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The purpose of this paper is twofold in examining the international transmission of REIT returns volatility. The first purpose is to add to the literature on whether the real estate securities market and the broader equity market are integrated. The second objective of the study is to determine whether geographic risk factors can be transmitted beyond their region of influence.

The study uses the GARCH(1, 1), EGARCH, and GARCH‐M models.

The results show that there are significant international spillovers of REIT returns volatility within the Pacific region. The results also show that there are significant volatility transmissions between the Pacific and the Atlantic regions.

The results are consistent with the implication that the real estate sector and the general equity market are integrated such that geographic risk can be transmitted across national borders. The result will have major implications for international investment strategies.

To date, there has been no published study on the international transmission of REIT returns volatility. This study therefore examines whether the conditional variance of REIT returns of a country is affected by volatility transmission across markets in the same region using four Pacific markets.

This study examined the dynamic role of the Japanese property sector, particularly the real estate investment trusts (REITs), in mixed-asset portfolios of stocks and bonds, as well as office, retail, hotel and residential REITs.

Daily data were retrieved from 01 January 2008 to 31 December 2019. The sample time frame consisted of in-sample and out-of-sample periods. The dynamic conditional correlation-generalised autoregressive conditional heteroskedastic (DCC-GJRGARCH) model was deployed to obtain the forecast estimates of time-varying volatility of REITs and correlations with other assets. The estimates were employed to construct out-of-sample portfolios based on the three assets for daily investment. The five sets of portfolios with each individual property sector REITs, as well as a portfolio of stocks and bonds that served as a benchmark, were produced. The average utility for each set of portfolios was estimated and compared with the average utility of the benchmark portfolio. The average transaction cost (TC) for portfolio rebalancing was calculated as well.

The forecast of volatility estimates for each property sector revealed that each asset displayed a similar pattern with the differences in the volatility magnitude. Notably, hotel and retail REITs were more volatile than other property sector REITs. The property sector REITs exhibited a positive correlation with stocks but negatively linked with bonds. The results unveiled the diversification benefits of incorporating property sector REITs. The portfolio with property sector REITs had higher risk-adjusted returns and utility, compared to portfolio consisting of stocks and bonds. The benefits outweighed the TC for portfolio rebalancing.

This study highlights the importance of quantifying the conditional time-varying volatility and correlations of the property sector REITs with other asset returns, especially for investment decision, to select and include property sector REITs in mixed-asset portfolios. For fund managers seeking liquid assets in daily investment, this analysis suggests the inclusion of hotel and retail REITs to enhance REITs' portfolio performance.

This study is the first to investigate the dynamic characteristics of the volatility and correlation of each property sector REITs with other financial assets by employing the conditional framework that accounted for short- and long-run persistency in economic shocks. The reported outcomes shed light on the differences in the underlying properties that contribute to the variances in dynamic volatility of each sector REITs, as well as REITs' correlations with stocks and bonds. This application enables the authors to transmit the dynamics of variance-covariance matrix amongst each property sector REITs, stocks and bonds into asset allocation problem on a daily basis.

This study aims to examine the performance and volatility of Turkey Real Estate Investment Trusts (Turkish REITs) as the world is adjusting to the new normal situation in every aspect of REITs' business activities.

The prices of REITs were acquired from 26 Turkish REITs in this study, but owing to autocorrelation difficulties, 14 Turkish REITs were employed in the analysis. The ten-year long-term bond of the Turkish Government was also utilized and the period of data obtained was based on availability. The performance of Turkish REITs was evaluated using Sharpe's ratio and Treynor's ratio, and the volatility was assessed using MGARCH-BEKK.

The authors found out that Turkish REITs are constantly underperforming and the REITs' returns remain highly volatile and persistent. In addition, findings showed evidence of volatility clustering and the asymmetric impact of shocks. This study further revealed the uniqueness of each of the Turkish REITs due to the lack of evidence of multicollinearity.

However, the limitation of this study is the constraint in obtaining more macro-economic variables of more than ten-years of Turkey's Government bond and the study focused mainly on Turkish REITs.

The result suggests that since Turkish REITs are not mandatory to payout 90% of taxable earnings as dividends, high performance and an appropriate risk management approach are expected. The need for timely revealing performance of T-REITs and associated uncertainty may trigger better performance as discussed in the relationship between disclosure and performance which is recently emphasized in a recent study by Koelbl (2020). With current performance and associated uncertainty in Turkish REITs, the need to protect Turkish REITs investors is highly essential. The result further educates REIT investors that diversification benefits of REITs tend to reduce in extremely risky situations.

This is the first study in the context of Turkish REITs that comprehensively integrated market capitalization of REITs and simultaneous evaluation of performance and the volatility of the Turkish REITs as the world adjusts to the new normal.

This paper aims to examine herding behaviour among investors and traders in UK-listed Real Estate Investment Trusts (REITs) within three market regimes (low, high and extreme volatility periods) from the period June 2004 to April 2016.

Observations of investors in 36 REITs that trade on the London Stock Exchange as at April 2016 were used to analyse herding behaviour among investors and traders of shares of UK REITs, using a Markov regime-switching model.

Although a static herding model rejects the existence of herding in REITs markets, estimates from the regime-switching model reveal substantial evidence of herding behaviour within the low volatility regime. Most interestingly, the authors observed a shift from anti-herding behaviour within the high volatility regime to herding behaviour within the low volatility regime, with this having been caused by the FTSE 100 Volatility Index (UK VIX).

The results have various implications for decisions regarding asset allocation, diversification and value management within UK REITs. Market participants and analysts may consider that collective movements and market sentiment/psychology are determinative factors of risk-return in UK REITs. In addition, general uncertainty in the equity market, proxied by the impact of the UK VIX, may also provide a signal for increasing herding-related risks among UK REITs.

The purpose of this paper is to analyze the effect of investor sentiment, measured with Google internet search data, on volatility forecasts of the US REIT market.

The author uses the S&P US REIT index and collects search volume data from Google Trends for all US REIT. Two different Generalized Autoregressive Conditional Heteroskedastic models are then estimated, namely, the baseline model and the Google augmented model. Using these models, one-step-ahead forecasts are conducted and the forecast accuracies of both models are subsequently compared.

The empirical results reveal that search volume data can be used to predict volatility on the REIT market. Especially in periods of high volatility, Google augmented models outperform the baseline model.

The results imply that Google data can be used on the REIT market as a market indicator. Investors could use Google as an early warning system, especially in periods of high volatility.

This is the first paper to use Google search query data for volatility forecasts of the REIT market.

We examined the dynamic volatility connectedness and diversification strategies among US real estate investment trusts (REITs) and green finance indices.

The DCC-GARCH dynamic connectedness framework and he DCC-GARCH t-copula model were employed in this study.

Using daily data from 2,206 observations spanning from 2 January 2015 to 31 January 2023 this paper presents the following findings: (1) cross-market spillovers exhibited a high correlation and significant fluctuations, particularly during extreme events; (2) our analysis confirmed that REIT acted as net receivers from other green indices, with the S&P North America Large-MidCap Carbon Efficient Index dominating the in-network volatility spillover; (3) this observation suggests asymmetric spillovers between the two markets and (4) a portfolio analysis was conducted using the DCC-GARCH t-copula framework to estimate hedging ratios and portfolio weights for these indices. When REIT and the Dow Jones US Select ESG REIT Index were simultaneously added to a risk-hedged portfolio, our findings indicated that no risk-hedging effect could be achieved. Moreover, the cost and performance of hedging green assets using REIT were found to be comparable.

We first examined the dynamic volatility connectedness and diversification strategies among US REITs and green finance indices. The outcomes of this study carry practical implications for market participants.

The purpose of this study is to examine the return sensitivity of public farmland and timberland real estate investment trusts (REITs) to private-equity farmland, timberland and real estate, long-term corporate bonds and large- and small-cap stocks. The study also examines time-dependent contributions of selected asset classes to farmland and timberland REIT volatility.

The authors use a multi-factor asset pricing model under a seemingly unrelated regression framework to evaluate farmland and timberland REIT returns, and a state-space model with the Kalman filter to evaluate the time-dependent contributors of farmland and timberland REIT volatility. The authors first perform orthogonalized regressions to obtain pure independent factors, and then decompose volatility into individual asset components.

Significant loadings on financial assets are found for both farmland and timberland REITs, suggesting that they are generally driven by some common state variables. Large-cap stocks are found to be the major contributor of farmland and timberland REIT volatility, despite some differing patterns over time.

Empirical analysis of farmland REIT is very scarce. The authors compare the risk-return characteristics of farmland and timberland REITs under a state-space framework with the Kalman filter. This study can improve the understanding of the roles of farmland and timberland REITs in a multi-asset portfolio.

The purpose of this paper is to examine market microstructure differences in stock market quality for hospitality real estate investment trusts (REITs) during the pre- and post-financial crisis eras. It provides insight on different trading strategies based on the underlying liquidity and volatility of hospitality REITs as compared traditional REITs and the broader market.

The paper uses established microstructure measures for liquidity, trading volumes and risk assessment and compares daily and intraday trading patterns of REITs, hospitality REITs and the broad market.

The results suggest a quicker recovery of performance for hospitality REITs and some fundamental increases in liquidity measures post-crisis. The results of the study highlight the differences in trading volumes, liquidity and risk profile of hospitality REITs compared to traditional REITs both in the pre- and post-financial crisis periods.

The quicker recovery of hospitality REITs in key trading measures may suggest flight to quality during periods of high volatility.

This study fills the gap in the literature relative to microstructure studies and provides information to help hotel firms and portfolio managers choose an appropriate organizational structure and investment vehicle, respectively.

The purpose of this paper is to study correlations between the national real estate investment trusts (REIT) markets in the USA and the four Asia‐Pacific countries of Australia, Hong Kong, Japan and Singapore, and document the extent to which the time variation present in these correlations can be explained from a set of 11 economic and financial factors. Both US dollar and local currency returns are used.

Time‐varying correlations are estimated using a DCC‐GARCH model that allows for asymmetries in both the correlations and volatilities. The correlations are then regressed on a set of four economic and seven financial factors, and tests of statistical significance are conducted in order to discriminate between relevant and irrelevant explanatory variables. The authors estimate a fixed‐effects panel regression as well as individual regressions for each dynamic correlation.

Significant time variation is found in the four REIT correlation series. Panel regressions suggest that REIT correlations rise with increases in the interaction of national inflation rates and with higher global equity market uncertainty. It is also found that REIT correlations fall with increases in the US default risk premium and global equity market volume. Relaxing the structure imposed by the panel data model, individual regressions confirm most of the results, although there are some exceptions. It is also found that there are no substantial differences in the dynamics of the correlation coefficients when switching from the US dollar to local currency denominated returns.

Investors in real estate securities across national markets should take into account information about the credit spread, the volatility and volume of global equity markets, and inflation rates when modeling correlations. These variables may alert the investors to the possibility that, under a set of circumstances, investing in real estate across different markets may not provide the expected diversification benefits. Another implication relates to the impact of currency hedging. It appears that the impact of switching from US dollar to local currency denominated returns does not substantially change the time dynamics of the correlations, or the importance of explanatory variables.

Although considerable progress has been made in modelling time‐varying correlations between various REIT markets, to the authors' knowledge, this is one of the first papers to investigate the underlying causes of the co‐movement, especially between the US and Asia‐Pacific markets. The paper's results will help investors and risk managers make better choices by identifying those factors that have more systematic effects on the change in the REIT correlations, rather than more transient forces.

This study is the first to examine the role of idiosyncratic risk in the pricing of European real estate equities. The capital asset pricing model predicts that in equilibrium, investors should hold the market portfolio. As a result, investors should only be rewarded for carrying undiversifiable systematic risk and not for diversifiable idiosyncratic risk. The study is adding to the growing body of countering studies by first examining time trends of idiosyncratic risk and subsequently the pricing of idiosyncratic risk in European real estate equities. The paper aims to discuss these issues.

The study analyses 293 real estate equities from 16 European capital markets over the 1991-2011 period. The framework of Fama and MacBeth is employed. Regressions of the cross-section of expected equity excess returns on idiosyncratic risk and other firm characteristics such as beta, size, book-to-market equity (BE/ME), momentum, liquidity and co-skewness are performed. Due to recent evidence on the conditional pricing of European real estate equities, the pricing is also investigated using the conditional framework of Pettengill et al. Either realised or expected idiosyncratic volatility forecasted using a set of exponential generalized autoregressive conditional heteroskedasticity models are employed.

The initial analysis of time trends in idiosyncratic risk reveals that while the early 1990s are characterised by both high total and idiosyncratic volatility, a strong downward trend emerged in 1992 which was only interrupted by the burst of the dotcom bubble and the 9/11 attacks along with the global financial and economic crisis. The largest part of total volatility is idiosyncratic and therefore firm-specific in nature. Simple cross-correlations indicate that high beta, small size, high BE/ME, low momentum, low liquidity and high co-skewness equities have higher idiosyncratic risk. While size and BE/ME are priced unconditionally from 1991 to 2011, both measures of idiosyncratic risk fail to achieve significance at reasonable levels. However, once conditioned on the general equity market or real estate equity market, a strong positive relationship between idiosyncratic risk and expected returns emerges in up-markets, while the opposite relationship exists in down-markets. The relationship is robust to firm-specific factors and a series of robustness checks.

The results show that ignoring the conditional relationship between idiosyncratic risk and returns might result in the false realisation that idiosyncratic risk does not matter in the pricing of risky (real estate) assets.

This study is the first to examine the role of idiosyncratic risk in the pricing of European real estate equities. The study reveals differences in the pricing of European real estate equities and US REITs. The study highlights that ignoring the conditional relationship between idiosyncratic risk and returns might result in the false realisation that idiosyncratic risk does not matter in the pricing of risky assets.