Property markets play a key role in many local economies and are fundamental to financial stability in a country. The rise in real-estate investment trusts (REITs) has increased the reliance of asset managers on property-related assets as a hedging mechanism in the portfolio construction process.

Real-estate investment trusts are positioned at the intersection of financial markets and the real economy. Their liquidity makes them an attractive vehicle to obtain real-estate exposure, and fund managers have increasingly incorporated REITs into portfolio hedging and risk-management strategies. However, because REITs are traded assets, they are sensitive to shifts in funding conditions, discount rates, and broader risk sentiment. This fact is particularly relevant today, given the recurrence of market stress episodes in recent years, during which risk premiums widen and financial conditions tighten, potentially altering how REIT volatility co-moves with systemic risk measures. This situation makes it important to assess whether REITs remain reliable hedges when systemic conditions deteriorate.

Hedging against price-movement risk in an asset portfolio is an essential part of effective asset management and plays a key role in the preservation of wealth during unstable periods. Part of effective hedging is to ensure that a hedging instrument acts as a hedge during volatile periods and downturns. It is essential to understand how property-related assets react to systemic risk in a country. Research on the dependence of property-related assets on systemic risk is required to determine whether these assets act as sufficient hedging instruments during periods of volatility. If the volatility of property-related assets has a strong dependence on systemic risk, then the hedging ability of these assets is limited. On the other hand, limited volatility dependence will justify the inclusion of these assets as a hedging mechanism in portfolios.

Complicating the issue is the growing evidence showing that the link between real-estate assets and systemic risk is not static. Evidence shows that the beta of real-estate assets shifts across regimes and crises (Voicu & Seiler 2013). It also shows that volatility connectedness increases during periods of extreme global uncertainty and that negative shocks elevate volatility disproportionately (Abdullah et al. 2023). This finding suggests that property assets provide a reasonable hedge during normal periods, but their hedging ability might deteriorate during abnormal periods of uncertainty and volatility. There is not a single linear relationship between systemic risk and real-estate-related assets; therefore, studies should employ non-linear modelling techniques. Yet most of the literature in this field makes use of linear modelling techniques, see for example Zhu and Lizieri (2024), Yong (2015), Li (2012), and Adams, Füss and Schindler (2015) and Gyourko and Nelling (1996). Many studies in this field focus on REITs’ returns and not volatility. Yet, measuring volatility dependence typically provides a more risk-oriented and robust measure of an asset’s ability to serve as a hedge, rather than just working with returns (Hsu, Tseng & Wang 2008). Hsu et al. (2008) provide evidence that volatility modelling outperforms traditional models in hedging. It is essential to research the non-linear volatility dependence of real-estate-related assets on systemic risk.

This focus is especially relevant in emerging markets in which systemic shocks can be transmitted rapidly through capital flows, exchange-rate movements, and domestic funding conditions (Bwowa, Mouton & De Wet 2024; Zhang et al. 2025). In these economies, property-linked assets are a key channel because real estate is typically credit- and rate-sensitive; therefore, shifts in funding costs and risk premia associated with systemic risk can translate into volatility in listed property exposures (De Wet & Botha 2019).

Furthermore, the listed REIT market in major emerging markets has expanded significantly. In South Africa, listed REIT market capitalisation increased from roughly South Africa Rands (ZAR) 25 billion in 2004 (circa 2004) to above ZAR200bn in 2024–2025, indicating a substantially larger and more liquid investable universe (SA REIT Association 2025). In India and China, listed REIT markets are newer but have scaled rapidly: India’s REIT market capitalisation increased from Indian Rupees (INR) 264bn in 2020 to INR1.6 trillion by 2025 (Jones Lang LaSalle [JLL] 2025), while China’s public REIT market expanded from Renminbi (RMB) 31.4bn in 2021 to a total market value exceeding RMB200bn by June 2025 (Shanghai Stock Exchange 2025). These developments support the relevance of examining systemic risk dependence on property-linked assets as these markets deepen and mature. Given the growth of listed REIT markets in several BRICS (Brazil, Russia, India, China, and South Africa) countries, REITs are increasingly considered as liquid instruments for real-estate exposure and potential portfolio hedging, making it necessary to test whether they dampen or amplify systemic risk.

Despite the growth of REIT markets in BRICS economies, it remains unclear which domestic systemic-risk measures transmit into REIT volatility, and whether those relationships are linear through normal conditions and during periods of stress. The main aim of this study is to determine whether there are systemic risk spillovers to the REITs market in major emerging countries. Specifically, the study will focus on BRICS countries, except for Russia, because Russia does not have an established REITs market.

Systemic risk is the economy-wide risk that disruptions in financial markets or institutions trigger widespread instability and spillovers across many assets and sectors, rather than remaining confined to a single firm or market (Voicu & Seiler 2013). While systemic risk cannot be fully diversified away, assets differ in how strongly they are exposed to it. The relevant question for investors is therefore not whether systemic risk can be eliminated, but whether assets such as REITs exhibit lower sensitivity to systemic risk or maintain more stable dependence under stress. If REIT volatility responds weakly to systemic risk measures, or if dependence does not strengthen in stress regimes, REITs can serve as a partial stabiliser that dampens portfolio vulnerability during systemic shocks. Conversely, if REITs amplify systemic risk through stronger volatility spillovers or tail dependence, their hedging usefulness is reduced.

The novelty of this study lies in providing a BRICS-wide, volatility-focused assessment of how domestic systemic risk transmits into REIT markets under both normal and extreme conditions, using one consistent empirical design. Existing work is largely focused on developed REIT markets or single emerging-market case studies, and it typically relies on a single modelling framework that cannot jointly capture time-varying co-movement, directional volatility transmission, and tail-risk amplification. By combining Dynamic conditional correlation–Generalised autoregressive conditional heteroscedasticity (DCC-GARCH), Diebold–Yilmaz vector autoregressive (DY-VAR) variance decompositions, and an asymmetric copula approach, the study separates these channels clearly and produces directly comparable evidence across REITs in BRICS markets. This study will produce decision-relevant results for portfolio construction and risk management.

The core debate in the REIT–systemic risk literature is whether REITs function as reliable hedging and/or diversifying instruments against systemic risk shocks, or whether their risk characteristics deteriorate and spillovers intensify when markets enter stress regimes. The debate is around whether the REIT–systemic risk relationship is stable, and whether the hedging benefit observed in normal conditions can weaken or reverse when dependence becomes stronger and more asymmetric during periods of heightened uncertainty. To evaluate this debate in the BRICS context, the study tests for time-varying co-movements, directional volatility spillovers, and tail-risk amplification, using a complementary multi-model framework. Research remains limited to the volatility dependence between systemic risk measures and property-related assets, such as REITs, in the BRICS context.

To address this issue, the study employs the DCC-GARCH model, the DY-VAR model, and an Asymmetric GARCH-Copula model. Each of these three models provides a different perspective on the spillovers from systemic risk to REITs’ volatility and provides a holistic analysis of this relationship. The DCC-GARCH model shows how the volatility of REITs co-moves with systemic risk and how this relationship evolves with regime shifts. The DY-VAR shows how much of the REIT volatility is explained by shocks in systemic risk. Lastly, the Asymmetric GARCH-Copula model focuses on extreme tail risks and indicates how systemic risk spills over to REIT volatility during extreme periods. To the best of my knowledge, this is the paper that considers systemic risk spillovers to REIT volatility in such a comprehensive way. This paper will provide asset managers with a comprehensive understanding of how REITs in BRICS countries react to systemic risk in various market conditions. This research, in turn, will show the ability as well as the limitations of REITs as a hedging instrument, and thereby provide the necessary information required to effectively use REITs as a hedging tool.

The paradigm of empirical property market research has shifted considerably over the years. Initial research predominantly focused on the link between property prices and traditional economic indicators like Gross Domestic Product (GDP) growth, interest rates, the unemployment rate, and income levels (Bjørnland & Jacobsen 2010). Even though these economic fundamentals are important to the property market, they have limitations in explaining the high volatility of REIT markets (Alkay, Watkins & Keskin 2018). Recently, the growing prominence of REITs as a hedging option in portfolio construction has attracted significant research interest related to the diversification potential of REIT markets (Bossman et al. 2022; Motegi & Iitsuka 2023). A central debate in this literature is whether REITs maintain diversification and hedging characteristics when financial conditions deteriorate, or whether interdependence and spillovers intensify during stress and reduce those benefits. Hence, an increasing number of researchers focus on determining the interdependence between REIT markets and other markets such as stock markets, the commodity market, and the bond market, see for example, Ito (2025) and Odusami (2024).

The results from these studies are mixed, but several researchers provide evidence of non-linear relationships between REITs and other markets; for example, Ito (2025), Alam et al. (2023), and Mensi et al. (2023) found that connectedness between REITs and traditional assets increases during periods of uncertainty. Ito (2025) and Alam et al. (2023) show that during calm periods, the relationship between REITs and traditional markets is broadly insignificant, yet during periods of uncertainty, the relationship between REITs and traditional markets becomes positive and statistically significant. Evidence from Armah and Amewu (2024) shows that linkages between REITs and financial stress are asymmetric across market states, with stronger positive dependence during lower-tail conditions. These findings indicate that a linear estimate of linkages between REITs and other markets could overstate the diversification potential of REITs during extreme periods.

Another strand of research that is particularly relevant to understanding REITs as a potential diversifying asset focuses on systemic risk and volatility spillovers between REITs and other markets. The most recent literature related to this line of research includes work by Hadad, Le and Luong (2024), Katyoka, Riberio and Ling (2024) and Danila (2024). Findings by these researchers highlight the complex and conditional nature of systemic risk spillover dynamics to REITs as well as volatility spillovers from other markets. Findings show that volatility spillovers between REITs and other markets are modest during normal conditions, but strong volatility spillovers exist during extreme market conditions and international uncertainty (Hadad et al. 2024; Katyoka et al. 2024). Interestingly, Hadad et al. (2024) as well as Le and Luong (2024) found that REITs are net receivers of volatility spillovers during normal periods but become transmitters during extreme periods. This phenomenon reduces REITs’ diversification characteristics when markets are extremely volatile. These findings support the argument that the volatility spillover dynamics form systemic risk to REITs, and volatility spillovers from other assets to REITs should not be considered a linear function but should be modelled with non-linear models. Recent work using copula-based and related tail-risk frameworks similarly highlights that diversification benefits can weaken when lower-tail dependence increases during crisis or high-uncertainty periods (Ito 2025; Odusami 2024).

Most of the research in this field focuses on REITs in developed economies. Where developing markets are considered, the evidence is more limited and usually market-specific. For example, Lekhuleni and Ndlovu (2023) show that South African housing prices are cointegrated with GDP and inflation but remain vulnerable in the short run to shocks from mortgage rates, household debt, and foreign investment. Ndamanomhata (2023) reaches a similar conclusion from a market-risk perspective, documenting that South African REITs display time-varying spillovers with equities, bonds, Foreign Exchange (FX), and commodities, and that they can switch between being net transmitters and net receivers depending on the regime. Evidence outside South Africa points to comparable state-dependence. Liu et al. (2023), for instance, found that Hong Kong REITs are strongly interconnected with Chinese markets and global volatility (Volatility Index [VIX]), particularly during coronavirus disease 2019 (COVID-19), which supports the view that international shocks can materially shape REIT–risk dynamics in developing-market settings. Related work also highlights that developing-market REIT volatility can respond asymmetrically to shocks and exhibit strong clustering (Danila 2024). Broader BRICS evidence on volatility transmission and connectedness reinforces this point, suggesting that these markets are exposed to time-varying contagion and that dependence is better treated as regime-dependent rather than constant (Zhang et al. 2025).

Although several studies have investigated REIT volatility and risk spillovers in developing markets, the existing literature remains fragmented with narrow methodological approaches. Existing research typically utilises a single econometric framework, such as GARCH for volatility clustering (Fateye, Ajayi & Ajayi 2022), Vector Error Correction Model (VECM) for macro fundamentals (Lekhuleni & Ndlovu 2023), or spillover indices (Danila 2024; Liu et al. 2023; Ndamanomhata 2023). These studies provide valuable insights, but they are limited in scope because a single model cannot concurrently capture directional volatility transmission, dynamic correlations, and the role of tail risks. In addition, the broader BRICS spillover literature tends to focus on equities, exchange rates, commodities, and policy uncertainty, rather than on REIT markets and the domestic systemic channels that are most relevant to property-linked risk, such as volatility indices and sovereign yields. As a result, the literature provides limited evidence that jointly measures time-varying dependence, directional spillover strength, and extreme tail co-movement between domestic systemic risk measures and REIT volatility across BRICS countries within a single, comparable empirical framework.

Given the complexities of the spillover dynamics between systemic risk and volatility to REITs, a study that comprehensively and consistently analyses these spillovers from multiple angles, across several key developing countries, is required. This study aims to contribute towards filling this research gap by analysing systemic risk spillovers to the REIT market in BRICS countries by means of employing multiple asymmetric models. This approach supports the use of a multi-model design to separate dynamic co-movements, directional spillovers, and tail behaviour within a BRICS setting.

This section starts with the results from the DDC-GARCH model, which shows how volatilities between two time series move together over time. The results from the DY-VAR and Asymmetric GARCH-Copula models then provide additional insights on the volatility relationship by identifying the direction and magnitude of volatility transmission, as well as non-linear and tail-risk dependence.

Table 2 shows the results from the DDC-GARCH model. The coefficients, Endog β₁ and Endog β₂, indicate that there is significant volatility persistence in the endogenous, REIT, and exogenous Yield and volatility variables themselves. Importantly for this study, though, are the dcca₁ and dccb₁ coefficients as they point to the time-varying correlation characteristics between the REITs and the risk measures.

The dcca₁ coefficient shows how strongly a new shock in standardised residuals adjusts the next period’s correlation, thus, if co-movements are shock-dependent. In this case, there is no evidence suggesting that the correlation between the risk measures and the volatility in the REITs of any of these countries is shock-dependent. However, the dccb₁ coefficient indicates that the co-movement of volatilities is long-lasting once established. Thus, once a relationship is established between the REITs and the risk measures, the relationship tends to exhibit statistically significant persistence. In this light, from a hedging perspective, REITs might sufficiently hedge against some shocks in all these countries, but if a relationship develops, the hedging ability might erode. However, these results tell only a part of the story and should be paired with the results from the next two models.

Table 3 shows the results from the DY-VAR model and shows how much of the REIT volatility in each country is explained by volatility in the risk measures of each corresponding country. The results show that a very low percentage of REIT volatility in both China and Brazil is explained by either of the systemic risk measures. Similarly, the South African volatility index explains a very low percentage of South African REIT volatility, and Indian yields explain a very low percentage of REIT volatility in India. On the other hand, the South African yields explain approximately 10% of South African REIT volatility, and the Indian volatility index explains approximately 12.5% of REIT volatility in India.

Even though 10% and 12.5%, respectively, are still relatively low, it does show that some of these systemic risk measures do have some explanatory power over REIT volatility in these countries and that asset managers should consider these variables in their hedging process. However, in general, these results show that the systemic risk measures considered in this study explain a very small portion of the volatility in REITs across the BRICS countries. However, the DY-VAR model is linear and, therefore, reflects dependencies during normal periods, not extreme periods. This is valuable information for portfolio construction during normal periods; yet, as argued previously, it is essential to measure this relationship at extreme levels to inform effective hedging. In this light, the results from the asymmetric GARCH-Copula model, shown in Table 4, are now considered.

Starting with the Kendall Tau output, we show that for most BRICS countries, the rank dependence during normal conditions is positive and very weak. For example, for China, the volatility on REITs is only 1.4 percentage points more likely to move in the same rank order as the volatility index, rather than in the opposite order, and 0.4 percentage points in the case of yields. For Brazil, REIT volatility is only 3.2 percentage points more likely to move in the same rank order as yields and 4.5% less likely to move in the same rank order as the volatility index. For South African yields and the Indian Volatility Index, the probabilities are much higher. In the case of India, REIT volatility is 34 percentage points more likely to move in the same rank order as the volatility index, rather than in the opposite order. Furthermore, South African REIT volatility is 25 percentage points more likely to move in the same rank order as yields. This finding corresponds with the results from the DY-VAR model, which shows that spillovers from yields to South African REIT volatility and Indian spillovers from the Indian volatility index to Indian REITs are much more likely than in other BRICS countries.

Very important from a hedging perspective is the volatility spillovers from the systemic risk measures to REITs during extreme periods. In this light, the 95% upper-tail results show how likely it is for REIT volatility to be extremely high when systemic risk is extremely high. In contrast, the 5% lower-tail results show how likely it is for REIT volatility to be extremely low when systemic risk is extremely low. The results from the 95% upper-tail show that only the volatility in Chinese REIT has a very low dependence on systemic risk at extreme levels. The results show that there is only a 4-percentage point chance that REIT volatility is extreme when the Volatility Index is extremely high and a 6.6-percentage point chance of being extremely high when yields are extremely high. South African REITs have the highest systemic risk dependence during extreme periods. The results show that there is a 58-percentage point chance that South African REIT volatility is extreme when the Volatility index is extremely high and a 62-percentage point chance of being extremely high when yields are extremely high. Furthermore, the results show that there is a 36-percentage point chance that Brazilian REIT volatility is extreme when the Volatility index is extremely high and a 52-percentage point chance of being extremely high when yields are extremely high. Lastly, there is a 42-percentage point chance that Brazilian REIT volatility is extreme when the Volatility index is extremely high and a 33-percentage point chance of being extremely high when yields are extremely high. On the other end of the spectrum, the results show that for all countries, it is likely that REIT volatility is extremely low when systemic risk measures are at extremely low levels.

As a robustness check, the study constructed a special frequency-domain connectedness decomposition. The frequency-domain connectedness decomposition provides a robustness check on the DY-VAR results by testing whether spillovers are primarily a short-run (high-frequency) phenomenon – consistent with transitory shocks and rapid repricing – or whether they reflect long-run (low-frequency) transmission – consistent with more persistent macro and/or financial linkages. Table 5 depicts the Frequency-domain connectedness decomposition results.

The main cross-country conclusions remain unchanged. China exhibits negligible connectedness in both bands, confirming the minimal spillovers found in the DY-VAR. For the countries where connectedness is present, the frequency split adds useful detail. South Africa shows meaningful connectedness in both the short and the long-run, but the results point to a more persistent component linked to yields. In the long-run band, total connectedness is slightly higher, and yields emerge as the more structurally important channel, consistent with the DY-VAR result that South African yields explain the largest share of South African REIT volatility. Interpreted economically, this result indicates that the yield–REIT volatility relationship is not only a short-lived shock effect; rather, it is supported by slower-moving, longer-horizon dynamics (e.g. sustained repricing of discount rates and funding conditions). The short-run band still shows non-trivial connectedness, implying that yields can also transmit more immediate risk during volatility episodes, but the stronger long-run support reinforces yields as a strategic hedging consideration in South Africa.

India displays strong connectedness in both frequency bands, and the volatility index remains the key risk measure while the yield channel stays negligible – fully consistent with the DY-VAR findings. The presence of connectedness in the short-run band suggests that Indian REIT volatility responds to fast-moving equity-market risk sentiment and sudden volatility spikes. At the same time, the persistence of connectedness in the long-run band implies that this relationship is not confined to short-lived stress events; rather, it extends into longer-horizon co-movement in which elevated risk conditions can remain embedded over time. This relationship strengthens the interpretation that the volatility index is a relevant conditioning variable for both tactical (short-run) and strategic (long-run) risk management for Indian REIT exposure.

Brazil remains comparatively modest in overall connectedness, but the decomposition indicates that any linkage is more pronounced at lower frequencies than at higher frequencies. This pattern suggests that spillovers – where they exist – are less about rapid, short-term shock transmission and more about persistent dynamics that accumulate over longer horizons (e.g. gradual repricing of risk conditions). This nuance aligns with the DY-VAR’s generally low explanatory shares, while clarifying that Brazil’s limited connectedness is relatively more ‘long-horizon’ in nature rather than purely transitory.

Overall, the frequency-domain robustness check supports the baseline results from the study. Connectedness is weakest for China, most economically relevant for South Africa (via yields) and India (via the volatility index), and modest for Brazil, with the added insight that South Africa and Brazil’s linkages contain a stronger long-run component, while India’s connectedness is clearly present in both short- and long-run dynamics.

The empirical results support the recent view that REIT–risk linkages are conditional and can look limited in normal periods but become more relevant under stress. The DCC-GARCH results show strong persistence once co-movement develops (the dccb1 terms are large and statistically significant across all country–proxy pairs), which corresponds with Liow and Chen (2007) and Voicu and Seiler (2013), who argue that REIT dependence and systematic risk exposure can shift across regimes rather than remain constant. In the same light, the DY-VAR FEVD results indicate that, for most BRICS markets, domestic systemic proxies explain only a small share of REIT volatility in normal conditions (China: 0.01–0.25; Brazil: 0.88–2.48; South Africa volatility index: 1.47; India yields: 0.39). This result is similar to what Fasanya and Oyewole (2023) and Danila (2024) show in related spillover settings, where linear or average connectedness measures often appear modest outside of stress periods. Where a clearer normal-times channel does appear, it is economically interpretable and consistent with established mechanisms.

For South Africa, the yield channel accounts for the largest FEVD share (about 10.16%), which matches the rate and discount-rate transmission logic emphasised by Bjørnland and Jacobsen (2010) and Yong (2015), and also fits Caballero, Farhi and Gourinchas (2017), who stress that yield movements can reflect broader system-level repricing. This finding aligns with Lekhuleni and Ndlovu (2023) and Ndamanomhata (2023), who document that South African property and REIT-related series are vulnerable to macro-financial shocks and can switch between transmitter and receiver roles. For India, the volatility index explains about 12.51% of REIT volatility, which corresponds with Katyoka et al. (2024), who highlight volatility conditions as an important transmission channel, and is consistent with Hansen and Timmermann (2024), who link volatility and yield conditions to broader risk cycles.

Once extreme dependence is considered, the results line up more directly with the stress-regime literature that argues diversification can weaken when it is needed most. The copula results show that normal dependence (Kendall’s tau) is generally weak for China and Brazil but becomes economically meaningful for South Africa-yield (tau about 0.251) and India-volatility index (tau about 0.339). This result is similar to Mensi, Nekhili and Kang (2022), who show that REIT spillovers differ by quantile and are stronger in adverse states, and it corresponds with Hadad et al. (2024), who report that REIT volatility spillovers intensify under extreme conditions and can change direction across regimes. The tail results reinforce the same point: South Africa shows very high upper-tail dependence (about 0.580 with the volatility index and 0.619 with yields), and India shows meaningful upper-tail dependence (about 0.421 with the volatility index), which is consistent with Abdullah et al. (2023) and Mensi et al. (2023) in showing stronger dependence and spillover behaviour during uncertainty episodes.

By contrast, China shows negligible spillovers in DY-VAR and very low upper-tail dependence (about 0.040–0.066), which provides a clear contrast to the stronger REIT volatility linkages reported by Liu et al. (2023) for Hong Kong REITs during COVID-19. The frequency-domain robustness check supports the same interpretation: where connectedness exists, it is not purely transitory, with South Africa’s yield channel showing a more persistent component and India’s volatility-index channel appearing in both short- and long-run bands, which is consistent with the idea that risk can transmit through both fast-moving sentiment shocks and slower-moving discount-rate and funding dynamics (Bjørnland & Jacobsen 2010; Caballero et al. 2017; Ndamanomhata 2023).

This study provides a comprehensive examination of systemic risk spillovers to REIT volatility in BRICS countries by employing the DCC-GARCH, DY-VAR, and Asymmetric GARCH-Copula models. The findings show that volatility spillovers from systemic risk measures to REITs are generally limited, but spillovers increase during extreme periods, and this relationship exhibits strong persistence once established. Importantly, the results from the DY-VAR provide evidence that systemic risk measures explain only a small percentage of REIT volatility during normal periods. Systemic risk in South Africa and India has the highest explanatory power over REITs relative to the other countries, but the explanatory power remains fairly low. However, the asymmetric GARCH-Copula results highlight that dependencies become substantially more pronounced during extreme market conditions, particularly in South Africa and India, where REIT volatility exhibits strong co-movements with systemic risk in both tails.

In quantitative terms, the DY-VAR results indicate that the domestic systemic risk proxies explain a small share of REIT volatility in normal conditions across most BRICS markets, with China and Brazil remaining close to zero, while South Africa’s yield share (about 10.16%) and India’s volatility-index share (about 12.51%) stand out as the largest normal-times channels. By contrast, the copula results show that dependence is materially stronger in extremes than in normal ranks: for South Africa, the upper-tail dependence rises to about 0.580 (volatility index) and 0.619 (yields), and for India, the upper-tail dependence is about 0.421 (volatility index). This difference between ‘normal-times’ spillover shares and ‘extreme-times’ dependence provides clear evidence that hedging conclusions drawn from linear averages can understate the risk relationship that matters most during stress episodes.

These findings have several important implications for portfolio managers. Firstly, the hedging benefits of REITs depend largely on the state of the market, as well as the country. Therefore, the hedging benefits of REITs should not be considered homogeneous across all the BRICS markets. In normal periods, REITs may serve as partial diversifiers, given the relatively weak dependence on systemic risk. However, during crises, their volatility is more likely to amplify systemic shocks, especially in South Africa and India, thereby limiting their effectiveness as a hedging asset. This situation underlines the need to employ dynamic hedging strategies that incorporate regime shifts and tail risks, rather than relying solely on linear models of risk transmission. It is therefore recommended by the researcher that asset managers adopt a conditional hedging approach when using REITs as a hedging instrument. Stress-testing should be used to model extreme tail risks and be paired with the portfolio construction process. Additionally, managers should exercise caution in assuming homogeneity across BRICS, as the spillover dynamics differ significantly across countries. Tail-risk management should therefore be country-specific, with greater vigilance in South Africa and India, where systemic risk spillovers are most pronounced. By integrating these insights, portfolio managers can enhance diversification benefits during normal periods while mitigating the erosion of hedging effectiveness during systemic crises.

The scientific value addition of the study lies in the consistent, multi-model evidence of how systemic risk transmits into REIT volatility in emerging markets. Most prior work relies on a single framework or a single market, which makes it difficult to separate co-movement, directional spillovers, and tail amplification within a comparable setting. By combining DCC-GARCH (time-varying correlation), DY-VAR (directional variance shares), and an asymmetric copula approach (tail dependence), the study provides a more complete assessment of hedging effectiveness across BRICS REIT markets. The results also provide an applied benchmark: when normal-times spillover shares remain below roughly 1% – 3% (as in China and Brazil), the case for REITs as partial diversifiers is stronger, but when tail dependence approaches the levels observed for South Africa and India, portfolio risk controls should be tightened because diversification can deteriorate during stress.