Exchange rates, bond yields and the stock market: Nonlinear evidence of Indonesia during the COVID-19 period

Prananta, Billy; Alexiou, Constantinos Article Exchange rates, bond yields and the stock market: Nonlinear evidence of Indonesia during the COVID-19 period Asian Journal of Economics and Banking (AJEB) Provided in Cooperation with: Ho Chi Minh University of Banking (HUB), Ho Chi Minh City Suggested Citation: Prananta, Billy; Alexiou, Constantinos (2024) : Exchange rates, bond yields and the stock market: Nonlinear evidence of Indonesia during the COVID-19 period, Asian Journal of Economics and Banking (AJEB), ISSN 2633-7991, Emerald, Leeds, Vol. 8, Iss. 1, pp. 83-99, https://doi.org/10.1108/AJEB-12-2022-0157 This Version is available at: https://hdl.handle.net/10419/334115 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/ Exchange rates, bond yields and the stock market: nonlinear evidence of Indonesia during the COVID-19 period Billy Prananta Bank Indonesia, Jakarta, Indonesia, and Constantinos Alexiou School of Management, Cranfield University, Bedford, UK Abstract Purpose –The authors explore the relationship between the exchange rate, bond yield and the stock market as well as the effect of capital market dynamics on the exchange rate before and during the COVID-19 pandemic. Design/methodology/approach –The authors employ a non-linear autoregressive distributed lag (NARDL) methodology using daily data of the Indonesian economy over the period 2012–2021. Findings –Whilst, over the full sample period, the authors find no cointegration between the exchange rate, the 10-year bond yield and stock market, for the COVID-19 period, evidence of cointegration is present. Furthermore, the results suggest that asymmetric effects are evident both in the short as well as the long run. Originality/value –To the best of the authors’knowledge, this is the first time that the relationship between the exchange rate, bond yield and the stock market as well as the effect of capital market dynamics on the exchange rate before and during the COVID-19 pandemic has been explored in the case of the Indonesian economy. Keywords Capital market dynamics, Exchange rate, Asymmetric effect, Bond market, Stock market Paper type Research paper 1. Introduction The increasingly integrated global economy has accelerated the growth of foreign currency transactions, notably in international transaction payments. These transactions are mostly non-physical in nature and are related to international trade payments and investment of foreign capital in the capital markets that are identified as capital flows. The role of the capital market is crucial in helping the economy, particularly for developing countries, which generally experience a deficit and seek funds to finance economic activities from investors, especially through the stocks and bonds market. After the Asian Currency Crisis in 1997, many developing countries reduced vulnerabilities arising from external debt by issuing bonds in local currencies (Hofmann et al., 2021). Due to limited funds from domestic investors, developing countries regularly issue local currency bonds as means of attracting funds from foreign investors. However, such efforts have not been able to The effect of capital market dynamics 83 JEL Classification —C32, F31, G11 © Billy Prananta and Constantinos Alexiou. Published in Asian Journal of Economics and Banking. 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 may be seen at http://creativecommons. org/licences/by/4.0/legalcode On behalf of all authors, the corresponding author states that there is no conflict of interest. The authors have no relevant financial or nonfinancial interests to disclose. The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2615-9821.htm Received 3 December 2022 Revised 4 February 2023 13 March 2023 Accepted 17 March 2023 Asian Journal of Economics and Banking Vol. 8 No. 1, 2024 pp. 83-99 Emerald Publishing Limited e-ISSN: 2633-7991 p-ISSN: 2615-9821 DOI 10.1108/AJEB-12-2022-0157 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/83/9525927/ajeb-12-2022-0157.pdf by ZBW German National Library of Economics user on 16 December 2025 eliminate currency risk issues as foreign investors have always the option of converting their assets to their preferred currency. According to Juhro et al., (2022), foreign capital flows make emerging market countries vulnerable to external shocks. In particular, inflows of foreign capital, to a great extent, are determined by the prevailing economic conditions and the level of yield offered. As such, negative investment perception entails an outflow of capital which in turn causes a disruption in the domestic economy and more vulnerable to external shocks. Engel and Wu (2018) provide evidence that liquidity yield on sovereign bonds has significant explanatory power to influence the exchange rate movements in G10 currencies. They also found that interest rates and lagged adjustment terms for the real exchange rate are important determinants of exchange rate movements. Furthermore, Bodart and Reding (1999) show that their study explains that the level of exchange rate variability influences international bonds and stock correlations in European countries. Fundamentally, inflows of capital constitute a significant source of finance for developing countries as a means of spurring economic growth, enhancing financial sector competitiveness, enabling greater investment activities and smoothing out consumption (Juhro et al., 2022;International Monetary Fund, 2012). The state of the fundamentals and the degree economic openness, the currency rate regime and the macroeconomic policies adopted are all significant factors that affect foreign capital flows. In this context, stronger economies can offer higher yields, hence attracting more foreign capital inflow. Global economic shocks, however, can disrupt a country’s economy through a reversal of foreign capital flows. Countries that operate under a fixed exchange rate system or a similar system as happened in the 1990s in Latin America and Southeast Asia are prone to pronounced currency crisis stemming from exchange rate speculation. As such in optimizing the advantages of foreign capital investment whilst mitigating the risk of currency crises in the future, it is imperative that we examine the relationship between capital flows and exchange rate movements. In this paper, we investigate the relationship between exchange rate movements and capital market transactions in the bond and stock markets and explore the possibility of nonlinearities in the underlying relationships. More specifically, we focus on the movement of bond price, the stock index –in terms of daily price returns –and the exchange rate over the period 2012–2021. In addition, thisstudy also investigates the capital marketdynamicsduring theCOVID-19 crisis. To this objective, we investigate the asymmetric cointegration among variables using the nonlinear autoregressive distributed lag (NARDL) approach developed by Shin et al. (2014). Using the NARDL model will allow us to simultaneously find and analyse both negative and positive asymmetric cointegration among variables in the short and long run, simultaneously. Several empirical studies have previously been conducted to investigate the relationship between the stock market and the exchange rate using different methodologies. However, there are still limited studies that use the NARDL method to investigate the relationship between variables, especially those related to exchange rates, government bonds and stock markets. To the best of our knowledge, this is the first study that employs the NARDL methodology in the context of the Indonesian economy and hence offers significant policy implications to be considered by policymakers, investors and portfolio managers when anticipating potential volatility in exchange rates, government bonds and stock markets. The rest of the paper is organized as follows: section 2 touches on the relevant literature in the area whilst section 3 focuses on the empirical investigation utilized in this study. Section 4 presents and discusses the results, and finally, section 5 provides some concluding remarks. 2. Relevant literature The literature on exchange rate determination is inundated with studies that employ macroeconomic indicators, capital market indices and microstructural approaches to AJEB 8,1 84 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/83/9525927/ajeb-12-2022-0157.pdf by ZBW German National Library of Economics user on 16 December 2025 investigate the impact of the bond and stock market on currency rate movements. For instance, in an emerging market context, Jongwanich and Kohpaiboon (2013) explored the impact of capital flows on the exchange rate in Asian countries and affirmed that the structure of capital flows plays a crucial role in determining how they affect exchange rates. The study showed that capital market investment and loan from banks have a bigger influence on currency appreciation than direct investment from overseas. They argued that the relatively stable and concentrated nature of foreign direct investment flows, especially in the tradable and export-oriented sectors, caused the slow pace of adjustment in the exchange rate. Therefore, by closely observing the development of investment portfolios, it will provide us with a better understanding of the movement of exchange rates. In the context of developed countries, there are studies from Lace et al. (2015) and Engel and Wu (2018) who studied the effect of government bond yields and other macroeconomic indicators on the exchange rate. Lace et al. (2015) found that United States (US) and German government debt yields can be utilized to determine the EUR/US$ exchange rate movements. Similar results were also found by Engel and Wu (2018) who observed a strong causal relationship between government bond liquidity and exchange rates. In contrast to previous studies, according to Rosnawintang et al. (2021), by using monthly data from 2006 to 2018, they found no long-run association among the US$/IDR currency rate and the yield on 10-year Indonesian sovereign bonds. Those factors, however, have a twoway causal association in the short run. In the same spirit, Soni et al. (2018) looked at the impact of various macroeconomic factors on the US$/INR exchange rates from 2000 to 2017 and found that government bonds are a significant predictor that affects the US$/INR exchange rate. Furthermore, using quarterly data from 1983 to 2014, Hsing (2016) established a positive impact between the South African government bond yield, US real gross domestic product (GDP), US stock price, South African inflation and exchange rate volatility. In so far as portfolio investment affects exchange rate movements, a number of studies have explored the impact of changes in equity market and currency volatility (see for instance, Andersen et al., 2007;Ehrmann et al., 2011;Kal et al., 2015;Raza and Wu, 2018). BahmaniOskooee and Sohrabian (1992) by using Granger causality and cointegration methodologies established a two-way association among the equity market and currency rate in the short run, but no long-run association among the equity market andthe domestic currency rate was found. Furthermore, using daily data from 1986 to 1998, Granger et al. (2000) examined the relationship between exchange rates and stock markets in Asian countries. The emerging evidence suggested a mixed picture as for Japan and Indonesia, no link was established, whilst in the case of Korea, the exchange rate was found to affect the stock price. For the rest of the countries in the sample, the stock price to a certain extent was found to affect the exchange rate. In another study, Nieh and Lee (2001) when investigated the interaction between stock prices and exchange rates in the G-7 economies failed to establish a long-run relationship. When studied the relationship between the stock market and currency rate in the context of 17 Organisation for Economic Co-operation and Development economies, Hau and Rey (2006) found that better returns in the domestic equities market relative to the foreign equity market are linked to a depreciation of the domestic currency. This finding however contradicts the view that increasing stock markets are followed by rising exchange rates. In the Indonesian context, Anggitawati and Ekaputra (2020) investigated the relationship between the total amount of net foreign investments, government securities, equity markets and movements in domestic currency. Applying Granger causality and Vector Autoregression (VAR) methodologies on daily data from 2011–2016, they found a bidirectional causality between foreign investment in Indonesia’s financial securities and the US$/IDR currency rate. It was also shown that total international capital fund flows to the domestic financial market had an impact on the US$/IDR currency rate’s appreciation, while foreign capital outflows caused a depreciation of the US$/IDR. The effect of capital market dynamics 85 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/83/9525927/ajeb-12-2022-0157.pdf by ZBW German National Library of Economics user on 16 December 2025 On a different note, and by using a microstructure approach, Rahman (2021) evidence suggests that the US$/IDR is significantly influenced by the time lags of foreign transactions, Non-Deliverable Forward (NDF) rate (US$/IDR), the US$/IDR spot price and the Bloomberg JPMorgan Asia Dollar index (ADXY index). In the long run, domestic individual transactions, non-deliverable forward transactions and the ADXY index were found to be important predictors of US$/IDR whilst market dominance and asymmetric information among Indonesian FOREX market participants was revealed. In view of the evidence set our previously, it can be discerned that several macroeconomic and financial variables have been identified as determinants of the exchange rate. According to Jare~ no et al. (2019), studies using classic approaches such as cointegration, linear regression or Granger causality might indeed enable us to gain invaluable insights into the short and long-run relationships but do not capture potential asymmetries in asset price dynamics. In their study, Baek and Choi (2021) argue that the assumption of a symmetrical effect on asset prices may not necessarily hold in the capital market, since market players in the foreign currency market may respond differently to changes in asset prices. As an intuitive explanation, asset price dynamics can affect exchange rate movements differently depending on their holdings, whether they are assets of domestic or foreign investors. It is therefore imperative that in the empirical part that will follow we address the gap in the extant literature by exploring any possible asymmetries in the interaction between capital market asset price and exchange rate movements both in the short and the long-run. In this direction, we will employ a nonlinear autoregressive distributed lag (NARDL) model suggested by Shin et al. (2014) which is an asymmetric extension of the already wellestablished linear autoregressive distributed lag (ARDL) bounds testing procedure developed by Pesaran et al. (2001). 3. Empirical investigation 3.1 The development of capital flows in Indonesia Before we set out to empirically explore the relationship between the capital market and the exchange rate, it would be appropriate to take a cursory glance at the development of capital inflows in Indonesia. Along with the growth of economic activity, the development of the capital market in Indonesia has experienced significant growth. This development is driven by the investment grade status that Indonesia received in 2011, which encourages foreign investors to invest in Indonesia’s economy, particularly in the capital market. In addition, capital market’s growth is inextricably linked to the growing demand for funding, which will be used to finance the government’s deficit as well as for private sector activities. In the bond market, the Indonesian’s government is the primary issuer of bonds, accounting for 91% of all issuance as of December 2021. The remaining portion is held by corporate bonds, Islamic bonds and asset-backed securities (Otoritas Jasa Keuangan, 2022). The Indonesian government has issued an increasing number of sovereign bonds in line with the expansionary stance of its fiscal policy. Government bonds have increased on average by 21% annually over the past ten years. In 2021, government bonds outstanding have reached 4.679 tn IDR or grew almost five times from the position at the end of 2012 which was recorded at 820 tn IDR. Figures 1 and 2suggest that the yield movement of the 10-year government bond yield has fluctuated in the last decade as a result of fundamental conditions and external sentiment. The large inflow of foreign capital in 2012 drove yields to their lowest level in February 2012 of 5.05%, while the highest yield was recorded in September 2015 at 9.69%. Moreover, a significant change in sovereign bond ownership during the last decade is observed with foreign ownership of all government bonds dwindling from 42% in 2018 to 19% at the end of 2021. AJEB 8,1 86 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/83/9525927/ajeb-12-2022-0157.pdf by ZBW German National Library of Economics user on 16 December 2025 3.2 Indonesia’s equity market development In addition to the bond market, the Indonesian stock market has played an increasingly important role in the capital market over the past ten years, as seen by the rise in the stock market capitalization value (see Figure 3). Market capitalization increased over the previous ten years, growing by 100.04% from IDR 4,127 tn (equivalent to US$ 427 bn) in 2012 to IDR 8,256 tn (equivalent to US$ 579 bn) in 2021. Despite the Euro crisis in 2015 and COVID-19 in 2020 having a big negative impact on the stock market, the Jakarta Composite Index (JCI) index has greatly increased over the last 10 years (see Figure 4). JCI index was able to continue to grow at an average of 6.16% per year to reach 6,581 until the end of 2021 or grow 52% compared to the 2012 position which was Figure 1. Indonesia’s sovereign bonds and foreign ownership during 2012–2021 Figure 2. 10-year government bond yields during 2012–2021 The effect of capital market dynamics 87 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/83/9525927/ajeb-12-2022-0157.pdf by ZBW German National Library of Economics user on 16 December 2025 recorded at 4,317. The weight of the Indonesian stock market is dominated by the financial, infrastructure and technology sectors, which reach 57%, while the weight of other sectors is less than 10%. Based on the composition of ownership, there has been a substantial change in equity ownership composition in the Indonesian stock market (KSEI, 2022). Foreign investors held 45.50% of the total value shares at the end of December 2017, while domestic investors held 54.50%. In 2021, these figures changed to 41.24% and 58.76%, respectively. The number of domestic investors in the Indonesian capital market has increased as a result of the rapid growth of retail investors in Indonesia. In comparison to the position in the previous year (3.88 million investors), the number of domestic investors in the capital market increased significantly by 92.99% (or an increase of 3.61 million investors) to 7.49 million investors in 2021, of which 81.48% were young investors. 4,317 4,274 5,227 4,593 5,297 6,356 6,195 6,300 5,979 6,581 12.94% ͵0.98% 22.29% ͵12.13% 15.32% 19.99% ͵2.54% 1.70% ͵5.09% 10.08% ͵40% ͵20% 0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200% 3,000 3,500 4,000 4,500 5,000 5,500 6,000 6,500 7,000 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Jakarta Composite Index 2012-2021 JCI Index-lhs JCI Index Growth-rhs Source(s): Figure has been created by the authors using publicly available data for Indonesia’s Composite Index (JCI) from 2012 to 2021, sourced from Bloomberg Financial Data Services Figure 3. Indonesia’s equity market capitalization 2012–2021 and composite index sector weights as of June 2022 Figure 4. The dynamics and growth of the Indonesia composite index (JCI) from 2012 to 2021 AJEB 8,1 88 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/83/9525927/ajeb-12-2022-0157.pdf by ZBW German National Library of Economics user on 16 December 2025 3.3 Data and methodology 3.3.1 Data. In line with the objective of this study, which is to investigate the relationship between the capital market and the exchange rate, we make use of the following data: (a) the closing price of US$ to IDR, (b) the closing price of Indonesia 10-year government bond yield – being the most transacted and used as a benchmark in bond trades (DJPPR, 2021) and (c) the closing price of Indonesia Stock Index Composite (IDX) which is a composite index of all equities listed on the Indonesia Stock Exchange are included in this index (previously referred to as the Jakarta Stock Exchange). The currencies used in this study are determined by their transaction market share, which is US$/IDR (see Figure 5). With a proportion of 93.65% in April 2019, US$/IDR transactions are the most traded currency pairs in Indonesia’s foreign exchange market (Bank for International Settlements, 2019). To provide more clarity about the relationship among variables, the data series used in this study is based on daily data, which is divided into two parts as follows: full sample, with a period of 10 years, starting from the early January 2012 to end of December 2021, which have 2,610 observations and a subsample, with a period of two years, starting from the early January 2020 to end of December 2021, which have 523 observations. The subsample is intended to examine the dynamics during the COVID-19 pandemic period. See Tables A1 and A2 in Appendix for summary statistics and correlation matrix. Given that this study employs daily data as means of acquiring a better understanding of the variables that drive the movement of the US$/IDR currency rate and to examine the impact of independent variable transmission more concretely we have left out potentially other key macroeconomic variables such as inflation, trade imbalance (export/import), GDP and unemployment rate due to the lower frequencies available. The main data sources were Bloomberg Financial Data Services, the Bank Indonesia (www.bi.go.id), Directorate General of Budget Financing and Risk Management –Indonesia’s Ministry of Finance (https://www. djppr.kemenkeu.go.id) and Indonesia Stock Exchange (www.idx.co.id). The fact that Indonesia has the world’s 16th largest economy and the largest economy in Southeast Asia is the main reason why we selected Indonesia as the focal economy in this study country. Furthermore, Indonesia implements a free-floating exchange rate and free capital flow regime, and in 2030, according to McKinsey (2021), it is expected to become the seventh largest economy in the world. 3.3.2 Methodology. For the empirical investigation, the NARDL approach developed by Shin et al. (2014) will be used [1]. The NARDL approach offers several advantages: in contrast 93.65% 4.55% 0.80% 0.73% 0.15% 0.12% USD other EUR JPY GBP AUD Source(s): Figure has been created by the authors using publicly available data for Indonesia’s Foreign Exchange Turnover by Currency as of April 20191, sourced from BIS Triennial Survey 2019 Figure 5. Indonesia’s foreign exchange turnover by currency as of April 2019 The effect of capital market dynamics 89 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/83/9525927/ajeb-12-2022-0157.pdf by ZBW German National Library of Economics user on 16 December 2025 to the normal VAR technique, which can lose information contained in connections between series levels, the NARDL approach can show variances in the regressors’responses to positive and negative shocks from the asymmetric dynamic multipliers (Jare~ no et al., 2019 and Allen and McAleer, 2021); we are able to test simultaneously the long and short-run asymmetric over the negative and positive partial sum decompositions of the regressors (Jare~ no et al., 2019) because the NARDL approach lacks residual association, and this model is unsusceptible to the omission of lag bias (Arize et al., 2017). To ensure that the requirements to conduct non-linear ARDL methodologies are fulfilled, the series were tested for unit roots to determine the level of integration (see Jare~ no et al., 2020: Baek and Choi, 2021). The generic form of the regression equation to examine the asymmetric link between Indonesian bond yield and equity market price with the exchange rate is expressed as follows: EXCt¼β0þβ1BONtþβ2JCItþ ε t(1) where EXC t is the US$/IDR currency rate, BON t is the 10-year Indonesian government bond yield, JCI t is the Indonesian Stock Exchange market price, β 0 is the constant term, tis the time index (trading day), β 1 and β 2 are the slope coefficients and ε t is the error term. It should also be stressed that we have taken the natural logarithm all variables used in this study and hence reflecting relative changes. The explicit model that considers long-run asymmetries is expressed in the following terms: Yt¼βþXþþβ−X−þ ε t t t(2) where Y t indicates dependent variable, βþand β – are the long-run parameters to be evaluated, whereas ε t is the error term and X t þ and X t  are the partial sums of the vectors of positive and negative changes of independent variables. Equation (2) can be reformulated to an asymmetric long-run regression equation (3) as follows: EXCt¼β0þβ1BONPOStþβ2BON:tþβ3JCIPOS tþβ4JCI:tþ ε t(3) where EXC t denotes the US$/IDR exchange rate, β 0 ,β 1 ,β 2, β 3 and β 4 are coefficient of longrun parameters to be estimated and ε t represents the error term. Moreover, BON_POS t and BON_NEG t are the partial sum of positive and negative changes in the bond yield, whereas JCI_POS t and JCI_NEG t are the partial sum of positive and negative changes in the stock price, and the values are formulated as follows: BONPOSt¼X t j¼1 ΔlnBON þ¼X t j¼1 max j(4) BON:t¼X t j¼1 ΔlnBON −¼X t j¼1 min j(5) JCIPOSt¼X t j¼1 ΔlnJCI þ¼X t j¼1 max j(6) AJEB 8,1 90 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/83/9525927/ajeb-12-2022-0157.pdf by ZBW German National Library of Economics user on 16 December 2025 sum decompositions of the independent variable(s). Potentially, due to presences of asymmetric impact, the usual ARDL may not able to capture this whilst the bounds test may show absence of cointegration. As such we have opted for the NARDL approach to capture possible asymmetries in the interaction between capital market asset price and exchange rate movements both in the short and the long run. 2. 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AJEB 8,1 98 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/83/9525927/ajeb-12-2022-0157.pdf by ZBW German National Library of Economics user on 16 December 2025 Appendix Corresponding author Constantinos Alexiou 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] EXC BON JCI LN_EXC LN_BON LN_JCI Full Sample: 1/02/2012 to 12/31/2021 Number of observations 2,610 2,610 2,610 2,610 2,610 2,610 Mean 12,900 7.22 5,312 9.45 1.97 8.57 Median 13,384 7.21 5,247 9.50 1.97 8.57 Maximum 16,575 9.83 6,723 9.72 2.29 8.81 Minimum 8,935 5.05 3,655 9.10 1.62 8.20 Std. Dev 1,735 0.98 769 0.15 0.14 0.15 Sub Sample: 1/01/2020 to 12/31/2021 Number of observations 523 523 523 523 523 523 Mean 14,419 6.66 5,731 9.58 1.89 8.65 Median 14,343 6.55 5,986 9.57 1.88 8.70 Maximum 16,575 8.38 6,723 9.72 2.13 8.81 Minimum 13,583 5.89 3,938 9.52 1.77 8.28 Std. Dev 488 0.54 653 0.03 0.08 0.12 Source(s): Authors’calculations Full-sample: 1/02/2012 12/31/2021 Sub-sample: 1/01/2020 12/31/2021 LN_BON LN_EXC LN_JCI LN_BON LN_EXC LN_JCI LN_BON 1 LN_BON 1 LN_EXC 0.443*** 1 LN_EXC 0.683*** 1 LN_JCI 0.033** 0.690*** 1 LN_JCI 0.846*** 0.682*** 1 Note(s): *** 51% level of significance, ** 55% level of significance, * 510% level of significance Source(s): Authors’calculations Table A1. Descriptive statistics Table A2. Correlation matrix The effect of capital market dynamics 99 Downloaded from http://www.emerald.com/ajeb/article-pdf/8/1/83/9525927/ajeb-12-2022-0157.pdf by ZBW German National Library of Economics user on 16 December 2025