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Interrelationship dynamics between stock markets of nation under debt crisis and its major trading partners: evidence from Sri Lankan crisis


A series of crises triggered over a decade may bring global recession, which may impact millions of investors, including countries teetering on the brink due to forex reserve shortages; this study addresses the significant financial event of a small nation declaring bankruptcy. Such events can have adverse consequences on the global economy, particularly affecting the stock market indices of the country’s trading partners. Our research investigates the impact of small nation bankruptcies on the stock market indices of connected importing and exporting partners. Focusing on the recent political and economic crisis in Sri Lanka, we analyze interactions between the Sri Lankan stock exchange and its key trading partners. Employing pairwise cointegration and the vector auto-regressive model-based Granger causal approach, our findings reveal cointegration among the stock markets in Germany, Italy, and Sri Lanka. Notably, the pre-crisis causal links between the Colombo Stock Exchange and other stock markets have dissolved. These insights hold valuable implications for understanding and preparing for similar circumstances in other South Asian economies grappling with forex shortages and rising inflation.

Graphical abstract


Sri Lanka experienced its worst-ever economic and political crisis in 2022. Beginning in March, the nation’s slide toward chaos accelerated, displacing high-ranking officials and political figures accused of carrying out the disastrous economic policies that are the core of the country’s issue [1]. The political casualties’ extended power outages, long lines, and shortages of necessities failed to console people [2].

Can this happen to other South Asian Economies? Probably yes, as per the [3], South Asia is expected to slow down in 2023 [4]. Therefore, this study becomes essential not only for Sri Lanka but to all the economies that are facing forex shortages and poorly beaten by high inflation. From the theoretical perspective, we look at how the stock market of a country under crisis and the stock markets of its major trading partners interact.

The interaction between economies has increased post-globalization and is still increasing in many small economies. In recent decades, globalization has resulted in the eternal links between the domestic stock market and cross-country trade and financial flows [5, 6]. The extensive economic liberalization process strengthened the linkage between the real economy and the fluctuating dynamics of international financial markets [7, 8]. However, economic globalization opens two fronts in global financial markets: possibilities (economic growth) and problems (transmission of crises). Although the interconnection of the world has provided some opportunities for investigators of global portfolio management, it is impossible to completely rule out the immediate effect of cross-country risks [9].

Globalization investment management in stock markets has benefited from the growing economic linkages between economies, yet financial crises may move from one economy to another in days or even hours [9]. While this capital flows through globalization have been linked with solid growth rates in certain emerging nations, several countries have seen periodic collapses in growth rates and significant financial crises throughout the same time. These concerns have exacted a heavy macroeconomic and social cost burden [10].

One example is macroeconomic forces influencing financial markets and their growth [11]. For instance, regional organizations like the Association of Southeast Asian Nations (ASEAN) and the Asian Development Bank (ADB) have to keep an eye on how closely the stock markets in Asia move in tandem so that macroprudential measures may be developed to protect financial stability in the face of potential shocks (in transferring these shocks co-movement, causal relationship). In Asia, emerging and frontier countries (Pakistan, Bangladesh, etc.) are standing just at the doorstep as facing recession problems due to over debt. As per the recent IMF report, one-third of the global economy will face recession or contraction with shrinking real income and rising prices [12]. So, exploring the contemporary crises in frontier countries, especially in Sri Lanka, is vital so that some relative startles could be framed for alarming recession.

For better understanding, the rest of the study is divided into five subsections: In “Literature review” section covers the literature review, Sect. 3 covers research objectives and questions, and the comprehensive methodology is covered in “Data and methodology” section. In “Results and discussion” section, empirical results are covered. Finally, the conclusion of the study is enclosed in “Conclusion” section.

Literature review

Bulgaria was plagued by a severe financial crisis in 1996–1997 that started with a banking crisis and transacted into a currency crisis [13]. Nagayasu [14] evidenced a significant impact on the stock markets as a transmission channel during the currency turmoil. The terrible economic and financial crisis in Thailand and its neighboring East Asian nations began on July 2, 1997, when a de facto $ peg of the Thai baht collapsed [15]. The depreciation rate for most of the currencies in the region from the end of June 1997 exceeded 50 percent in January 1998, when the crisis was at its peak. In several crisis-stricken emerging nations, a dramatic stock market loss enhanced the risk of an extreme currency devaluation on the same day. For currency markets, there is evidence of excessive spillover within areas but limited impact outside the region. Severe occurrences in stock markets are far more interconnected globally, especially in the USA [16]. Such rigorous transmission of crises over the global stock markets takes the form of contagion.

Economic contagion is the spread of financial or economic shocks from shocked countries (economies or regions) to stable countries (economies or regions) through international business (trade) and finance (financial networks) [17,18,19]. When many financial indexes move together because of some external effect, this sharp movement across borders has been named a contagion. This concept of contagion states that a crisis in one nation amplifies the probability of a problem in another country beyond the interdependence between these countries in non-crisis periods [20]. The subprime crisis (2007) and other financial shocks significantly influenced fundamental and financial factors [21]. Between “contagion” and “common shocks,” the literature makes a distinction [22,23,24,25]. When a crisis is dubbed contagion, there should be a robust transmission of shocks. However, cross-market interdependence may be called ‘shocks,’ which may be temporary. Several patterns of worldwide spillover of economic shocks are discussed in the literature. Masson [22] stated three stages of contagion: (1) financial contagion through “monsoonal effects” becomes contagious on the correlated underlying macroeconomic variables, (2) transmission of contagion through “spillovers” (via external links as trade), (3) “pure contagion” which causes a bull (favorable) market to bear (adverse) market. The first two situations (monsoonal impacts and spillovers) demonstrate interdependence. Crises caused by interdependence may be predicted using macroeconomic factors. Suppose the interdependence between countries is observed during non-crisis times. In that case, the impact of a financial crisis in one nation on the risk of a crisis in another country may be assessed. The third situation, equilibrium jumps, is what we refer to in this study as contagion: a more unexpected, stronger correlation during crisis periods than regular times.

The relationship between contagion and interdependence is significant. When markets have a more considerable correlation during crises, investors should modify their portfolios appropriately since diversification across markets may be less effective than expected if based on correlations in quiet times. Similarly, governmental responses to a crisis will be determined by the perceived pattern of shock transmission throughout financial markets. Policy intervention may be helpful if the source of a crisis is a random leap between equilibria, i.e., contagion. In contrast, if a crisis extends to other markets due to associated fundamentals, policymakers are less inclined to be able to avert the crisis from spreading. In a continual effort to boost economies, central banks, governmental organizations, and international organizations have intervened in the financial market [26, 27]. In a persistent effort to boost economies, central banks, governmental organizations, and international organizations have intervened in the financial market. Policymakers tasked with preserving financial stability are worried about whether shocks are communicated differentially across equities markets in unfavorable versus normal conditions.

Most influencing studies on Sri Lanka’s prior crises with other recent crises and variables

In the late 1960s, Sri Lanka’s purchasing power parity (PPP) per capita income was much higher than in Thailand and South Korea. In contrast, it was slightly lower than Malaysia’s [28]. Wijeweera et al. [29] stated that from 1952 to 2002, despite the free trade policy launched in 1977. Moreover, from 1983 to 2009, the Sri Lankan economy was affected a lot; almost 100,000 people were killed in the civil war [30].

Sri Lanka does not have a debt overhang policy as total foreign indebtedness was not too high. Sri Lanka—the Tamil separatist war results indicated that fundamental conflicts in Sri Lanka’s national development strategy and restricted trade system were at the core of the country’s dual political strife [31]. Kelegama [32] stated that during 2007–2009, different terrorist attacks shook the industries of Sri Lanka. Nevertheless, the Sri Lankan economy grew by an astonishing 7.2 percent in 2012. It exhibited booming growth after the conclusion of a 30-year civil war in May 2009 and advanced to a higher sustainable development path. All-important sectors of the economy performed admirably in 2012, supported by a stable domestic environment, greater investor confidence, improving macroeconomic circumstances, and the global economy’s slow recovery from one of the darkest periods of historical recession (Central Bank of Sri Lanka, 2012). According to research by AkXram [33], foreign public debt has aided the process of economic development in Sri Lanka; nevertheless, debt payment has a negative link with per capita GDP and investment. External debt played a critical part in developing the country’s civil war; debt payment remains a severe worry in Sri Lanka. Domestic debt has a significant and positive link with per capita GDP [34,35,36]. Furthermore, COVID-19 had a substantial impact on the global stock market [37,38,39,40] and the behavior of sustainable business [41, 42, 42,43,44] and Colombo stock exchange, which reported a record low in mid-march 2020 [45, 46].

Sri Lanka’s external sector performance has decreased because of lower export revenues, tourist receipts, and remittances, and the Sri Lanka rupee has drastically fallen versus major foreign currencies. Li et al. [42] also indicated that the economic center is critical to the region’s economic and social growth.

Moreover, in further testing, it is of utmost importance to consider the distinct attributes associated with each location, including factors such as a country’s level of accessibility to the capital market, the extent to which research and development expenditures are tax-deductible, and pertinent regulatory considerations [47].

In the Russia–Ukraine war, Ukrainian firm has shown negative return as the Ukrainian boundary touch with European markets; these markets (European) intend to react negatively as significant abnormal negative returns as increased political uncertainty and geographic proximity [48, 49] as stock market impacted by the Russia–Ukraine [50,51,52], Which make changes in the behavior of the stock market [31] due to changes in the investor’s sentiments [39]. Such crises further impact the corporations for disinvestments [53]. However, the impact of this war created a significant adverse effect on the Asian and European regions [54]. Ganegodage and Rambaldi [55] stated that the Sri Lankan war significantly affected GDP. In Sri Lanka, there is no evidence to suggest that political ties boost a company’s value. The Sri Lankan government does not favor politically affiliated companies when awarding large contracts [56].

Politics may negatively influence economic ties, which can hurt fiscal linkage. The degree of risk aversion of investors may be increased by political crises as well. Foreign investments (particularly in developing nations, where political risk is higher) might be seen as riskier, and investors’ funds could be pulled out of these areas due to investors’ attention [39]. Since less money flows into such markets, this may induce a crisis and reduce their degree of global integration [57]. Arguably, a decline in share demand and market fragmentation would result from increased risk premia. A positive influence on stock market integration from political crises is feasible if contagion drives the impact of crises on financial integration. When the reasons for both positive and negative links appear credible, it is impossible to foresee which one will prevail; the subject must instead be decided experimentally [58]. A political crisis may cause awful news to spread swiftly to other markets, creating contagion effects that may exacerbate the co-movement of stock markets [39, 59, 60].

Current status of Sri Lankan political and economic crisis.

On April 3, 2022, a political crisis started in Sri Lanka for power between the Gotbaya Rajpaksha (Sri Lankan president) and the parliament of Sri Lanka [61]. It is made worse by the economic crisis in the country, which has caused people to protest against the government. Different parts of Sri Lanka are against the government, which has led to political instability that the nation has not seen since the civil war. The Chamber of Young Lankan Entrepreneurs (COYLE) also issued a call to action. It warned that if the ministry did not handle the present economic and political crises, it might lead to the liquidation of firms. A former World Bank official, Shanta Devarajan, cautioned that Sri Lanka’s main threat is societal upheaval and volatility. He said a money transfer program might be implemented to prevent the economy from collapsing and assist people in need. To avoid economic collapse, it also suggested reducing subsidies on food and gasoline [62]. On May 9, 2022, amid widespread anti-government movements, Prime Minister Mahinda Rajapaksa handed in his resignation letter. Ranil Wickremesinghe was sworn in on May 12, 2022, as prime minister. Still, on July 9, crowds put homes stormed and burned, which resulted in the reign of Wickremesinghe (prime minister) and Gotabaya (president). On July 15, Wickremesinghe retook an oath as acting president as Wickremesinghe flew to the Maldives and said not to discharge duty as out of Sri Lanka. Dinesh Gunawardena took oath on July 22 after the secret ballot of July 20, 2022 [61].

Sri Lankan government (on April 12) declared a unilateral debt standstill by suspending its foreign debt servicing with the limitation of payments to Multilateral Development Banks (MDBs) [63]. As a burst in inflation, rates paralyzed the Sri Lankan economy (Fig. 1), increasing daily, which severely impacted the economy of Sri Lanka. However, in August 2022, the IMF came up with some bailout packages to restructure Sri Lankan foreign debt (i.e., $51 billion).

Fig. 1
figure 1

Source: Created by Authors using Data from the Central Bank of Sri Lanka

Sri Lankan monthly inflation rate for 2022 (till June).

Sri Lanka’s Colombo stock market (consisting of 295 companies representing 20 Global Industry Classification System (GICS) with a $5489 billion market cap in 2021) has also been influenced by different domestic and international crises [34,35,36]. So, it is essential to review the impact of crises on risk movements transfer in the trading stock market; no theoretical or empirical literature on Sri Lankan political turmoil (2022). A former World Bank official’ and United Nations (UN) experts’ deleterious statements related to economic collapse (as foreign reserves dried up with a $51 billion loan, financial experts stated the country didn’t have enough currency to import), historically breaking inflation (high inflation rate with consistent growth (Fig. 1) with record high inflation (54.6%)), power outages, and debilitating fuel shortages have all contributed to Sri Lanka’s economic downfall. At the ground level, the UN reported that people face challenges with medicines, food, and fuel [64]. Since January 2020, their foreign currency reserve has decreased by 70 percent ($779 in December 2021). The asymmetric effect has been found in Sri Lankan stock returns [65,66,67].

In general, the prior studies have not investigated the emergence of economic contagion from the spark of Sri Lanka’s political turbulence (2022) and worsening financial crisis impacts and interdependency among most trading economies (focusing on top export and import in volume).

Theoretical description

Stock market reaction theory

The stock market reaction theory indicates an unbiased reaction of stock prices to public information. The property of “unbiased reaction” to public information, which formed the basis of efficiency, was seen to be an implication of rational, maximizing investor behavior in competitive securities markets [68]. Reduced to a basic level, the reasoning was that any systematically biased reaction to public information is costless and publicly observable and thus provides pure profit opportunities to compete away. Characterizing the market in terms of its response to information is only one of many feasible ways of modeling stock price behavior. Still, it introduced economic theory to the empirical study of stock prices, which had received little serious attention from economists prior to that point. Despite the subsequent spate of anomalies, the early efficiency literature not only adapted the standard economic theory to provide the first formal economic insights into how stock prices behave, but it helped pave the way for an outpour of theoretical and empirical work on stock markets and capital markets in general.

Moreover, this theory indicated that (a) the efficient market hypothesis was audacious and included a departure from the previous comparative ignorance of stock market behavior; (b) “efficiency” is an implication of rational information use in a competitive market; (c) a priori, stock markets are paradigm examples of competition; (d) “efficiency” is one way of modeling competitive behavior in stock markets and, like all models, it has strengths, and (e) there are binding limitations in our model. (f) Similarly, the wide range of strange evidence discovered highlights the limitations of “efficiency” as a construct and our knowledge of asset pricing; (g) much of the strange evidence occurs where research designs are most sensitive to limitations in our understanding of asset pricing; and (h) the anomalous evidence provides an intriguing set of puzzles for researchers to solve (i) because of the priors stated in (c) above, anticipate that many of the anomalies will be resolved in favor of efficiency; however, (j) because of the low cost of accessing and processing large data files. The limitations in the theory of efficient markets indicate an inherently imperfect understanding of asset pricing and the likelihood that genuine exploit pricing errors occur in any market.

The findings of this study are aligned with the above theory as the Sri Lankan crises acted as unbiased stock market reactions that reacted with the Colombo stock market. The results are designed to aid investors by providing a foundation for analyzing the company’s future with consideration in the investment decision-making process.

Objective of the study

Restrictions on domestic migration and fiscal policy may have a positive impact on the economy. There could be several factors that can also have a strong positive association between political risk (Internal or external instability), health (COVID-19), war (Russia–Ukraine war), investors’ attention, and financial market uncertainty significantly may demonstrate a strong correlation with a notable decrease (increase) in an (other) stock market performance may act as the spillover transmitter or receivers [6, 39, 69,70,71,72]. Thus, it becomes essential to unveil the relationship as the interconnectedness among the economy of the Sri lankan economy due to financial and political instability with its trading partner stock markets as variables.

As, the study seeks to address the following research objective, which is operationalized by the research questions in the form of RQ1 and RQ2.

RO: To analyze the causal relationship between the Colombo Stock Exchange (CSE) and the stock markets of the top five -export and -import partner countries during the Sri Lankan political crisis.

RQ1: Is there any significant co-movement existing between CSE and the stock markets of the top five -export and -import partner countries of Sri Lanka? If yes, then how would convergence take place after the Sri Lankan political crisis?

RQ2: Is there any significant causal relationship exist between the CSE and the stock markets of the top five -export and -import partner countries? If yes, then did it change during the crisis period as compared to the pre-crisis period?

Data and methodology

The secondary data has been extracted from Bloomberg (source of data) in the form of daily closing price from 01/01/2017 to 20/07/2022 for trading partners of Sri Lanka (Table 1). To understand the cointegration dynamics between the markets, the study uses Johansen–Juselius’s [73] cointegration test along with the Vector Error Correction Model (VECM). The notion of cointegration was first presented in the field of econometrics by Granger [74] and subsequently expanded upon and formalized by Engle and Granger [75]. The underlying principle of this approach is rooted in the notion that economic time series have nonstationary characteristics. However, by using a suitable linear combination of trending variables, it is possible to eliminate the shared trend component. The linear combination of the time series variables will provide a stationary result, indicating that the relevant time series variables are cointegrated. Cointegration is a topic of interest for economists due to its potential implications for the presence of a long-term or stable equilibrium connection.

The study of cointegration tests has progressed in two primary avenues: (a) the utilization of residuals from a cointegration regression proposed by Engle and Granger [75] for testing and (b) the application of vector auto-regressive models in a system of equations, as suggested by Johansen [76], Johamen and Jtiselius [77]. Cointegration is a statistical methodology used to ascertain the enduring association between time series data. -Within the framework of this research, the use of cointegration serves to facilitate comprehension of the extent to which the stock markets of several nations exhibit long-term synchronicity. This study has used a “pairwise” approach in their investigation of cointegration, whereby they have systematically examined pairs of nations to determine the presence of cointegration in their respective stock markets.

This approach is comprehensive, hence minimizing the possibility of disregarding any prospective long-term connections. The VAR-based Granger Causal Approach is a methodology used to analyze causal relationships between variables. The idea of Granger causality is a statistical tool used to ascertain the predictive relationship between two-time series. The use of a vector auto-regression (VAR) framework enables researchers to comprehensively analyze the Granger causality, hence facilitating an understanding of the dynamic interconnections between stock markets and the identification of the market that serves as the Granger causal factor for the other. The significance of this analysis is in its ability to ascertain the presence of a causal link between the stock markets of the respective nations, particularly in the aftermath of a notable event such as the declaration of bankruptcy. As per the objective of the study, the existence of cointegration among the stock markets in Germany, Italy, and Sri Lanka indicates a long-term equilibrium link among these markets.

This implies that over an extended period, these markets tend to exhibit parallel movements, indicating a state of dependency. The rationale for using the numerous studies [78, 79] as the VECM lies in its ability to use a complete information maximum likelihood estimation model. This model allows the testing of cointegration within a comprehensive system of equations, all in a single step, without necessitating the normalization of any individual variable. This approach enables us to prevent the propagation of mistakes from the first step to the subsequent one, which would occur if Engle–Granger’s technique were used. Additionally, it has the benefit of not necessitating any a priori assumptions on the endogeneity or exogeneity of the variables.

Vector auto-regression-based Granger causality test [80] implemented to compare the causal relationship between the pre-crisis (01/01/2017 to 02/04/2022) and during crisis (03/04/2022 to 20/07/2022) periods (Fig. 2). In this study, stock market indices selected as variables as the viability of stock market has the potential to affect a diverse array of stakeholders, including ordinary retail investors, big institutional investors, businesses, and governments alike. Gaining comprehension of the stock market’s response to a crisis helps facilitate the preparedness and effective response of these relevant parties. Stock markets serve as a reflection of not just concrete economic indicators but also the subjective emotions of investors. Fluctuations in stock prices and trading volumes have the potential to serve as indicators of alterations in investor confidence, assuming a critical role in both the immediate aftermath and subsequent stages of a crisis. The interconnectedness of international commerce and finance implies that a crisis occurring in one nation might result in consequential repercussions for other countries. The examination of stock markets, particularly in nations that have substantial trade connections with the crisis-affected country (as in the Sri Lankan crises), is of paramount importance due to the potential manifestation of these ripple effects. However, in different periods, other sector studies used other relevant methods, such as [81, 82] used quantile regression to study FDI, renewable energy, [83] used cross-sectional auto-regression distributive lag model, and other variables. In taxation (Table 1).

Fig. 2
figure 2

Source: Created by authors

Chronological event timeline of the Sri Lankan political crisis.

Table 1 Trading Matrix of most significant trading partners of Sri Lanka (in $) for 2021.

Results and discussion

Table 2 shows the results of Augmented Dickey–Fuller (ADF) [84] and Philips–Perron (PP) [85] unit root tests at levels and first difference. Both tests confirm the non-stationarity issue in sample data sets.

Table 2 Results of unit root test

Evidence does not accept the hypothesis that the selected ten stock index closing prices of different currencies have a unit root. The results convey that the data have mean and variance reverting behavior and changes with time. All variables were identified as stationary at the first order of integration I (1). Alternatively, we may say they were I(0) in the first difference. The mean remains constant across all variables. The first difference helps in stabilizing the mean once the seasonality and trend in the data series are taken care of. The stabilized mean helps avoid spurious analysis and draws realistic observations.

After determining that the variables under analysis were stationary at the first difference, we proceeded to test the cointegration between the variables. First, the CSE_P (Colombo Stock Exchange closing price) was analyzed with the other nine index prices of different countries. Then, we followed three steps to understand the cointegration between the nine pairs of variables, as depicted in Table 3.

Table 3 Results of pairwise cointegration test

In the first step, the selected pair was graphically checked for the presence of any sign of co-movement. The results of which are depicted in column 4 of Table 3. In the second step, the selected variables were directly checked for cointegration by selecting variables and checking for the cointegration test in Eviews 12. The conclusion (step 3) regarding the cointegration between the variables was determined by the procedure suggested by the Johansen-Juselius test. Then, the appropriate vector auto-regression (VAR) model is reached using the proposed five lag length criteria, including Likelihood Ratio (LR), Final Prediction Error (FPE), Akaike information criterion (AIC), Schwarz Criterion (SC), and Hannan–Quinn information criterion (HQC). Finally, the cointegration test is performed after reaching the final VAR model using lag length criteria to assess better the possibility of cointegration between the variables under consideration.

The results (Fig. 3) show that CSE_P is individually (Pairwise) and collectively (Sri Lanka–Germany–Italy–China) correlated with DAX_P, FTSEMIB_P, and SSE_P, respectively. Table 4 shows the results of the cointegration test performed on the VAR model with the above four variables after being log transformed collectively.

Fig. 3
figure 3

Source: Created by Authors

Correlation among the CSE, DAX, and FTSE.

Table 4 Results of the cointegration test based on the VAR framework

The cointegration test can be represented in the VAR framework as follows:

$$X_{t} = A_{0} + \mathop \sum \limits_{j = 1}^{p} B_{j} X_{t - j} + e_{t}$$

where all variables are nX1 vectors except B (nXn matrix), Xt comprises variables at the first difference, A0 includes constants, p indicates the maximum lag length, B represents the coefficient, and et is the error term.

The cointegration test airs for any long-term relationships between China, Germany, Italy, and Sri Lankan stock markets indices. However, the results of the stationarity test must show stationarity, and all variables must be integrated in the same order for the analysis to be valid. Since cointegrated variables do not eventually drift apart, a long-run relation can be established. Table 4 clearly shows the presence of cointegration with the rejection of the Null hypothesis.

The impact of divergence and convergence mechanisms in the cointegrated variables is studied by applying the vector error correction model (VECM). The same set of variables, when estimated using the VECM, shed light on the error correction mechanism.

The cointegration equation was found to be:

$$\begin{aligned} E_{t} & = - 12.7707 + 1\left( {{\text{Ln}}_{{{\text{cse}}_{p} }} } \right) + 3.9178\left( {{\text{Ln}}_{{{\text{dax}}_{p} }} } \right) \\ & \quad - 0.5643\left( {{\text{Ln}} - {\text{ftsemib}}_{p} } \right) - 1.2262\left( {{\text{Ln}}_{{{\text{sse}}_{p} }} } \right) \\ \end{aligned}$$

The results are shown in Table 5. The CSE_P, DAX_P, FTSEMIB_P, and SSE_P are cointegrated with each other. As per the data from 1/01/2017 to 08/08/2022, the variables might diverge in the short run but eventually converge, given the ordinary course of operations in the stock market. The impact of the Sri Lankan political crisis on the Colombo stock exchange creates a short-term divergence among the cointegrated markets of Sri Lanka, Germany, Italy, and China. As per the results of VECM, Sri Lanka, Germany, and Italy would converge by 0.44 percent, 0.42 percent, and 0.39 percent, respectively. This convergence speed is relatively slow, but collectively, they would achieve it relatively fast.

Table 5 Results of VECM among Sri Lanka, Germany, Italy, and China

Apart from checking the cointegration, we also compared the causality of stock indices of different key import and export countries for Sri Lanka. The causality was also performed pairwise using the VAR model for better interpretation of the causality, given that VAR follows the appropriate lag length criteria (LR, FPE, AIC, SC, and HQ) to reach a stable model. The causality results for the pre-crisis and post-crisis periods are compared in Table 6.

Table 6 Causality results during the pre-crisis and post-crisis

The Df column depicts the number of lags chosen for the 9 Pairwise VAR models as per the lag length suggested by different lag length criteria discussed above. The results show that all the unidirectional and bidirectional causal relationships do not exist in the crisis period. Only FTSEMIB_P (Italy) granger causes CSE_P (Sri Lanka). There could be many reasons for such a market behavior of the Colombo stock exchange, which may include the following but may not be limited to a crash in the stock market (already 25 percent market got wiped out on the date of the political crisis in our study, i.e., 03/04/2022) high inflation in Sri Lanka, Drastic fall in Sri Lanka Rupee to US$ (1 US$ = 202.88 on March 3, 2022, and was 1US$ = 359.93 on August 12, 2022).

The findings reveal the presence of cointegration among the stock markets of Sri Lanka, Germany, Italy, and China. The results are consistent with the study of [86], which also indicated among emerging economies China (weak integration) and India (no integration) as this study has a similar pattern of the causal relationships in the pre-crisis period between CSE and other stock markets disappeared in the crisis period. Overall, this study suggests that small country bankruptcy can have a significant negative impact on the stock market indices of importing and exporting partners. These findings indicate the importance of monitoring small countries’ economic conditions and taking appropriate measures to mitigate the spillover effects of their bankruptcy.


This study has examined the cointegration and causal relationship between Sri lanka and its top five imports and five export countries with reference to the Sri lankan crises. It was found that the Colombo Stock Exchange Index was cointegrated with the Financial Times Stock Exchange (FTSE)-Milano Indice di Borsa (MIB) or FTSEMIB (Italy) and Deutscher Aktien Index (DAX) (Germany) Index (Meeting RQ1). In the long term, the Sri Lankan stock market can converge (0.44 percent) as per the movement of the German and Italian stock markets, i.e., 0.42 percent and 0.39 percent per day. Given the situation, after the crisis reverts to normal, the Sri Lankan stock market would adjust to the pre-crisis period in 80 Days [100/(0.44 + 0.42 + 0.39)]. Even if we assume the adjustments to be twice as slow, the convergence could happen in 160 days (approximately five months).

Also, the VAR-based Granger causality results infer that all the unidirectional and bidirectional causal relationships between Stock exchanges of dominant import and export partner countries and the Colombo Stock Exchange do not hold in case of such an enormous economic and financial turmoil (deficient levels of forex reserves) (Meeting RQ2). Therefore, all the economies on the verge of bankruptcy, struggling with low forex reserves, and dealing with extreme inflation (especially after COVID-19 and the Russia-Ukraine crisis) can use the above results by incorporating these findings while drafting their fiscal and monetary policy.

The literature’s core argument for financial contagion considers changes in shareholder psychology, attitude,’ and ‘behavior.’ Real linkages (such as trade) and financial linkages (including investor behavior) play critical roles in understanding financial contagion, and their importance varies from instance to case. Strong domestic economic foundations may determine an economy’s ability to withstand global shocks. In this regard, fiscal, monetary, and trade policies should reduce financial fragility, decreasing needless debt commitments. In addition, financial standards should be strengthened in a synchronized approach at the international level.

Policy recommendation and future agenda

Various issues were hampering effective mangrove management, such as inefficient communication, inconsistencies between policies, and insufficient financial capacity of government stakeholders responsible for policy implementation. Similarly, in the situation of the political and economic crises of Sri Lanka produce spirals of market uncertainty, which in turn undermines investor confidence and promotes market volatility. This discovery was made feasible since the present economic crisis is having a substantial detrimental impact. Furthermore, the study emphasizes empirical evidence of the importance of financial crises, as indicated by Italy and Germany as highly cointegrated economies, which can act as hedging of the risk with other economies like India, followed by China as least cointegrated due to the instability in the Sri Lankan economy. As a result, government regulators and politicians must defend investors’ interests, maybe by ensuring that enterprises have access to prospects for greater liquidity and profitability. Given the urgency of the situation, the fundamental goal of government policy should be to progressively revive the travel and tourism, manufacturing, construction, and service sectors.

As a consequence, investors will have a more optimistic perspective on the company’s future profitability, which will lessen market volatility and pave the way for economies to expand more steadily. The current situation of Sri Lankan recession and inflation can act as a formulation of the framework across the globe; the study would help small economies or countries get insights to formulate their fiscal and monetary policy better. It is high time that all countries, especially ones that are facing shortages in forex reserves, analyze the Sri Lankan political crisis and its impact on its stock market. This proactive approach to analyzing a country with bankruptcy would help many countries, like Bangladesh, Pakistan, Bhutan, etc., to formulate better strategies to counter inflation, recession, and the problem of low forex reserves, which are generally coupled together.

The state of politics, additional socioeconomic, population demographic, political, and policy elements in the analysis and stability have a significant impact on the stock market. Effective policy choices, elections, and political unpredictability may cause market volatility. Since political changes may affect industries, policies, and investment sentiment, investors keep a careful eye on them. We address the limitation of the study to the stock market indices with the period of the Sri Lankan crises period only, as designed to check the impact of the crises on the top trading partners’ stock markets of Sri Lanka. The primary constraint of this research is that it focused on trading partners of Sri Lanka only.

As in the future, recent crises, such as the silicon valley bank (SVB) crash and the Russia–Ukraine war crash, can be further tested by expanding to the South Asian stock markets. Moreover, a future study can be conducted to investigate the impact of the Sri Lankan crises on different macro-variables or exchange rates, which could help in framing the currency board or may have relevance to IMF or domestic government in the context of monetary (to reconstruct the debt of Sri Lankan) policy or fiscal policy.

Limitations of the study

In the context of the limitation, this study is limited to the benchmark indices of the stock market only. This study is limited to the recent (2022) political and economic crises in Sri Lanka. Policies and the economy might change over time as potential changes in Sri Lanka’s economic circumstances or the state of the global economy that took place after the study period may not have been captured by this study. The study’s emphasis on Sri Lanka’s current political and economic crises may restrict the findings’ applicability to other small countries or other geopolitical circumstances. Not every small country approaching bankruptcy will have the same set of conditions as Sri Lanka. The results could be impacted by certain political or economic circumstances depending on the period selected, which could not apply in other contexts. Although analyzing the Sri Lankan crisis has yielded insightful information, care should be used when applying these conclusions to other economies in South Asia. Financial dynamics are influenced by a variety of distinct economic, political, and social factors; thus, what is valid for one country may not always be true for another. According to the Granger Causal Approach, there are a number of external factors that can make it challenging to demonstrate actual causation in financial markets, even while statistical techniques such as Granger causality can uncover correlations.

Availability of data and materials

The data used in this study are secondary and are sourced from the Bloomberg database.



Colombo Stock Exchange


The Chamber of Young Lankan Entrepreneurs


Global Industry Classification System


Multilateral Development Banks


Purchasing Power Parity


United Nations


Vector Auto-Regression


Vector Error Correction Mechanism


Silicon Valley Bank crash


Financial Times Stock Exchange-Milano Indice di Borsa


Deutscher Aktien Index


Likelihood Ratio


Final Prediction Error


Akaike Information Criterion


Schwarz Criterion


Hannan–Quinn information Criterion


  1. Dushni Weerakoon I (2022) Sri Lanka’s hard road to recovery from economic and political crisis|East Asia Forum.

  2. Perera J (2023) Sri Lanka in 2022: a country wracked by multiple crises—South Asian voices.

  3. World Bank (2022) A world bank group flagship report finance for an equitable recovery.

  4. Ren J, Xie ZL (2022) A mixed bag for South Asian economies: challenges and opportunities ahead.,broad%20structural%20and%20fiscal%20reforms

  5. Mishkin FS (2007) Is financial globalization beneficial? J Money. Credit Bank 39:259–294.

    Article  Google Scholar 

  6. Caporale GM, Gil-Alana LA, You K (2022) Stock market linkages between the Asean countries, China and the US: a fractional integration/cointegration approach. Emerg Mark Financ Trade 58:1502–1514.

    Article  Google Scholar 

  7. Nogués J, Grandes M (2001) Country risk: Economic policy, contagion effect or political noise? J Appl Econ 4:125–162.

    Article  Google Scholar 

  8. Athukorala P (2016) Sri Lanka’s post-civil war development challenge: learning from the past. Contemp South Asia 24:19–35.

    Article  Google Scholar 

  9. Xu G, Gao W (2019) Financial risk contagion in stock markets: causality and measurement aspects. Sustain 11:1402.

    Article  Google Scholar 

  10. Prasad ES, Rogoff K, Wei S-J, Kose MA (2013) Financial globalization, growth, and volatility in developing countries.

  11. Badullahewage SU (2018) The effects of macroeconomic factors on the performance of stock market in Sri Lanka. Int J Innov Econ Dev 3:33–41.

    Article  Google Scholar 

  12. Gourinchas PO, Ray DW, Vayanos D (2016) A preferred-habitat model of term premia, exchange rates, and monetary policy spillovers, pp 1–23.

  13. Berlemann M, Hristov K, Nenovsky N (2005) Lending of last resort, moral hazard and twin crises: lessons from the bulgarian financial crisis 1996/1997. SSRN Electron J.

    Article  Google Scholar 

  14. Nagayasu J (2001) Currency crisis and contagion: evidence from exchange rates and sectoral stock indices of the Philippines and Thailand. J Asian Econ 12:529–546.

    Article  Google Scholar 

  15. Khalid AM, Kawai M (2003) Was financial market contagion the source of economic crisis in Asia?: Evidence using a multivariate VAR model. J Asian Econ 14:131–156.

    Article  Google Scholar 

  16. Cumperayot P, Keijzer T, Kouwenberg R (2006) Linkages between extreme stock market and currency returns. J Int Money Financ 25:528–550.

    Article  Google Scholar 

  17. Goldstein M (1996) Origins of the crisis. Trade Currencies Financ 375–96.

  18. Forbes KJ, Chinn MD (2004) A decomposition of global linkages in financial markets over time. Rev Econ Stat 86:705–722.

    Article  Google Scholar 

  19. Glick R, Rose AK (1999) Contagion and trade: Why are currency crises regional? J Int Money Financ 18:603–617.

    Article  Google Scholar 

  20. Forbes K, Rigobon R (2001) Measuring contagion: conceptual and empirical issues BT—international financial contagion. In: Claessens S, Forbes KJ (eds). Springer, Boston, pp 43–66.

  21. Tong H, Wei S-J (2011) Real effects of the 2007–08 financial crisis around the world. SSRN Electron J.

    Article  Google Scholar 

  22. Masson MPR (1998) Contagion: monsoonal effects, spillovers, and jumps between multiple equilibria. International Monetary Fund.

  23. Kaminsky GL, Reinhart CM (2000) On crises, contagion, and confusion. J Int Econ 51:145–168.

    Article  Google Scholar 

  24. Von Károlyi C, Winner E, Gray W, Sherman GF (2003) Dyslexia linked to talent: global visual-spatial ability. Brain Lang 85:427–431.

    Article  Google Scholar 

  25. Wang P, Zakeeruddin SM, Moser JE, Nazeeruddin MK, Sekiguchi T, Grätzel M (2003) A stable quasi-solid-state dye-sensitized solar cell with an amphiphilic ruthenium sensitizer and polymer gel electrolyte. Nat Mater 2:402–407

    Article  Google Scholar 

  26. Akhtaruzzaman M, Boubaker S, Sensoy A (2021) Financial contagion during COVID–19 crisis. Financ Res Lett.

    Article  Google Scholar 

  27. Fu S, Liu C, Wei X (2021) Contagion in global stock markets during the COVID-19 Crisis. Glob Chall 5:2000130.

    Article  Google Scholar 

  28. Athukoralge P, Rajapatirana S (2000) Liberalization and industrial transformation: Sri Lanka in international perspective

  29. Wijeweera A, Dollery B, Pathberiya P (2005) Economic growth and external debt servicing : a cointegration analysis of Sri Lanka, 1952 to 2002 by Albert Wijeweera, Brian Dollery, and Palitha Pathberiya, pp 1–20

  30. Bandara JS (1997) The impact of the civil war on tourism and the regional economy. South Asia J South Asia Stud 20:269–279.

    Article  Google Scholar 

  31. Abeyratne S (2004) Economic roots of political conflict: the case of Sri Lanka. World Econ 27:1295–1314.

    Article  Google Scholar 

  32. Kelegama S (2017) Tax policy in Sri Lanka: economic perspectives

  33. Akram N (2017) Role of public debt in economic growth of Sri Lanka: an ARDL approach. Pak J Appl Econ 27:189–212

    Google Scholar 

  34. Wongswan J (2006) Transmission of information across international equity markets. Rev Financ Stud 19:1157–1189.

    Article  Google Scholar 

  35. Goldfajn I (1998) Financial market contagion in the Asian crisis.

  36. Kaminsky GL, Reinhart CM (1999) The twin crises: the causes of banking and balance-of-payments problems. Am Econ Rev 89:473–500.

    Article  Google Scholar 

  37. Yu S, Abbas J, Draghici A, Negulescu OH, Ain NU (2022) Social media application as a new paradigm for business communication: the role of COVID-19 knowledge, social distancing, and preventive attitudes. Front Psychol 13:903082

    Article  Google Scholar 

  38. Kakran S, Sidhu A, Bajaj PK, Dagar V (2023) Novel evidence from APEC countries on stock market integration and volatility spillover: a diebold and yilmaz approach. Cogent Econ Financ 11:2254560.

    Article  Google Scholar 

  39. Lohan S, Sidhu A, Kakran S (2023) The impact of investor’s attention on global stock market: statistical review of literature. Int J Bus Forecast Mark Intell.

    Article  Google Scholar 

  40. Dabwor DT, Iorember PT, Yusuf Danjuma S (2022) Stock market returns, globalization and economic growth in Nigeria: evidence from volatility and cointegrating analyses. J Public Aff 22:e2393.

    Article  Google Scholar 

  41. Hanif MW, Hafeez S, Afridi MA (2023) Does wastophobia bring sustainability in consumers’ responsible behavior? A case of electricity waste management. Int J Energy Sect Manag 17:265–287.

    Article  Google Scholar 

  42. Li Y, Al-Sulaiti K, Dongling W, Abbas J, Al-Sulaiti I (2022) Tax avoidance culture and employees’ behavior affect sustainable business performance: the moderating role of corporate social responsibility. Front Environ Sci 10:1–14.

    Article  Google Scholar 

  43. Abbas J, Zhang Q, Hussain I, Akram S, Afaq A, Shad MA (2020) Sustainable innovation in small medium enterprises: the impact of knowledge management on organizational innovation through a mediation analysis by using SEM approach. Sustainability.

    Article  Google Scholar 

  44. Micah AE, Bhangdia K, Cogswell IE, Lasher D, Lidral-Porter B, Maddison ER et al (2023) Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026. Lancet Glob Heal 11:e385-413.

    Article  Google Scholar 

  45. Diyalagoda K (2021) Economic impacts of COVID-19 macro and microeconomics evidences from Sri Lanka. SSRN Electron J.

    Article  Google Scholar 

  46. Karunarathne ACID, Ranasinghe JPRC, Sammani UGO, Perera KJT (2021) Impact of the COVID-19 pandemic on tourism operations and resilience: stakeholders’ perspective in Sri Lanka. Worldw Hosp Tour Themes 13:369–382.

    Article  Google Scholar 

  47. Dejnirattisai W, Huo J, Zhou D, Zahradník J, Supasa P, Liu C et al (2022) SARS-CoV-2 omicron-B11529 leads to widespread escape from neutralizing antibody responses. Cell 185:467–484.

    Article  Google Scholar 

  48. Sohag K, Vasilyeva R, Urazbaeva A, Voytenkov V (2022) Stock market synchronization: the role of geopolitical risk. J Risk Financ Manag.

    Article  Google Scholar 

  49. Federle J, Müller GJ, Meier A, Sehn V (2022) Proximity to war: the stock market response to the Russian invasion of Ukraine.

  50. Kumari V, Kumar G, Pandey DK (2023) Are the European Union stock markets vulnerable to the Russia–Ukraine war? J Behav Exp Financ 37:100793.

    Article  Google Scholar 

  51. Pandey DK (2022) Advancing financial knowledge: exploring information dynamics, market reactions, and valuation theories—insights from the december 2022 issue. Int J Account Bus Financ 2:i–ii

    Article  Google Scholar 

  52. Dhiman B, Kumar R (2022) Spillover effects between Indo-China metal futures markets. Business Management/Biznes Upravlenie.

  53. Mandiratta P, Bhalla GS (2023) Analyzing the stock market performance of central public sector enterprises disinvested through public offering mode: Indian evidence. Benchmarking An Int J 30:407–432.

    Article  Google Scholar 

  54. Yousaf I, Patel R, Yarovaya L (2022) The reaction of G20+ stock markets to the Russia-Ukraine conflict “black-swan” event: evidence from event study approach. J Behav Exp Financ 35:100723.

    Article  Google Scholar 

  55. Ganegodage KR, Rambaldi AN (2014) Economic consequences of war: evidence from Sri Lanka. J Asian Econ 30:42–53.

    Article  Google Scholar 

  56. Berkman H, Galpoththage V (2016) Political connections and firm value: an analysis of listed firms in Sri Lanka. Pac Account Rev 28:92–106.

    Article  Google Scholar 

  57. Frijns B, Tourani-Rad A, Indriawan I (2012) Political crises and the stock market integration of emerging markets. J Bank Financ 36:644–653.

    Article  Google Scholar 

  58. Beine M, Cosma A, Vermeulen R (2010) The dark side of global integration: Increasing tail dependence. J Bank Financ 34:184–192.

    Article  Google Scholar 

  59. Kashyap S (2023) Review on volatility and return analysis including emerging developments: evidence from stock market empirics. J Model Manag 18:756–816.

    Article  Google Scholar 

  60. Krishnan D, Dagar V (2022) Exchange rate and stock markets during trade conflicts in the USA, China, and India. Glob J Emerg Mark Econ 14:185–203.

    Article  Google Scholar 

  61. Abeyagoonasekera A (2023) Sri Lanka’s political-economic crisis; corruption, abuse of power and economic crime. J Financ Crime.

    Article  Google Scholar 

  62. De Guzman C (2022) How organic farming worsened Sri Lanka’s economic and political crisis|Time

  63. Uditha Jayasinghe and Jorgelina Do Rosario (2022) Sri Lanka unilaterally suspends external debt payments, says it needs money for essentials|Reuters

  64. Hendel N (2022) United Nations International Children’s Emergency Fund (UNICEF) BT—International conflict and security law: a research handbook. In: Sayapin S, Atadjanov R, Kadam U, Kemp G, Zambrana-Tévar N, Quénivet N (eds) Asser Press, The Hague, T.M.C. pp 719–31.

  65. Heston SL, Ranjan Sinha N (2017) News vs. sentiment: predicting stock returns from news stories. Financ Anal J 73:67–83.

    Article  Google Scholar 

  66. Atmaz A, Basak S (2018) Belief dispersion in the stock market. J Finance 73:1225–1279.

    Article  Google Scholar 

  67. Hussain SM, Ben Omrane W (2021) The effect of US macroeconomic news announcements on the Canadian stock market: evidence using high-frequency data. Financ Res Lett 38:101450.

    Article  Google Scholar 

  68. Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Finance 25:383–417.

    Article  Google Scholar 

  69. Chowdhury EK, Khan II, Dhar BK. Catastrophic impact of Covid‐19 on the global stock markets and economic activities. Business and Society Review.

  70. Kakran S, Kumari V, Bajaj PK, Sidhu A (2024) Exploring crisis-driven return spillovers in APEC stock markets: a frequency dynamics analysis. J Econ Asymmetries 29:e00342.

    Article  Google Scholar 

  71. Kakran, S., Sidhu, A., Bajaj, P. K., & Dagar, V. (2023). Novel evidence from APEC countries on stock market integration and volatility spillover: A Diebold and Yilmaz approach. Cogent Economics & Finance, 11(2):2254560.

    Article  Google Scholar 

  72. Lohan S, Sidhu A, Journal SK-I (2023) Impact of investors’ attention on the global stock market: a bibliometric analysis. Indian J Finance

  73. Johansen S, Juselius K (1990) Maximum likelihood estimation and inference on cointegration—with applications to the demand for money. Oxf Bull Econ Stat 52:169–210.

    Article  Google Scholar 

  74. Granger CWJ (1981) Some properties of time series data and their use in econometric model specification. J Econom 16:121–130

    Article  Google Scholar 

  75. Engle RF, Granger CWJ (1987) Co-integration and error correction: representation, estimation, and testing. Econometrica 55:251–276.

    Article  Google Scholar 

  76. Johansen S (1991) Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models. Econometrica 59:1551–1580.

    Article  Google Scholar 

  77. Johamen S, Jtiselius K (1990) Maximum likelihood estimation and inference on cointegration-with applications to the demand for money. Oxf Bull Econ Stat 52:169–210

    Article  Google Scholar 

  78. Kismawadi ER (2023) Contribution of Islamic banks and macroeconomic variables to economic growth in developing countries: vector error correction model approach (VECM). J Islam Account Bus Res.

    Article  Google Scholar 

  79. Koondhar MA, Aziz N, Tan Z, Yang S, Raza Abbasi K, Kong R (2021) Green growth of cereal food production under the constraints of agricultural carbon emissions: a new insights from ARDL and VECM models. Sustain Energy Technol Assess 47:101452.

    Article  Google Scholar 

  80. Granger CWJ (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424–438.

    Article  Google Scholar 

  81. Abbas J, Wang L, Ben Belgacem S, Pawar PS, Najam H, Abbas J (2023) Investment in renewable energy and electricity output: Role of green finance, environmental tax, and geopolitical risk: empirical evidence from China. Energy 269:126683.

    Article  Google Scholar 

  82. Wang S, Abbas J, Al-Sulati KI, Shah SAR (2023) The impact of economic corridor and tourism on local community’s quality of life under one belt one road context. Eval Rev.

    Article  Google Scholar 

  83. Shah SAR, Zhang Q, Abbas J, Tang H, Al-Sulaiti KI (2023) Waste management, quality of life and natural resources utilization matter for renewable electricity generation: the main and moderate role of environmental policy. Util Policy 82:101584.

    Article  Google Scholar 

  84. Dickey D, Fuller WA (1981) Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49:1057–1072

    Article  Google Scholar 

  85. Phillips PCB, Perron P (1988) Testing for a unit root in time series regression. Biometrika 75:335–346.

    Article  Google Scholar 

  86. Ali S, Butt B, Rehman K (2011) Co-movement between emerging and developed stock markets: an investigation through cointegration analysis. World Appl Sci J 12:395–403

    Google Scholar 

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NS worked on conceptualization, methodology, supervision and writing original draft. SK managed the conceptualization, methodology, Project Administration, writing original draft. AS has played a crucial role in visualization, reviewing & editing, and AK has contributed by writing, conceptualization, reviewing & editing. All authors read and approved the final manuscript.

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Correspondence to Nishant Sapra.

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Kakran, S., Sapra, N., Kumar, A. et al. Interrelationship dynamics between stock markets of nation under debt crisis and its major trading partners: evidence from Sri Lankan crisis. Futur Bus J 10, 12 (2024).

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