Optimal Lag Length Research Articles

Impact of COVID-19 vaccinations in India: a state-wise analysis

BackgroundEver since the emergence of COVID-19 and its consequent spread across continents, engulfing both advanced and developing nations, COVID-19 vaccine was considered to be the main weapon to curb the spread of the virus. The COVID-19 vaccination program in India started after the first wave of infections (March – December 2020) had almost subsided.ObjectiveIn this work, the objective is to perform a state-wise analysis to assess the impact of vaccination in slowing down the spread of infections during the second COVID-19 wave (February – October 2021) in India. The prediction accuracy of the proposed model with the optimal lag length (in days) after including the impact of vaccination is evaluated and compared with a model without it. A total of 21 states are chosen for the analysis encompassing 97% of the Indian population.MethodsWe use the generalized Gompertz curve to study the COVID-19 outbreak. The generalized Gompertz model is then further modified to study the impact of vaccination to slow down the spread of COVID-19. The modified model considers the cumulative proportion of individuals having the first COVID-19 vaccine shot in each state as the explanatory variable.ResultsBy incorporating the impact of vaccination in the Generalized Gompertz Curves, it is seen that the visible impact of the first dose of the vaccination is observed after a lag of 20 days with 16 out of the 21 states showing the impact of vaccines in curbing the spread of COVID-19. However, in states like Telangana, West Bengal, Tamil Nadu, Rajasthan, and Kerala, we do not conclusively observe the impact of vaccination during the study period.ConclusionsUsing only COVID-19 infection cases and the vaccination data in the proposed model, we conclude that overall, the vaccination program effectively curbed the spread of COVID-19 in India.

Analisis Tren Ekonomi Digital terhadap Kebijakan Moneter di Indonesia

This study aims to determine the effect of fintech, the amount of money in circulation, interest rates and economic growth on the analysis of digital economic trends on monetary policy in Indonesia. There are four variables in this study, namely fintech, the amount of money in circulation, interest rates and economic growth. The analysis method used is Vector Autoregression with the Impluse Response Function test or abbreviated as IRF and the Forecast Error Variance Decomposition test commonly abbreviated as FEVD, stationarity test, cointegration test, lag structure stability test and optimal lag length test. There is a contribution from each variable to the variable itself and other variables, according to the results of the Vector Autoregression study with a lag basis of 2. In addition, the results of the Vector Autoregression analysis show that the past variable (t-1) contributes to the current variable both to the variable itself and to other variables. The results of the analysis show that there is a reciprocal relationship between the variables. By using response function analysis, we can see if there is a response from other variables to changes in one variable in the short, medium, or long term. In addition, we know that the stability of all variables is formed in the short, medium, and long term. According to the Variance Decomposition Analysis, factors such as Fintech and Money Supply contribute the most to the variable itself. However, other variables that have the greatest influence on the variable itself and are supported by other variables in the short, medium, and long term are economic growth and interest rates are most influenced by Fintech.

Analysis of Price Transmission Chili at the Level from Producer to Consumer in Tapin Regency

The fluctuating prices of chili peppers can significantly impact farmers positively and negatively. Farmers cultivating bird’s eye chili can reap substantial profits with high prices. However, they earn less when bird’s eye chili prices are low. Currently, market integration refers to the evolution of costs across different market sectors and the extent to which market sectors in various geographical locations are informed about the supply and demand occurring in a market. This study aims to: 1) analyze the price developments of bird’s eye chili at the producer, wholesale, and consumer levels in Tapin Regency; 2) examine the price transmission of bird’s eye chili from the producer level to the consumer level through prices at the wholesale level and the production of bird’s eye chili in Tapin Regency. The data used in this research are secondary in the form of monthly time series data. The study utilized the documentation method, with data collected from January 2017 to December 2022, totaling 72 data points. The data analysis method employed is the Vector Error Correction Model (VECM), with tests including the Stationarity Test, Optimal Lag Length Test, Stability Test, Cointegration Test, VECM Estimation, Impulse Response Function (IRF), and Forecast Error Variance Decomposition (FEVD). The findings indicate that the prices of bird’s eye chili exhibit monthly fluctuations. The higher price fluctuations at the producer level indicate that farmers have weak bargaining power in setting the prices of bird’s eye chili. The stationarity test shows that the prices of bird’s eye chili at the consumer (HK), wholesale (HG), and producer (HP) levels, along with bird’s eye chili production (PC), are stationary at the first difference. The optimal lag test, with the smallest Akaike Information Criterion (AIC), is lag 2. The cointegration test shows that the trace statistic and max-eigen values are greater than the critical value, thus accepting H1, indicating a long-term relationship. In the short term, the increase in bird’s eye chili prices is influenced by the two previous periods. The analysis results demonstrate asymmetric price transmission from the producer level to the consumer price level.

Prepivoted Augmented Dickey-Fuller Test with Bootstrap-Assisted Lag Length Selection

We investigate the application of prepivoting in conjunction with lag length selection to correct the size and power performance of the Augmented Dickey-Fuller test for a unit root. The bootstrap methodology used to perform the prepivoting is a residual based AR bootstrap that ensures that bootstrap replicate time series are created under the null irrespective of whether the originally observed series obeys the null hypothesis or not. Simulation studies wherein we examine the performance of our proposed method are given; we evaluate our method’s performance on ARMA(1,1) models with varying configurations for size and power performance. We also propose a novel data dependent lag selection technique that uses bootstrap data under the null to select an optimal lag length; the performance of our method is compared to existing lag length selection criteria.

Devaluation and stock prices

Aim: The study evaluated the causal link between exchange rate devaluation and stock prices in African stock markets within the African region, namely, Zambia, Nigeria, Uganda, Tunisia, Tanzania, Botswana, Indonesia, Egypt, South Africa, and Malaysia. Research method: The Toda-Yamamoto (T-Y) Granger causality methodology which entails a determination of maximum order of integration, determination of optimal lag length, and finally conducting the T-Y causality test based on augmented var k + dmax was used in the study. We also deployed the ARDL model to estimate the effects of devaluation on stock prices in Africa. To accommodate issues of endogeneity, we further implemented dynamic panel model estimation to unravel the true effects of exchange rate devaluation on stock prices. Findings: Currency devaluation and the consumer prices negatively impacted stock prices by 3.6% and 3.2% respectively in the short term period following a 1% rise in devaluation and consumer prices. In the long-term period, a 1% rise in currency devaluation and consumer price level stimulated 1.45% and 0.68% reductions in stock prices respectively in ten developing African countries. The lending interest rate also significantly and positively impacted stock market prices over the long-term period by 0.25% following a 1% rise in lending rate, whereas, in the short-term period, a 1% rise in lending rate stimulated 0.38% decline in stock prices of the ten African countries. Originality: in this research, we executed the Toda-Yamamoto granger causality methodology in explaining the causal relation between exchange rate devaluation and stock prices in ten developing African countries and with the ARDL model, the study estimated the effects of devaluation on stock prices in Africa. Contributions: The research is a contribution to a remarkable long-run connection linking stock market price, exchange rate and price index. Though there was a remarkable long-term association linking them, the outcome also showed an essential short-run relationship linking stock market price, consumer price index and interest rate. Limitations: This research is limited to ten countries within the African region. This choice of countries was made on basis of the available data. Hence, data could be sought on additional developing countries and estimation done for a larger sample of countries to obtain results that can be used to generalize the causality between devaluation and stock price movements.

Autoregressive Vector (VAR) Method in Analyzing Inflation Persistence in Indonesia

One of the most important economic variables is inflation. It is known that some changes in each major variable in the economy are caused by the rate of inflation, such as unemployment and future economic conditions. This is what makes inflation a variable that is often observed and tested, both theoretically and empirically. Economic development is said to run well if supported by stable inflation values, which will later serve to make the population more prosperous in the future. This research is a quantitative research using secondary data, namely inflation and CPI data from January 2018 to December 2022. Data obtained from Bank Indonesia and the Indonesian Central Bureau of Statistics. This study aims to see persistence in Indonesia and see the effect of CPI on inflation both in the long and short term. Where the model used in this study is using the VAR model of differential levels, because the data is not stationary at the level level. Determination of the optimal lag length is obtained from the lowest Akaike Information Criterion (AIC) value. The results of the analysis show that the persistence of inflation in Indonesia is fairly high. This is because the lag value in inflation is close to 1 or even more, which is 6.71. The high persistence of inflation is expected to cause shocks that affect the inflation rate in Indonesia. Then the length of time it takes for inflation to return to the equilibrium position is about 1 year and a half or 18 months. Furthermore, for the effect of inflation on CPI, in this case it has an influence in the long run, while for the short term influence is only influenced by its own variables.

The effect of monetary policy shocks on industrial output in Afghanistan

Purpose- This study examines the impact of monetary policy shocks on industrial output in Afghanistan. Quarterly secondary data were collected for the period from 2003 to 2021 from various official sources such as the Statistical Bulletin of the Central Bank of Afghanistan, the International Monetary Fund, and the World Bank. Methodology- This study used a three-stage procedure. The first stage involved testing the stability of the variables to be included in the model. The second stage involves selecting the optimal lag length using various lag length criteria. Finally, the Vector Error Correction (VECM) model was used to determine if there were any short-run correlations or dynamics among the variables. The study also conducted some post-tests to confirm the validity and robustness of the regression model. Findings- The results of the long-run vector error correction model show that there is a long-run causality running from monetary policy rate, broad money supply, inflation, exchange rate, and a commercial bank loan to the industrial sector. So, there was a speed of adjustment from the short-run to the long-run equilibrium. However, the Wald test confirmed that the short-run causality runs from the explanatory variables to the dependent variable. Commercial bank credit to the industrial sector was found to cause a change in industrial production in all six lag periods. The results were further supported by the Granger causality test. Shocks in commercial bank lending to the industrial sector were found to have no significant effect on industrial output. However, the performance of the industrial sector was the main cause of the flow of commercial banks' credit to the industrial sector. Conclusion- The study recommends that monetary policy should proceed with extreme caution in managing the exchange rate. The study also recommends that the Central Bank of Afghanistan should encourage commercial banks to offer credit to the industrial sector at low-interest rates. Keywords: Monetary policy shocks, industrial output, Vector Error Correction Model (VECM), Granger Causality Test, Afghanistan. JEL Codes: E52, E32, L60

Carbon dioxide emissions and the economic growth: competitiveness and economic development view

PurposeThis study aims to shed light on the experience of the United Arab Emirates (UAE) in balancing three main pillars: the environmental criteria, the reduction of CO2 emissions and the economic growth. Based on the environmental Kuznets curve (EKC) framework, it will assess the causal relationship between economic indicators such as gross domestic product (GDP) per capita, trade openness and energy use and environmental indicators such as CO2 emissions.Design/methodology/approachThe analysis relies on a period of 40 years (1981–2020) where data is extracted from the World Bank database. This study uses the unit root test for time series stationarity, the optimal lag length test, the “Johansen” test for co-integration and the vector error correction model.FindingsThe paper concludes to two major findings. On a short-term basis, CO2 emissions and economic indicators are negatively correlated, whereas on a long-term basis, there is no association between CO2 emissions and economic indicators in the UAE.Research limitations/implicationsThe research ends with important recommendations. It illustrates the importance of rationalizing the use of primary resources and the necessity to embrace successful and efficient policies in the energy production.Practical implicationsMore specifically, UAE is urged to address the problem of CO2 emissions in the electricity sector and increase awareness of the use of environmentally friendly processes in the transport and industrial sectors. While setting their economic agendas, UAE are encouraged to meet environmental criteria and invest in renewable energy projects such as “Shams 1”, the largest solar power plant outside of Spain and the USA.Originality/valueThe current study is significant in its research on the environmental impact of economic development, trade openness and energy use policies in the UAE. It uses CO2 emissions as an environmental proxy and evaluates the environmental policies adopted in the UAE to reduce its impact.

Assessing Economic Growth Quality

Purpose: The aim of this study is to examine the quality of economic growth according to the stages of its generation and manifestation. Theoretical framework: The use of quantitative estimation when exploring economic growth can lead to misinterpretation or inaccurate judgments, since the economic reality implies not only quantitative, but also qualitative components. The goal of economic growth, its planning and statement are not an end in themselves, they are aimed to improve the welfare of society. Its implementation implies an improvement in the qualitative facet of economic growth. To date, attempts to assess the quality of economic growth are mostly based on its social outcomes. With such an approach, we miss the formation process of economic growth, focusing only on its effects. Design/methodology/approach: There are developed special indices for both economic growth generation quality and economic growth effects quality, based on macroeconomic and social indicators that play a significant role in the relevant process. These together, as subindices, form an economic growth quality index. The latter is calculated for 76 countries during 2005-2020. The following techniques and indicators are used in the process of the index calculation - normalization, percentiling, optimal lag length selection informational criteria, cointegration tests. Findings: The research empirically confirms that the economic growth quality in the world manifests itself more steadily than it is being formed. Between the leaders and the countries in the last positions in index rankings, the drastic difference mainly lies in their productivity, expenditures on education and lending to the private sector through domestic credit. Research, Practical & Social implications: The nature of the proposed economic growth assessment can serve as a baseline for further research studies in this context. On the other hand, the findings of this study can be used to analize the drivers and barriers to growth in each of the observed country. Originality/value: The approach according to which the economic growth quality index is constructed gives an opportunity to observe economic growth not as a static indicator but as a breathing organism, describing the development process with detailed growth causes and consequences.

Relationship between greenfield investment and economic growth: Evidence from Bangladesh

The key purpose of the study is to investigate the relationship between Greenfield investment and economic growth of Bangladesh using annual time series data during the period 2003-2020. The study employs Toda-Yamamoto (T-Y) tests of Granger causality method that performs Modified Wald Test (MWALD) in order to establish causal relation among different variables. There are three steps in implementing the T-Y procedure. The first step involves using different tests (ADF, PP, and KPSS test) to identify the maximum order of integration of the variable. The second step requires selecting the optimal lag length (p) based on several lag length selection criteria. In the third step, MWALD approach is used for testing the vector auto regression model for causality. The results of the tests (ADF, PP, and KPSS) concluded that the maximum order of integration of the variables is two. Then, the optimal lag length of two (p=2) has been selected based on several lag length selection criteria. Finally, the findings reveal the evidence of unidirectional causality from Real Greenfield Foreign Direct Investment (RGFDI) to Real Gross Domestic Product (RGDP). The key contribution of this study is to investigate the Greenfield investments-growth relationship for a country like Bangladesh.

Examining the Effect of Financial Markets Shocks on Financial Stability in South Africa

The paper analyzed the impact of financial market shocks on financial market stability. The goal was achieved by employing quarterly time-series data spanning from 2003:Q1 to 2020:Q4. The study used various econometric techniques such as stationarity, determining optimal lag length, cointegration analysis, estimating a vector error correction model, impulse response functions and forecast error variance decomposition. Following this, the long run relationship amongst the variables was established. The findings revealed that inflation has a negative impact on financial stability in both the short and long run. Lastly, it was only the shocks in economic activities that was found to have a significant impact on financial stability.

PENERAPAN VECTOR AUTOREGRESSIVE (VAR) DALAM MEMPREDIKSI RETURN SAHAM DI INDONESIA

The rate of return (return) and risk are inseparable in investing activities. One equilibrium model that describes the relationship between return and risk assumes that the expected return is influenced by more than one macroeconomic factor. Furthermore, the causal relationship between stock returns and macroeconomic factor returns was analyzed using VAR. The application of VAR in this study is to predict stock returns through the stages of checking data stationarity, determining the optimal lag length, testing Granger causality between variables, estimating VAR model parameters and Portmanteau diagnostic tests, and predicting stock returns. The results show that the VAR (1) model is the most appropriate model to describe the relationship between stock returns and macroeconomic factor returns with a significant model owned by BBCA, ICBP, INTP, KLBF, and SMGR stocks. Furthermore, the VAR (1) model is used to predict the five stock returns. The prediction results show that INTP's stock returns are negative while the returns of the other four stocks are positive. This shows that INTP shares experienced a capital loss, while the stock returns of BBCA, ICBP, KLBF, and SMGR experienced capital gains

Inflation and Growth Nexus: An Estimate of the Threshold Level of Inflation in Sierra Leone

This study investigates the relationship between inflation and economic growth, and determines the threshold level of inflation in Sierra Leone, using a non-linear model (quadratic function) with time-series data from 1980 to 2020. The study employs the Ordinary Least Squares estimation technique within the framework of Hendry’s General-to-Specific approach to obtain a parsimonious result. The results of the unit root test confirm that all the variables are stationary in the first difference, i.e. they are integrated of order one or I(1), with an optimal lag length of two (2). The findings confirm the presence of a non-linear relationship in the inflation-growth nexus. The results reveal that the threshold level of inflation (optimal level of inflation) favourable for economic growth is 10.3 per cent. Thus, inflation is growth-enhancing in Sierra Leone when it is below 10.3 per cent, (i.e. ≤ 10.3 per cent). However, inflation above the threshold is detrimental to economic growth. The results also indicate that economic growth is largely influenced by investment, openness and dummy variable for war, with statistically significant coefficients. Specifically, the results confirm that both openness and investment have a positive impact on economic growth, whilst war dummy has a negative effect on economic growth. The results suggest that the regressors explain 86% of the variation in economic growth and the F-stat reveals that the regressors are jointly significant. The policy implication of this study is that the central bank should implement a prudent monetary policy aimed at maintaining inflation below the threshold level of 10.3 per cent. In this regard, the authorities should follow a rule-based approach to monetary policy implementation rather than discretion in order to contain inflation at the threshold level.

Suisse Stock Return, Macro Factors, and Efficient Market Hypothesis: Evidence From ARDL Model

This study investigates the long-run equilibrium relationship between Suisse stock market (SSM) prices and a set of macroeconomic variables (inflation, interest rate, and exchange rate) using Monthly data for the period 1999:1 to 2018:4. Different specifications and tests will be carried out, namely unit root tests (ADF and PP), Vector Auto Regression (VAR) to select the optimal lag length and for Granger causality and Toda and Yamamoto (TY) Wald non causality testing, VEC Model and (Johansen, 1988)’s test for no cointegration, and ARDL framework and FPSS test of no cointegration hypothesis. Once ECM representation of the ARDL model is used, it confirms temporal causality between (inflation, interest rate, exchange rate) and the stock price. Finding say that there is dynamic short-run adjustment and long-run stable equilibrium relationship between considered macroeconomic variables (except exchange rate) and the stock prices in the SSM. This imply that the SSM is informationally inefficient because publicly available information on macroeconomic variables (inflation and interest rate) can be potentially used in predicting Suisse stock prices.

Determinant Analysis of Company Debt Policy with Vector Error Correction Model Approach

Purpose- This study uses a VECM model that aims to see the short-run and long-term effects of managerial ownership, sales growth, free cash flow, and asset structure against debt policy. Vector Error Correction Model (VECM) is a model that can be used for time series data that is not stationary but has a cointegration relationship where in the model included stationary exogenous variables as additional regressors.
 Research Method- The sample used in this research is quantitative data with a purposive sampling technique. Based on the criteria, the number of samples collected is 32 samples in the period 2013-2021.The data analysis techniques in this study use Vector Error Correlation Model (VECM) analysis, several stages that researchers must go through before determining the right model, namely data stationarity test, optimal lag length test, co-integration test, VAR model stability test, granger causality analysis, VAR/VECM empirical model, Impulse Response Function analysis and Variance Decomposition analysis.
 Findings- The results of the analysis show that in the short term only sales growth and asset structure have a significant influence on debt policy. Meanwhile, in the long-term free cash flow, asset structure and sales growth have a significant influence on debt policy, while managerial ownership has an insignificant effect on debt policy.
 Implication- For the company should reduce the proportion of funding from debt in the implementation of its operations so as to reduce financial distress, because funding from corporate debt causes financial distress and agency costs greater than tax savings from debt interest expense, as a result of which the company is very vulnerable to economic turmoil. For creditors who provide sources of debt funding, pay more attention to aspects of the company's asset structure to be used as collateral for debt, because the company usually uses loan funds for high-risk projects. For investors should take deeper considerations to invest in companies that have large free cash flow because companies that have large free cash flow tend to show good cash flow for the future.

US oil supply shocks and economies of oil-exporting African countries: A GVAR-Oil Resource Analysis

This study examined the effects of US Shale-oil supply which started in 2013. The study employed GVAR-Oil resource model from the first quarter of 1980 to the fourth quarter of 2018 for 36 trading countries with special focus on 4 oil-exporting African countries. Given that shale supply is a recent phenomenon, we estimated two models to capture these effects. The first model spanned from 1980 to 2013, while the second model was from 1980 to 2018. After implementing the preliminary tests of optimal lag length selection, cointegration test and the weak exogeneity test, we conducted the persistence profile to find Egypt and Gabon adjust to US oil supply shocks faster than Algeria while Nigeria did not. The result of the generalized impulse response functions showed negative effects of the US shale oil supply in Egypt, positive effects in Nigeria and Gabon but insignificant in Algeria. The generalized forecast variance error decomposition function conducted found output of China, Japan, US, Europe, Asia and Latin America respectively to contribute the largest variations in oil supplies during the study period. African countries, on the other hand, were found to contribute in small proportion. We recommended that robust oil savings funds by oil-exporting African countries can act as buffers against oil supply shocks. • The study examines the US shale oil revolution shock on oil-exporting African countries. • Convergence to equilibrium obtained for Gabon and Egypt, slower for Algeria and no convergence for Nigeria. • Effects of US shale oil supply is negative in Egypt, positive in Nigeria and Gabon but insignificant in Algeria. . • China, US, Euro, Asia and Latin America contribute most to variations in oil supplies and African oil-exporting insignificant.

Exploring the Dynamics of Global Plate Motion Based on the Granger Causality Test

The geodynamic mechanism is the research focus and core issue of plate motions and plate tectonics. Analyzing the time series of earthquakes may help us understand the relationship between two plate boundaries and further explore movement mechanisms. Therefore, this paper uses earthquake event data and the Granger causality test method to quantitatively analyze the interaction and energy transfer relationship of plate boundaries from the viewpoint of statistics. The paper aims to explore the relationship between the pull effect and the push effect of plate motion and to provide knowledge to explore seismic energy transfer relationships, and even to predict earthquakes: (1) The directions of the global plate motion field are opposite to the directions of Granger causality between plate boundaries of the Pacific, Nazca, African, Australian, Eurasian, and Philippine plates. (2) The slab-pull force (not limited to the subduction force of the ocean plates) provides a main driving force for plate motions in the Pacific plate, Nazca plate, African plate, Australian plate, Eurasian plate, and Philippine sea plate. (3) The causality relationship and optimal lag length of energy release between plate boundaries may provide another view to forecasting earthquakes.

Finite Sample Lag Adjusted Critical Values of the ADF-GLS Test

Ng and Perron (Econometrica 69:1519–1554, 2001) demonstrated the merits to employing their Modified Akaike Information Criterion to select the optimal lag length in the Elliott, Rothenberg and Stock (Econometrica 64:813–836, 1996) unit rot test. Perron and Qu (Econ Lett 84:12–19, 2007) introduced an empirical method that resolved an associated power problem for non-local alternatives. While Cheung and Lai (Oxford Bull Econ Stat 57:411-419, 1995) contains response surface estimates to generate finite-sample, lag-adjusted critical five and ten percent values for use in applied work, these relate to the original Elliott et al. (Econometrica 64:813–836, 1996) test. This paper provides response surfaces estimates of critical values for both the Ng and Perron (Econometrica 69:1519–1554, 2001) and Perron and Qu (Econ Lett 84:12–19, 2007) approaches, demonstrating they are sometimes quite different, an important consideration when performing inference.

Public finance in Jordan: a lesson from Covid-19

Purpose - COVID-19 is a human and economic tragedy. It has become obvious that the virus pressed hard public finances all over the world. However, countries with relatively limited fiscal space face will experience more pressure. Within this context, this paper has two objectives. First, to outline the status of public finance in Jordan, and in particular, the weight that foreign grants carry in public revenues. Second, to examine the impact of foreign grants on the tax effort in Jordan. In other words, the objective is to examine whether or not public finance in Jordan suffers from the “aid curse”. Methodology - The period 1984 – 2019 forms the data for the statistical analysis. In addition, and to examine the impact of foreign aid on tax effort, the paper uses time series analysis techniques including stationarity tests, optimal lag length criteria, co-integration, long-run relationship, and variance decomposition analysis. Findings - Based on the empirical results, Jordan does suffer from the curse of aid. Grants do have a negative and significant impact of tax revenues (tax effort). Conclusion - Relevant stakeholders in Jordan should use COVID-19 as a “trigger” point for change in the country’s fiscal mobilization process. This is the only way to reduce the growing public debt, and reduce the country’s reliance on foreign grants.

Borsa Birleşmelerinin Endeksler Üzerine Etkisi: Nasdaq Omx Örneği (The Impact of Stock Market Integration on Indices: Nasdaq Omx Case)

Purpose – The aim of this study is to explore the relationship between the main indices of Northern European stock exchanges within Nasdaq OMX. The relationship between the stock exchanges of different countries operating under the same roof will guide investors to control portfolio risk. Design/methodology/approach – For this purpose, daily market closing data between 05.11.2013-10.10.2018 was used as data set in the study. The main indices of the 8 different countries, NordicALL40 and Baltic10 group indices belonging to the Nasdaq group are listed in the analysis. In the study, Lee Strazicich unit root test, which takes into account the structural breaks of the series, was applied. The optimal lag length was then determined and then the Toda-Yamamoto Model was applied to determine if there was any causality between the variables and the direction of the relationship. Findings – As a result, there is a causal relationship from the Baltic10 index to the NordicALL40 index. The Oslo20 index is also influenced by both NordicALL40 and Baltic10 indices. Discussion – Located in the same region of the stock market in fact interact with each other, it was one of the findings of this study. Whether this is the case for other stock exchanges in a similar situation can be revealed with new researches to be conducted.