Does trading mechanism shape cross-market integration? Evidence from stocks and corporate bonds on the Tel Aviv Stock Exchange

Hadad, Elroi Article Does trading mechanism shape cross-market integration? Evidence from stocks and corporate bonds onthe Tel Aviv Stock Exchange Journal of Economics, Finance and Administrative Science Provided in Cooperation with: Universidad ESAN, Lima Suggested Citation: Hadad, Elroi (2025) : Does trading mechanism shape cross-market integration? Evidence from stocks and corporate bonds onthe Tel Aviv Stock Exchange, Journal of Economics, Finance and Administrative Science, ISSN 2218-0648, Emerald Publishing Limited, Leeds, Vol. 30, Iss. 59, pp. 169-188, https://doi.org/10.1108/JEFAS-11-2023-0262 This Version is available at: https://hdl.handle.net/10419/319676 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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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/ Does trading mechanism shape cross-market integration? Evidence from stocks and corporate bonds on the Tel Aviv Stock Exchange Elroi Hadad Department of Industrial Engineering and Management, Shamoon College of Engineering, Beer-Sheva, Israel Abstract Purpose –This study investigates the influence of trading mechanisms on cross-market integration between stocks and corporate bonds on the Tel Aviv Stock Exchange (TASE) during the COVID-19 crisis. Unlike the worldwide practice of trading corporate bonds on an over-the-counter (OTC) market, TASE uses a limit-order-book (LOB) for both stocks and bonds, potentially creating unique volatility dynamics through direct information spillover. We analyze the volatility dynamics and spillover effects between TASE’s stock and corporate bond markets. Design/methodology/approach –We employ an exponential general autoregressive conditional heteroskedastic (EGARCH)(1,1) model to assess the impact of stock market fear, measured by implied volatility, on Tel-Bond 20 Index returns and volatility. A bivariate diagonal Baba-Engle-Kraft-Kroner (BEKK) model is also applied to capture time-series integration and cross-volatility spillovers between the TA-35 Index (stocks) and the Tel-Bond 20 Index (corporate bonds), especially during financial stress. Findings –The EGARCH model reveals a significant contagion effect, with increased stock market fear lowering corporate bond returns and increasing bond volatility. It also indicates a leverage effect,where negative shocks disproportionately amplify bond volatility. Diagonal BEKK results confirm strong cross-market volatility persistence, especially during crises, highlighting substantial financial contagion between stocks and bonds in TASE. While TASE’s market design improves the overall market quality, these findings underscore the LOB trading mechanism in facilitating financial contagion and systemic risk. Practical implications –The LOB trading in TASE facilitates direct information flow, intensifying volatility spillover and cross-market integration, with the degree of integration fluctuating based on market conditions. Investors and managers should consider alternative hedging strategies during volatile periods, as stock market sentiment significantly impacts bond stability. Regulators should assess how trading mechanisms affect market integration and risk, especially during periods of distress. Originality/value –This study offers new insights into how trading mechanisms influence cross-market dynamics, contributing to the literature on market design and financial contagion. Keywords Corporate bonds, Stocks, Cross-market integration, Trading mechanism, Conditional volatility Paper type Research paper 1. Introduction Firms use capital markets to finance their business activity via the issuance of equity or corporate debt. Since both equity and corporate bonds are claims on the same asset, their Journal of Economics, Finance and Administrative Science 169 JEL Classification — G12, G14, G15 © Elroi Hadad. Published in Journal of Economics, Finance and Administrative Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode I am grateful to Shmuel Hauser, the Ono Academic College and Bizportal.co.il for granting me the opportunity to access and study their unique sentiment indexes. Financial aid by the Ono Research Fund is gratefully acknowledged. Funding and/or conflicts of interests/competing interests: The author declares that he has no known competing financial interests or personal relationships. This work was supported by the Ono Research Fund. The author has no relevant financial or non-financial interests to disclose. The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2077-1886.htm Received 3 December 2023 Revised 28 November 2024 Accepted 28 November 2024 Journal of Economics, Finance and Administrative Science Vol. 30 No. 59, 2025 pp. 169-188 Emerald Publishing Limited e-ISSN: 2218-0648 p-ISSN: 2077-1886 DOI 10.1108/JEFAS-11-2023-0262 expected returns should be associated with rational pricing in liquid and frictionless markets. In a perfectly liquid and rational market, equity and corporate bonds should have linked expected returns, as they both represent claims on the same asset, as noted by Merton (1974). However, this cross-market linkage might be altered in times of crisis and generally change over time, resulting from changes in macroeconomic conditions or investor sentiment (Campbell et al., 2020). If equity and corporate bond markets exhibit integration, risk premia in one market should influence the other, and the relative magnitude of return premia for stocks should align with those of contingent bond returns premia (Choi and Kim, 2018). Financial literature supports this view of integration, indicating that stock-bond correlations can vary between positive and negative values, reflecting shifts in the risk-return tradeoff over time. The risk-return tradeoff between stocks and corporate bonds, as noted by Choi and Kim (2018), is less documented and often examined in isolation within either equity or debt markets, primarily in the USA. This gap is surprising given the significant increase in debt financing and increased corporate bond trading volume, especially post-financial crisis (Abraham et al., 2021; Bai et al., 2019;Graham et al., 2015). Gebhardt et al. (2005) suggest that spillover effects may arise when investors in one market (e.g. corporate bonds) underreact to information from another market (e.g. stocks). Examining the co-movements of price volatility between stock and corporate bond markets is crucial, as corporate bond returns volatility is not just driven by different risk characteristics (i.e. downside risk, credit risk and liquidity risk) but can also be affected by informational frictions, leading to volatility spillover effects. Recent studies highlight positive intertemporal relationships between stock and corporate bond markets, including momentum spillover from the USA stocks to bonds (Choi and Kim, 2018;Downing et al., 2009;Gebhardt et al., 2005;Gurun et al., 2016;Haesen et al., 2017; Hong et al., 2012) and cross-sectional evidence between the USA stocks and corporate bonds of the same firms (Anginer and Yildizhan, 2018;van Zundert and Driessen, 2022). Choi and Kim (2018) further highlight that integration between the USA equities and corporate bonds weakens with noisier investor demand, underscoring the role of information asymmetry and investor behavior. Notably, van Zundert and Driessen (2022) find stronger cross-sectional correlations between the USA equities and non-investment-grade bonds than with investmentgrade bonds, suggesting a different risk-return tradeoff and potential mispricing. Similarly, Bali et al. (2021) observed mispricing between the USA stocks and corporate bonds, with economic uncertainty premia varying according to heterogeneous risk-aversion levels in these markets. They attribute these differences to clientele behavior, noting that institutional investors dominate the USA corporate bond market, while retail investors are more active in equity markets (Bajo et al., 2013). Retail and institutional investors differ significantly in information efficiency, with institutional investors possessing greater resources and skills for processing market data (Boulatov et al., 2013;Hendershott et al., 2015). Institutional investors tend to gather firmspecific information and closely monitor management activities (Bajo et al., 2013), exhibiting different risk preferences and investment strategies compared with retail investors (Boulatov et al., 2013;Hendershott et al., 2015). Given that momentum spillover often stems from initial underreaction to information (Barberis et al., 1998;Hong and Stein, 1999;Wu et al., 2023), this heterogeneity in information processing can drive spillovers across equity and corporate bond markets, influencing cross-asset price dynamics. Motivated by recent evidence on the time-series relationship between stocks and corporate bonds and the potential impact of clientele behavior on asset dynamics, we hypothesize that financial integration between stocks and corporate bonds is more pronounced in corporate bond markets with high retail investor activity. Retail investors, often less informed and more susceptible to psychological biases and sentiment (Barber and Odean, 2008;Kaniel et al., 2008;Kumar and Lee, 2006), may impact market dynamics significantly. This aspect of financial interconnectedness aligns with extensive literature on financial contagion, where markets exhibit heightened co-movements following a shock in one of these markets (Patel JEFAS 30,59 170 et al., 2022). Our study investigates the specific dynamics of interconnectedness between equity and corporate bond markets during and after the COVID-19 pandemic, highlighting how these markets respond to external shocks and the potential influence of high retail trading activity on market dynamics. To examine financial market integration within a high retail trading environment, we focus on volatility connectedness between stocks and bonds on the Tel Aviv Stock Exchange (TASE), where retail trading is prevalent across both markets (Abudy and Shust, 2023;Abudy and Wohl, 2018;Gur-Gershgoren et al., 2020;Hadad and Kedar-Levy, 2024). TASE’s setup allows stocks and bonds to trade on the same limit-order book (LOB), which facilitates direct information flow and reduces information heterogeneity between institutional and retail investors. In contrast, corporate bonds in the USA are traded over-the-counter (OTC), limiting retail investors’ participation due to low transparency and high transaction costs (Edwards et al., 2007). Abudy and Wohl (2018) document that the Israeli corporate bond market exhibits higher liquidity and narrower spreads than the USA OTC bond market, suggesting a stronger interdependence and information flow between stock and corporate bond markets and making TASE an ideal context for studying cross-market integration. Our primary objective is to analyze co-movements from stocks and corporate bonds, to understand connectedness amid heightened uncertainty periods and to explore potential volatility dynamics driven by information spillover. To capture spillover, we utilize a univariate EGARCH model (Nelson, 1991) to Tel-Bond 20 index returns (representing the bond market) and assess the impact of a change in the stock market “fear gauge” – proxied by the implied volatility of Tel Aviv-35 (TA-35) Index returns (representing the stock market) – on its returns and volatility. Additionally, we employed a bivariate diagonal Baba-Engle-Kraft-Kroner (BEKK) model (Engle, 2002) on the returns of the TA-35 and Tel-Bond 20 indices to capture return co-movement and illustrate market integration. Recognizing that bond mispricing intensifies during crises (Batten et al., 2018), we examined volatility dynamics throughout the COVID-19 crisis and other periods of financial distress from 2017 to 2022. Our findings reveal that market returns demonstrate volatility clustering, particularly during financial distress periods, including the COVID-19 pandemic, political uncertainty and inflation concerns. We identify a strong contagion effect from stocks to bonds: as stock market fear rises, corporate bond returns fall and volatility escalates, indicating heightened risk premiums. The diagonal BEKK model confirms robust interdependencies, with major news events amplifying conditional volatility and correlations between stocks and bonds. These results suggest that stock and corporate bond markets in TASE are economically integrated, with their co-movement highly sensitive to informational frictions. We innovate in two aspects. First, our findings highlight the critical role of investor behavior during financial turmoil, such as the COVID-19 pandemic, inflation fears and political uncertainty, in shaping corporate bond dynamics. Investor reactions to new information and heightened risk perceptions significantly influence market integration, reinforcing cross-asset dynamics between stocks and corporate bonds (Baele et al., 2020; Ponrajah and Ning, 2023). Secondly, while prior studies in OTC bond markets associate market integration with shifts between contagion and flight-to-safety behavior (Baur and Lucey, 2009;Ponrajah and Ning, 2023), our findings from TASE reveal a strong contagion effect, which intensifies during heightened periods. We attribute these differences to variations in clientele behavior and market structure, whereby TASE’s unique LOB system is characterized by high retail trading activity and transparency (Abudy et al., 2024;Hadad and Kedar-Levy, 2024), in contrast to institutional investor dominance in OTC markets (Bajo et al., 2013). Retail investors, being more prone to sentiment-driven behavior (Baker and Stein, 2004;Baker and Wurgler, 2007), amplify price volatility and contagion effects, as sentiment spreads quickly across asset classes. While TASE’s market design enhances the overall market quality (Abudy and Shust, 2023;Abudy et al., 2024), our findings suggest that these same features make the market highly responsive to systemic shocks, emphasizing the dual-edged impact of trading infrastructure on market quality. Journal of Economics, Finance and Administrative Science 171 Our study has important implications for investors, managers and regulators. First, our results suggest that investors should consider the price dynamics between markets, especially during financial turmoil, to make informed investment decisions. Secondly, our results highlight the need for diversification and risk management, as corporate bonds are not only driven by differences in risk characteristics but also affected by informational frictions. Thirdly, the role of TASE’s unique trading mechanism in shaping market integration between stocks and bonds underscores the importance of understanding the impact of trading mechanisms on market dynamics for regulators to ensure market stability. The rest of the paper is structured as follows: Section 2 provides a literature review describing the theoretical and empirical evidence of market integration between stocks and bonds and the potential impact of investor sentiment on corporate bond returns; Section 3 describes the data and variables; Section 4 details the methodology; Section 6 presents empirical results and Section 6 concludes. 2. Literature review Equity and corporate bonds are theoretically linked by shared economic fundamentals, as both represent claims on a firm’s underlying assets (Merton, 1974). This relationship suggests that risk premiums across equity and corporate bonds should be correlated, given their mutual dependence on firm-specific fundamentals (Gebhardt et al., 2005). However, empirical evidence reveals significant deviations, particularly during periods of economic crises, macroeconomic shifts or changes in investor sentiment, highlighting the dynamic and evolving nature of cross-market integration (Campbell et al., 2020;Choi and Kim, 2018). Historical evidence from the USA markets provides further insights into these dynamics. Studies indicate a strong correlation between stock and bond returns, suggesting that equities often lead bonds in reflecting new information (Chordia et al., 2017;Haesen et al., 2017). For instance, Huang et al. (2015) find that a decline in stock liquidity negatively affects the USA bond yield spreads, with stronger effects observed after the financial crisis. Similarly, Chung et al. (2019) highlight the impact of idiosyncratic stock volatility on bond returns via concurrent stock price movements. Choi and Kim (2018) emphasize that the integration between stocks and corporate bonds fluctuates with investor sentiment, indicating that sentiment plays a significant role in driving spillovers between these markets. Radi et al. (2024) further explored behavioral influences, showing that herding and anti-herding behaviors in stock and corporate bond markets substantially shape stock-bond return correlations. Recent studies further reveal time-varying correlations between stocks and bonds, which oscillate between negative and positive, depending on market conditions. Negative dependence arises when bonds, seen as safe-haven assets, attract investors during periods of heightened risk (Baele et al., 2020;Opitz and Szimayer, 2018). This flight-to-safety behavior prompts reallocations from bonds to stocks during market upturns and back to bonds during downturns (Aslanidis et al., 2020;Ponrajah and Ning, 2023). Rising interest rates also reinforce this negative dynamic by increasing bond returns while reducing equity returns (Ponrajah and Ning, 2023). Empirical studies confirm this negative co-movement during crises (Aslanidis et al., 2020;Baele et al., 2020;Connolly et al., 2005), attributing them to flight-to-quality, where investors seek higher-quality bonds or flight-to-liquidity, where investors prioritize more liquid assets (Acharya et al., 2013;Acharya and Pedersen, 2005; Chen et al., 2007;Dick-Nielsen et al., 2012;Friewald et al., 2012;Longstaff, 2004;Næs et al., 2011;P� astor and Stambaugh, 2003;Tachibana, 2020). These dynamics, as noted by Baur and Lucey (2009), contribute to financial stability by mitigating investor losses during turbulent periods. Conversely, positive dependence often signals financial contagion, emerging after economic shocks when investors adjust positions across both markets to manage heightened risks (Katsiampa et al., 2022). This contagion typically stems from systemic JEFAS 30,59 172 economic risks or significant macroeconomic changes that simultaneously impact stocks and bonds (Bernanke and Kuttner, 2005;Boyd et al., 2005;Yang et al., 2009). Although extensive research has documented contagion within stock markets (Forbes and Rigobon, 2002a; Morana and Beltratti, 2008;Nguyen et al., 2022) and bond markets (Cronin et al., 2016; Forbes and Rigobon, 2002b;Leschinski and Bertram, 2017;Li et al., 2022), studies specifically addressing stock-bond contagion remain limited (Baur and Lucey, 2009;Choi and Kim, 2018). Recent studies suggest that stock-bond integration alternates between contagion during downturns and flight-to-quality during crises, underscoring the sensitivity of crossasset dynamics to market stress (Baele et al., 2020;Baur and Lucey, 2009;Cappiello et al., 2006;Ponrajah and Ning, 2023). However, these findings predominantly focus on government bonds, leaving corporate bond dynamics underexplored. Other research highlights the role of sentiment and irrational behavior in bond markets. For example, Piazzesi (2005) shows that Federal Open Market Committee announcements significantly affect bond market volatility, suggesting underreaction among bond investors. Nayak (2010) and Bethke et al. (2017) identify sentiment-driven co-movements in bond yield spreads and flight-to-quality during periods of low sentiment, while Lu et al. (2010) demonstrate that information uncertainty and asymmetry are priced into the USA corporate bond yield spreads. International studies further corroborate these trends, finding that sentiment-driven behavior influences corporate bond returns across various markets (Goldstein and Namin, 2023;Lithin et al., 2023;Mukherjee, 2019;Rath, 2023). However, these studies predominantly focus on OTC corporate bond markets dominated by institutional investors rather than retail-sized participants (Bajo et al., 2013;Edwards et al., 2007) who are more susceptible to sentiment-driven behavior (Brown and Cliff, 2004). In the Israeli context, several studies document the significant role of retail investors in enhancing market liquidity and efficiency and in contributing to market quality (Abudy and Shust, 2023;Abudy and Wohl, 2018;Abudy et al., 2024). Hadad and Kedar-Levy (2024) further highlight the positive impact of retail activity on corporate bond returns and volatility. While these studies suggest that sentiment plays a role in shaping bond returns, they do not explore the information spillover between equity and corporate bond markets. Compared to OTC markets, where institutional investors dominate, the prevalence of retail investors in the TASE could amplify such spillovers, as retail participants are more prone to sentiment-driven behavior (Baker and Wurgler, 2006,2007). We utilize unique data from TASE to investigate these dynamics, examining the impact of retail investor activity and centralized trading infrastructure on stock-bond interactions during periods of heightened uncertainty. Details on the data and variables are provided in Section 3. 3. Data and variables Our dataset includes daily closing prices of the TA-35 Index and the Tel-Bond 20 Index. The TA 35 Index consists of 35 companies with the highest market capitalization, which collectively account for 55% of the trading volume (TASE, 2021) and hence represent the equity market. The Tel-Bond 20 Index consists of 20 corporate bonds with the highest market capitalization, which capture most of the trading volume (Abudy and Wohl, 2018) and hence represent the debt market. Daily observations of the TA-35 Index and Tel-Bond 20 Index are publicly available at https://www.tase.co.il/en, which is the official TASE website. To account for time-varying volatility and cross-market correlations across a range of financial scenarios, we have chosen a sample period spanning from June 5, 2017, to June 26, 2022. This timeframe covers the periods before, during and after the exceptional market turbulence triggered by the COVID-19 crisis, providing a comprehensive view of market reactions during different phases of the crisis. Additionally, it encompasses the volatility observed during the inflation concerns of 2022 and the period marked by political uncertainty and early election speculation in 2018. Daily returns are defined as follows: Journal of Economics, Finance and Administrative Science 173 Rt¼lnðPtÞ�lnðPt−1Þ;(1) Where Rtis the logarithmic price change and Ptis the daily closing price of the Tel-Bond 20 Index and the TA-35 Index and at time t. Figure 1 illustrates the price trends of the TA-35 Index and Tel-Bond 20 Index, showing a general upward movement from June 2017 to March 2020, except for a dip in late 2018. Both indexes dropped sharply during the COVID-19 crisis in March 2020 but rebounded similarly from April 2020. They declined again in Q2 2022, reflecting inflation concerns. These patterns suggest that the TA-35 Index and Tel-Bond 20 Index could be correlated. Both Pearson correlation (0.792) and Spearman rank-order correlation (0.768) are highly significant, indicating the existence of covariation between the stock and bond markets and suggesting common behavior in the price trends for both stocks and bonds. Figure 2 shows the price trends of the TA-35 Index and Tel-Bond 20 Index returns. The figure depicts a common trend, with both indexes exhibiting similar spikes in returns and volatility over time. Specifically, Figure 2 shows that both indexes experienced significant spikes in returns volatility during the COVID-19 crisis period, suggesting that the pandemic had a significant impact on both indexes and underscores the interdependence between the two indexes in times of crisis. The figure also depicts a common volatility trend in returns in December 2018, in line with the rise in the political uncertainty and speculation about early elections (resulting from the resignation of key government officials), which have been observed to influence the TA-35 Index. Further, another volatility trend is observed in January 2022, in line with the rise in fear in the markets resulting from fear of inflation. These results suggest for possible interconnectedness between the two indexes and a potential spillover 1,200 1,400 1,600 1,800 2,000 17 18 19 20 21 22 Panel (a). TA-35 Index 320 340 360 380 400 17 18 19 20 21 22 Panel (b). Tel-Bond 20 Index Source(s): Author’s own work Figure 1. Daily closing prices of TA-35 and Tel-Bond 20 Indexes JEFAS 30,59 174 effect between the equity and debt markets. These results suggest that a bivariate GARCH can be employed in order to study the co-movement between the stock and bond markets. Table 1 shows summary statistics, unit root tests and heteroscedasticity tests for TA35 Index and Tel-Bond 20 Index returns for the entire sample. Results in Panel (a) show positive average returns for both indexes, suggesting a bullish trend in stock and bond markets. The returns of the TA 35 Index exhibit a negative skewness, while the Tel-Bond 20 Index returns show a positive skewness, implying that the stock market is more likely to observe outlying negative returns. Considering the volatility, as expected, the TA 35 Index returns exhibit a much larger standard deviation and lower kurtosis than Tel-Bond 20 Index returns, suggesting that corporate bond returns are more concentrated about the mean; however, the kurtosis values of both index returns are higher than three, indicating that the returns distribution could be fat-tailed. Jarque–Bera results confirm the departure from normality, while the conditional heteroscedasticity test suggests the existence of the autoregressive conditional heteroskedasticity (ARCH) effect in both index returns, implying volatility clustering in returns. Panel (b) results show the Augmented Dickey–Fuller (ADF) test (Dickey and Fuller, 1981) and Phillips–Perron (PP) test (Perron, 1988) for the daily returns of the TA-35 index and TelBond 20 index. Results show that both ADF and PP values are highly significant, suggesting stationarity in TA-35 and Tel-Bond Index returns and stationarity in price levels. These results suggest that GARCH modeling is suitable to model the conditional variances and covariance of the index returns. Lastly, we consider the volatility of the TA-35 index to quantify how changes in investors’ fear in the stock market influence the returns and conditional volatility of the Tel-Bond 20 index. Following Hadad and Kedar-Levy (2024), we use the implied volatility in TASE –0.08 –0.04 0.00 0.04 0.08 17 18 19 20 21 22 Panel (a). TA-35 Index returns –0.03 –0.02 –0.01 0.00 0.01 0.02 0.03 0.04 17 18 19 20 21 22 Panel (b). Tel-Bond 20 Index returns Source(s): Author’s own work Figure 2. Daily returns of TA-35 and Tel-Bond 20 Indexes Journal of Economics, Finance and Administrative Science 175 (VIXTA) indicator to capture stock market volatility, which measures the implied volatility of TA-35 index options. Similar to the widely used VIX indicator for S&P100 (Pi~ neiro-Chousa et al., 2017;Whaley, 2000), the VIXTA captures the fear in the stock market, allowing us to analyze the impact of the change in stock market volatility on corporate bond returns volatility to enhance our understanding about the volatility dynamics in TASE. Daily observations of VIXTA are from the Bizportal website (https://www.bizportal.co.il/publictrustindices). We calculate the variation of the VIXTA indicator as the change in at time t, namely ΔVIXTAt¼VIXTAt�VIXTAt−1:(2) 4. Methodology To study financial market dynamics among stocks and corporate bonds in the TASE and potential volatility dynamics and spillovers from stocks to corporate bonds, we examine potential volatility dynamics and spillovers from stocks to corporate bonds by modeling the impact of changes in VIXTA, a measure capturing market fear (Baker and Wurgler, 2007;Hadad and Kedar-Levy, 2024;Whaley, 2000), on bond returns and volatility. This step is crucial, as a significant impact of changes in VIXTA on corporate bond returns may reveal risk-return dynamics between equity and bond markets, suggesting that stock market fluctuations influence risk perceptions in TASE’s bond market. To capture the volatility patterns of corporate bond returns, we employ univariate GARCH models, which are well suited for handling the volatility clustering commonly observed in financial time series as well as the fat-tailed distribution of asset returns. Given the clustering and distributional characteristics of Tel-Bond 20 Index returns, we tested several asymmetric GARCH models, including EGARCH (Nelson, 1991), Threshold GARCH (TGARCH) (Zakoian, 1994) and Glosten-Jagannathan-Runkle GARCH (GJR-GARCH) (Glosten et al., 1993), which capture asymmetrical effects in which negative shocks can lead to greater volatility than positive shocks of similar magnitude. Following Hadad and Kedar-Levy (2024), we found EGARCH(1,1) to provide the best fit, with the lowest Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC) values among models tested. Model diagnostics confirmed its suitability, with an insignificant autoregressive conditional heteroskedasticity lagrange multiplier Table 1. Summary statistics and unit root tests for TA-35 and Tel-Bond returns Rs Rb Panel (a): summary statistics Mean 0.000251 8.29E�05 Median 0.000587 0.000229 Maximum 0.070995 0.034540 Minimum �0.066993 �0.025511 Std. Dev 0.010548 0.003134 Skewness �0.610842 0.789193 Kurtosis 10.07618 36.45240 Jarque–Bera 2672.776*** 58133.91*** ARCH(1) 0.3255*** 0.249*** Observations 1,244 1,244 Panel (b): unit root tests ADF �35.5854*** �15.5341*** PP �35.737*** �28.1269*** Note(s): Rs: TA-35 returns; Rb: Tel-Bond 20 returns; sample range: 5 June 2017–26 June 2022. Significance: ***1%; **5% and *10% Source(s): Author’s own work JEFAS 30,59 176 6. Conclusions Existing financial literature highlights the impact of sentiment on stock returns volatility, emphasizing the role of investor behavior in shaping stock market dynamics. Research has also indicated spillover effects between stock and bond markets, suggesting bidirectional influences between price changes in these asset classes. Despite stocks and corporate bonds both representing claims on the same underlying asset (Merton, 1974) and having potential for information spillover, behavioral studies exploring cross-market integration between these two assets remain limited. This study offers novel insights into the linkage between stocks and corporate bonds in the TASE, a market distinguished by high retail trading activity. Our findings demonstrate a robust return and volatility connectedness between the markets, characterized by time-varying correlation. We document volatility clustering and substantial interdependencies between the markets, particularly during periods of financial turmoil, such as the COVID-19 pandemic, political uncertainty and inflation concerns. Both stocks and corporate bonds show responsiveness to innovations, and the dynamic conditional correlation between the markets is notably strong. Contrary to the flight-to-safety behavior often observed in OTC markets during crises (Baele et al., 2020;Ponrajah and Ning, 2023), our results point to a positive contagion effect in TASE, where interdependencies between stocks and bonds increase during turbulent times. These findings underscore significant market integration within TASE, offering valuable insights into the connection between stock and corporate bond markets. However, they also raise concerns for investors, as contagion during crises causes bond prices to fall alongside stocks, undermining diversification benefits when they are most needed. Unlike the stabilizing effect of negative stock-bond dynamics observed in other OTC markets (Baur and Lucey, 2009), the contagion observed in TASE exacerbates instability during turbulent periods. Our results highlight the pivotal role of TASE’s exchange-based bond market and its centralized trading mechanism in shaping market integration. The platform’s direct information flow facilitates volatility spillovers and amplifies interdependencies between stocks and corporate bonds, particularly during periods of financial stress. This pattern aligns with global evidence of volatility spillovers in markets with high retail trading activity, highlighting the significant influence of retail investors on cross-market dynamics. While Abudy and Shust (2023) emphasize TASE’s contribution to market quality through enhanced liquidity, price discovery and stability, our findings reveal a dual-edged nature of this infrastructure. The same features that promote market quality also enable the rapid transmission of shocks, leaving TASE highly susceptible to systemic risk. This duality underscores how transparency and centralized trading can simultaneously foster efficient information flow and exacerbate destabilizing contagion during heightened uncertainty. Overall, this study reinforces the interconnectedness of stock and bond markets and highlights the critical role of trading mechanisms in shaping cross-market linkages. The findings have broad implications for investors, regulators and market participants. For investors, understanding these linkages is essential for making informed decisions and developing effective risk management strategies, particularly during volatile periods. For regulators, recognizing the dynamics of market integration is essential to maintaining financial stability and designing effective policies for centralized exchanges and OTC markets characterized by high retail trading activity. Despite these insights, our study has certain limitations. While this analysis is robust within TASE’s unique LOB trading environment, the findings may not fully generalize to OTCs with different trading mechanisms. Furthermore, the study’s focus on specific crisis periods – including the COVID-19 pandemic, late 2018 political uncertainty and early 2022 inflation concerns – provides key insights into volatility spillovers during financial stress but limits the applicability of the results to other periods and contexts. Future research could explore the influence of macroeconomic and monetary conditions and other behavioral factors in driving volatility spillover between stocks and corporate bonds Journal of Economics, Finance and Administrative Science 183 in the TASE and in similar markets. Additionally, exploring the causal patterns between stocks and corporate bonds in TASE and the spillover effect between stocks and corporate bonds in other OTC markets will provide valuable insights into cross-market linkages in global markets. Such studies would further contribute to the understanding of the relative advantages and drawbacks of OTC markets compared to centralized exchanges. References Abraham, F., Cortina, J.J. and Schmukler, S.L. (2021), “The rise of domestic capital markets for corporate financing: lessons from East Asia”, Journal of Banking and Finance, Vol. 122, 105987, doi: 10.1016/j.jbankfin.2020.105987. Abudy, M.(M.) and Shust, E. (2023), “Does market design contribute to market stability? Indications from a corporate bond exchange during the COVID-19 crisis”, Journal of Economics and Business, Vol. 123, 106105, doi: 10.1016/j.jeconbus.2022.106105. Abudy, M. and Wohl, A. (2018), “Corporate bond trading on a limit order book exchange”, Review of Finance, Vol. 22 No. 4, pp. 1413-1440, doi: 10.1093/rof/rfx054. Abudy, M.M., Nathan, D. and Wohl, A. (2024), “Mutual fund flows and government bond returns”, Journal of Banking and Finance, Vol. 162, 107119, doi: 10.1016/j.jbankfin.2024.107119. Acharya, V.V. and Pedersen, L.H. (2005), “Asset pricing with liquidity risk”, Journal of Financial Economics, Vol. 77 No. 2, pp. 375-410, doi: 10.1016/j.jfineco.2004.06.007. Acharya, V.V., Amihud, Y. and Bharath, S.T. (2013), “Liquidity risk of corporate bond returns: conditional approach”, Journal of Financial Economics, Vol. 110 No. 2, pp. 358-386, doi: 10.1016/j.jfineco.2013.08.002. Allen, D.E. and McAleer, M. (2018), “Theoretical and empirical differences between diagonal and full BEKK for risk management”, Energies, Vol. 11 No. 7, 1627, doi: 10.3390/en11071627. Anginer, D. and Yildizhan, Ç. (2018), “Is there a distress risk anomaly? Pricing of systematic default risk in the cross-section of equity returns”, Review of Finance, Vol. 22 No. 2, pp. 633-660, doi: 10.1093/rof/rfx044. Aslanidis, N., Christiansen, C. and Savva, C.S. (2020), “Flight-to-safety and the risk-return tradeoff: European evidence”, Finance Research Letters, Vol. 35, 101294, doi: 10.1016/j. frl.2019.09.009. Baele, L., Bekaert, G., Inghelbrecht, K. and Wei, M. (2020), “Flights to safety”, Review of Financial Studies, Vol. 33 No. 2, pp. 689-746, doi: 10.1093/rfs/hhz055. Bai, J., Bali, T.G. and Wen, Q. (2019), “Common risk factors in the cross-section of corporate bond returns”, Journal of Financial Economics, Vol. 131 No. 3, pp. 619-642, doi: 10.1016/j. jfineco.2018.08.002. Bajo, E., Barbi, M., Bigelli, M. and Hillier, D. (2013), “The role of institutional investors in public-toprivate transactions”, Journal of Banking and Finance, Vol. 37 No. 11, pp. 4327-4336, doi: 10.1016/j.jbankfin.2013.07.004. Baker, M. and Stein, J.C. (2004), “Market liquidity as a sentiment indicator”, Journal of Financial Markets, Vol. 7 No. 3, pp. 271-299, doi: 10.1016/j.finmar.2003.11.005. Baker, M. and Wurgler, J. (2006), “Investor sentiment and the cross-section of stock returns”, The Journal of Finance, Vol. 61 No. 4, pp. 1645-1680, doi: 10.1111/j.1540-6261.2006.00885.x. Baker, M. and Wurgler, J. (2007), “Investor sentiment in the stock market”, The Journal of Economic Perspectives, Vol. 21 No. 2, pp. 129-151, doi: 10.1257/jep.21.2.129. Bali, T.G., Subrahmanyam, A. and Wen, Q. (2021), “The macroeconomic uncertainty premium in the corporate bond market”, Journal of Financial and Quantitative Analysis, Vol. 56 No. 5, pp. 1653-1678, doi: 10.1017/S0022109020000538. Barber, B.M. and Odean, T. (2008), “All that glitters: the effect of attention and news on the buying behavior of individual and institutional investors”, Review of Financial Studies, Vol. 21 No. 2, pp. 785-818, doi: 10.1093/rfs/hhm079. JEFAS 30,59 184 Barberis, N., Shleifer, A. and Vishny, R. (1998), “A model of investor sentiment”, Journal of Financial Economics, Vol. 49 No. 3, pp. 307-343, doi: 10.1016/s0304-405x(98)00027-0. Batten, J.A., Khaw, K.L.H. and Young, M.R. (2018), “Pricing convertible bonds”, Journal of Banking and Finance, Vol. 92, pp. 216-236, doi: 10.1016/j.jbankfin.2018.05.006. Baur, D. and Lucey, B. (2009), “Flights and contagion-An empirical analysis of stock-bond correlations”, Journal of Financial Stability, Vol. 5 No. 4, pp. 339-352, doi: 10.1016/j. jfs.2008.08.001. Bernanke, B.S. and Kuttner, K.N. (2005), “What explains the stock market’s reaction to federal reserve policy?”, The Journal of Finance, Vol. 60 No. 3, pp. 1221-1257, doi: 10.1111/j.15406261.2005.00760.x. Bethke, S., Gehde-Trapp, M. and Kempf, A. (2017), “Investor sentiment, flight-to-quality, and corporate bond comovement”, Journal of Banking and Finance, Vol. 82, pp. 112-132, doi: 10.1016/j.jbankfin.2017.02.007. Bollerslev, T. (1990), “Modelling the coherence in short-run nominal exchange rates: a multivariate generalized arch model”, The Review of Economics and Statistics, Vol. 72 No. 3, p. 498, doi: 10.2307/2109358. Boulatov, A., Hendershott, T. and Livdan, D. (2013), “Informed trading and portfolio returns”, The Review of Economic Studies, Vol. 80 No. 1, pp. 35-72, doi: 10.1093/restud/rds020. Boyd, J.H., Hu, J. and Jagannathan, R. (2005), “The stock market’s reaction to unemployment news: why bad news Is usually good for stocks”, The Journal of Finance, Vol. 60 No. 2, pp. 649-672, doi: 10.1111/j.1540-6261.2005.00742.x. Brown, G.W. and Cliff, M.T. (2004), “Investor sentiment and the near-term stock market”, Journal of Empirical Finance, Vol. 11 No. 1, pp. 1-27, doi: 10.1016/j.jempfin.2002.12.001. Campbell, J.Y., Pflueger, C. and Viceira, L.M. (2020), “Macroeconomic drivers of bond and equity risks”, Journal of Political Economy, Vol. 128 No. 8, pp. 3148-3185, doi: 10.1086/707766. Cappiello, L., Engle, R.F. and Sheppard, K. (2006), “Asymmetric dynamics in the correlations of global equity and bond returns”, Journal of Financial Econometrics, Vol. 4 No. 4, pp. 537-572, doi: 10.1093/jjfinec/nbl005. Chang, C.L. and McAleer, M. (2017), “The correct regularity condition and interpretation of asymmetry in EGARCH”, Economics Letters, Vol. 161, pp. 52-55, doi: 10.1016/j. econlet.2017.09.017. Chen, L., Lesmond, D.A. and Wei, J. (2007), “Corporate yield spreads and bond liquidity”, The Journal of Finance, Vol. 62 No. 1, pp. 119-149, doi: 10.1111/j.1540-6261.2007.01203.x. Choi, J. and Kim, Y. (2018), “Anomalies and market (dis)integration”, Journal of Monetary Economics, Vol. 100, pp. 16-34, doi: 10.1016/J.JMONECO.2018.06.003. Chordia, T., Goyal, A., Nozawa, Y., Subrahmanyam, A. and Tong, Q. (2017), “Are capital market anomalies common to equity and corporate bond markets? An empirical investigation”, Journal of Financial and Quantitative Analysis, Vol. 52 No. 4, pp. 1301-1342, doi: 10.1017/ S0022109017000515. Chung, K.H., Wang, J. and Wu, C. (2019), “Volatility and the cross-section of corporate bond returns”, Journal of Financial Economics, Vol. 133 No. 2, pp. 397-417, doi: 10.1016/j. jfineco.2019.02.002. Connolly, R., Stivers, C. and Sun, L. (2005), “Stock market uncertainty and the stock-bond return relation”, Journal of Financial and Quantitative Analysis, Vol. 40 No. 1, pp. 161-194, doi: 10.1017/s0022109000001782. Cronin, D., Flavin, T.J. and Sheenan, L. (2016), “Contagion in Eurozone sovereign bond markets? The good, the bad and the ugly”, Economics Letters, Vol. 143, pp. 5-8, doi: 10.1016/j. econlet.2016.02.031. Dick-Nielsen, J., Feldh€ utter, P. and Lando, D. (2012), “Corporate bond liquidity before and after the onset of the subprime crisis”, Journal of Financial Economics, Vol. 103 No. 3, pp. 471-492, doi: 10.1016/j.jfineco.2011.10.009. Journal of Economics, Finance and Administrative Science 185 Dickey, D.A. and Fuller, W.A. (1981), “Likelihood ratio statistics for autoregressive time series with a unit root”, Econometrica, Vol. 49 No. 4, p. 1057, doi: 10.2307/1912517. Downing, C., Underwood, S. and Xing, Y. (2009), “The relative informational efficiency of stocks and bonds: an intraday analysis”, Journal of Financial and Quantitative Analysis, Vol. 44 No. 5, pp. 1081-1102, doi: 10.1017/S0022109009990305. Edwards, A.K., Harris, L.E. and Piwowar, M.S. (2007), “Corporate bond market transaction costs and transparency”, The Journal of Finance, Vol. 62 No. 3, pp. 1421-1451, doi: 10.1111/j.15406261.2007.01240.x. Engle, R.F. (2002), “Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models”, Journal of Business and Economic Statistics, Vol. 20 No. 3, pp. 339-350, doi: 10.1198/073500102288618487. Engle, R.F. and Kroner, K.F. (1995), “Multivariate simultaneous generalized arch”, Econometric Theory, Vol. 11 No. 1, pp. 122-150, doi: 10.1017/S0266466600009063. Fengler, M.R., Herwartz, H. and Raters, F.H.C. (2017), pp. “25-37, Multivariate volatility models”, doi: 10.1007/978-3-662-54486-0_2. Forbes, K. and Rigobon, R. (2002a), “Measuring stock market comovements”, The Journal of Finance, Vol. 57 No. 5, pp. 2223-2261, doi: 10.1111/0022-1082.00494. Forbes, K. and Rigobon, R. (2002b), “No contagion, only interdependence: measuring stock market comovements”, The Journal of Finance, Vol. 57 No. 5, pp. 2223-2261, doi: 10.1111/00221082.00494. Friewald, N., Jankowitsch, R. and Subrahmanyam, M.G. (2012), “Illiquidity or credit deterioration: a study of liquidity in the US corporate bond market during financial crises”, Journal of Financial Economics, Vol. 105 No. 1, pp. 18-36, doi: 10.1016/j.jfineco.2012.02.001. Gebhardt, W.R., Hvidkjaer, S. and Swaminathan, B. (2005), “Stock and bond market interaction: does momentum spill over?”, Journal of Financial Economics, Vol. 75 No. 3, pp. 651-690, doi: 10.1016/j.jfineco.2004.03.005. Glosten, L.R., Jagannathan, R. and Runkle, D.E. (1993), “On the relation between the expected value and the volatility of the nominal excess return on stocks”, The Journal of Finance, Vol. 48 No. 5, pp. 1779-1801, doi: 10.1111/j.1540-6261.1993.tb05128.x. Goldstein, M.A. and Namin, E.S. (2023), “Corporate bond liquidity and yield spreads: a review”, Research in International Business and Finance, Vol. 65, 101925, doi: 10.1016/j. ribaf.2023.101925. Graham, J.R., Leary, M.T. and Roberts, M.R. (2015), “A century of capital structure: the leveraging of corporate America”, Journal of Financial Economics, Vol. 118 No. 3, pp. 658-683, doi: 10.1016/ J.JFINECO.2014.08.005. Gur-Gershgoren, G., Kedar-Levy, H. and Hadad, E. (2020), “Deep-market by IAS-19: a unified crosscountry approach for discount rate selection*”, Multinational Finance Journal, Vol. 24, pp. 3-4. Gurun, U.G., Johnston, R. and Markov, S. (2016), “Sell-side debt analysts and debt market efficiency”, Management Science, Vol. 62 No. 3, pp. 682-703, doi: 10.1287/mnsc.2014.2145. Hadad, E. and Kedar-Levy, H. (2024), “The impact of retail investor sentiment on the conditional volatility of stocks and bonds: evidence from the Tel-Aviv stock exchange”, International Review of Economics and Finance, Vol. 89, pp. 1303-1313, doi: 10.1016/J.IREF.2023.09.002. Haesen, D., Houweling, P. and van Zundert, J. (2017), “Momentum spillover from stocks to corporate bonds”, Journal of Banking and Finance, Vol. 79, pp. 28-41, doi: 10.1016/j. jbankfin.2017.03.003. Hendershott, T., Livdan, D. and Sch€ urhoff, N. (2015), “Are institutions informed about news?”, Journal of Financial Economics, Vol. 117 No. 2, pp. 249-287, doi: 10.1016/j. jfineco.2015.03.007. Hong, H. and Stein, J.C. (1999), “A unified theory of underreaction, momentum trading, and overreaction in asset markets”, The Journal of Finance, Vol. 54 No. 6, pp. 2143-2184, doi: 10.1111/0022-1082.00184. JEFAS 30,59 186 Hong, Y., Lin, H. and Wu, C. (2012), “Are corporate bond market returns predictable?”, Journal of Banking and Finance, Vol. 36 No. 8, pp. 2216-2232, doi: 10.1016/j.jbankfin.2012.04.001. Huang, H.H., Huang, H.Y. and Oxman, J.J. (2015), “Stock liquidity and corporate bond yield spreads: theory and evidence”, Journal of Financial Research, Vol. 38 No. 1, pp. 59-91, doi: 10.1111/ jfir.12052. Kaniel, R., Saar, G. and Titman, S. (2008), “Individual investor trading and stock returns”, The Journal of Finance, Vol. 63 No. 1, pp. 273-310, doi: 10.1111/j.1540-6261.2008.01316.x. Katsiampa, P., Yarovaya, L. and Zie ˛ba, D. (2022), “High-frequency connectedness between Bitcoin and other top-traded crypto assets during the COVID-19 crisis”, Journal of International Financial Markets, Institutions and Money, Vol. 79, 101578, doi: 10.1016/j.intfin.2022.101578. Khalid, A. and Ahmad, Z. (2023), “Stock–bond co-movement in ASEAN-5: the role of financial integration and financial development”, International Journal of Emerging Markets, Vol. 18 No. 5, pp. 1033-1052, doi: 10.1108/ijoem-11-2020-1312. Kumar, A. and Lee, C.M.C. (2006), “Retail investor sentiment and return comovements”, The Journal of Finance, Vol. 61 No. 5, pp. 2451-2486, doi: 10.1111/j.1540-6261.2006.01063.x. Leschinski, C. and Bertram, P. (2017), “Time varying contagion in EMU government bond spreads”, Journal of Financial Stability, Vol. 29, pp. 72-91, doi: 10.1016/j.jfs.2017.01.007. Li, P., Guo, Y. and Meng, H. (2022), “The default contagion of contingent convertible bonds in financial network”, The North American Journal of Economics and Finance, Vol. 60, 101661, doi: 10.1016/j.najef.2022.101661. Lithin, B.M., Chakraborty, S., Iyer, V., Nikhil, M.N. and Ledwani, S. (2023), “Modelling asymmetric sovereign bond yield volatility with univariate GARCH models: evidence from India”, Cogent Economics and Finance, Vol. 11 No. 1, doi: 10.1080/23322039.2023.2189589. Longstaff, F.A. (2004), “The flight-to-liquidity premium in U.S. treasury bond prices”, Journal of Business, Vol. 77 No. 3, pp. 511-526, doi: 10.1086/386528. Lu, C.W., Chen, T.K. and Liao, H.H. (2010), “Information uncertainty, information asymmetry and corporate bond yield spreads”, Journal of Banking and Finance, Vol. 34 No. 9, pp. 2265-2279, doi: 10.1016/j.jbankfin.2010.02.013. McAleer, M. (2019), “What they did not tell you about algebraic (non-) existence, mathematical (IR-) regularity, and (non-) asymptotic properties of the dynamic conditional correlation (DCC) model”, Journal of Risk and Financial Management, Vol. 12 No. 2, p. 61, doi: 10.3390/ jrfm12020061. Merton, R.C. (1974), “ON the pricing of corporate debt: the risk structure of interest rates*”, The Journal of Finance, Vol. 29 No. 2, pp. 449-470, doi: 10.1111/j.1540-6261.1974.tb03058.x. Morana, C. and Beltratti, A. (2008), “Comovements in international stock markets”, Journal of International Financial Markets, Institutions and Money, Vol. 18 No. 1, pp. 31-45, doi: 10.1016/ j.intfin.2006.05.001. Mukherjee, K.nath (2019), “Demystifying yield spread on corporate bonds trades in India”, AsiaPacific Financial Markets, Vol. 26 No. 2, pp. 253-284, doi: 10.1007/s10690-018-09266-w. Næs, R., Skjeltorp, J.A. and Ødegaard, B.A. (2011), “Stock market liquidity and the business cycle”, The Journal of Finance, Vol. 66 No. 1, pp. 139-176, doi: 10.1111/j.1540-6261.2010.01628.x. Nayak, S. (2010), “Investor sentiment and corporate bond yield spreads”, Review of Behavioral Finance, Vol. 2 No. 2, pp. 59-80, doi: 10.1108/19405979201000004. Nelson, D.B. (1991), “Conditional heteroskedasticity in asset returns: a new approach”, Econometrica, Vol. 59 No. 2, p. 347, doi: 10.2307/2938260. Nguyen, T.N., Phan, T.K.H. and Nguyen, T.L. (2022), “Financial contagion during global financial crisis and COVID–19 pandemic: the evidence from DCC–GARCH model”, Cogent Economics and Finance, Vol. 10 No. 1, doi: 10.1080/23322039.2022.2051824. Opitz, S. and Szimayer, A. (2018), “What drives flight to quality?”, Accounting and Finance, Vol. 58 No. S1, pp. 529-571, doi: 10.1111/acfi.12315. Journal of Economics, Finance and Administrative Science 187 P� astor, � L. and Stambaugh, R.F. (2003), “Liquidity risk and expected stock returns”, Journal of Political Economy, Vol. 111 No. 3, pp. 642-685, doi: 10.1086/374184. Patel, R., Goodell, J.W., Oriani, M.E., Paltrinieri, A. and Yarovaya, L. (2022), “A bibliometric review of financial market integration literature”, International Review of Financial Analysis, Vol. 80, 102035, doi: 10.1016/j.irfa.2022.102035. Perron, P. (1988), “Trends and random walks in macroeconomic time series. Further evidence from a new approach”, Journal of Economic Dynamics and Control, Vol. 12, pp. 2-3, doi: 10.1016/ 0165-1889(88)90043-7. Piazzesi, M. (2005), “Bond yields and the federal reserve”, Journal of Political Economy, Vol. 113 No. 2, pp. 311-344, doi: 10.1086/427466. Pi~ neiro-Chousa, J., Vizca� ıno-Gonz� alez, M. and P� erez-Pico, A.M. (2017), “Influence of social media over the stock market”, Psychology and Marketing, Vol. 34 No. 1, pp. 101-108, doi: 10.1002/ mar.20976. Ponrajah, J. and Ning, C. (2023), “Stock–bond dependence and flight to/from quality”, International Review of Financial Analysis, Vol. 86, 102467, doi: 10.1016/j.irfa.2022.102467. Radi, S., Gebka, B. and Kallinterakis, V. (2024), “The wisdom of the madness of crowds: investor herding, anti-herding, and stock-bond return correlation”, Journal of Economic Behavior and Organization, Vol. 224, pp. 966-995, doi: 10.1016/j.jebo.2024.07.005. Rath, P.K. (2023), “Nexus between Indian financial markets and macro-economic shocks: a VAR approach”, Asia-Pacific Financial Markets, Vol. 30 No. 1, pp. 131-164, doi: 10.1007/s10690022-09372-w. Tachibana, M. (2020), “Flight-to-quality in the stock–bond return relation: a regime-switching copula approach”, Financial Markets and Portfolio Management, Vol. 34 No. 4, pp. 429-470, doi: 10.1007/s11408-020-00361-5. TASE (2021), “2021 annual report”, available at: https://info.tase.co.il/Eng/Lists/gen_res/0133_ annual_review/2021__annualreview_eng.pdf van Zundert, J. and Driessen, J. (2022), “Stocks versus corporate bonds: a cross-sectional puzzle”, Journal of Banking and Finance, Vol. 137, 106447, North-Holland, doi: 10.1016/J. JBANKFIN.2022.106447. Whaley, R.E. (2000), “The investor fear gauge”, Journal of Portfolio Management, Vol. 26 No. 3, pp. 12-17, doi: 10.3905/jpm.2000.319728. Wu, B., Min, F. and Wen, F. (2023), “The stress contagion among financial markets and its determinants”, The European Journal of Finance, Vol. 29 No. 11, pp. 1267-1302, doi: 10.1080/ 1351847X.2022.2111222. Yang, J., Zhou, Y. and Wang, Z. (2009), “The stock-bond correlation and macroeconomic conditions: one and a half centuries of evidence”, Journal of Banking and Finance, Vol. 33 No. 4, pp. 670-680, doi: 10.1016/j.jbankfin.2008.11.010. Zakoian, J.-M. (1994), “Threshold heteroskedastic models”, Journal of Economic Dynamics and Control, Vol. 18 No. 5, pp. 931-955, Elsevier, doi: 10.1016/0165-1889(94)90039-6. Corresponding author Elroi Hadad can be contacted at: [email protected] For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected] JEFAS 30,59 188