. (2006). Stock and real estate markets in Korea: Wealth or credit-price effect. Journal of Economic Research-seoul-,11(1), 103. Stelk, S. J., Zhou, J., & Anderson, R. I. (2017). REITs in a mixed-asset portfolio: An investigation of extreme risks. The Journal of Alternative Investments,20(1), 81–91. doi:10.3905/jai.2017.20.1.081 Tirtiroglu, D., Nguyen, T. H., Tirtiroglu, E., & Wee, T. C. (2017). REITs, growth options and beta. The Journal of Real Estate Finance and Economics,55(3), 370–394. doi:10.1007/s11146-016-9590-z Tsai, I.-C. (2015). Dynamic information transfer in the United States housing and stock markets. The North American Journal of Economics and Finance,34, 215–230. doi:10.1016/j.najef.2015.09.012 Wang, K., Erickson, J., & Chan, S. H. (1995). Does the REIT stock market resemble the general stock market? Journal of Real Estate Research,10(4), 445–460. Ijasan et al., Cogent Economics & Finance (2019), 7: 1690211 https://doi.org/10.1080/23322039.2019.1690211 Page 17 of 18 Retrieved from http://www.jstor.org/ stable/ 24885680 Wang, P., & Moore, T. (2008). Stock market integration for the transition economies: Timevarying conditional correlation approach. The Manchester School,76(s1), 116–133. doi:10.1111/j.1467-9957.2008.01083.x Wilson, P. J., & Okunev, J. (1996). Evidence of segmentation in domestic and international property markets. Journal of Property Finance,7(4), 78–97. doi:10.1108/ 09588689610152408 Wong, W. W., & Reddy, W. (2018). Evaluation of Australian REIT performance and the impact of interest rates and leverage. International Real Estate Review,21(1), 41–70. Yunus, N., & Swanson, P. E. (2007). Modelling linkages between US and Asia-Pacific securitized property markets. Journal of Property Research,24(2), 95–122. doi:10.1080/09599910701439992 Zhang, Z., Telesford, Q. K., Giusti, C., Lim, K. O., & Bassett, D. S. (2016). Choosing wavelet methods, filters, and lengths for functional brain network construction. PLoSONE,11(6), 1–24. doi:10.1371/ journal.pone.0157243 Zhou, J. (2010). Comovement of international real estate securities returns: A wavelet analysis. Journal of Property Research,27(4), 357–373. doi:10.1080/ 09599916.2010.517853. Zhou, J. (2012). Multiscale analysis of international linkages of REIT returns and volatilities. Journal of Real Estate Finance and Economics,45(4), 1062–1087. doi:10.1007/s11146-011-9302-7 Zhou, X., & Clements, S. (2010). The inflation hedging ability of real estate in China. JournalofRealEstatePortfolio Management,16(3), 267–278. Retrieved from http:// www.jstor.org/stable/ 24884579 © 2019 The Author(s). Thisopen access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. You are free to: Share —copy and redistribute the material in any medium or format. Adapt —remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution —You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. 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