2017
DOI: 10.1080/1331677x.2017.1340176
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Multivariate Granger causality between macro variables and KSE 100 index: evidence from Johansen cointegration and Toda & Yamamoto causality

Abstract: The pursue of this article is to scrutinise the long-haul relationship between stock returns of the KSE 100 index and monetary indicators such as rate of exchange, inflation, and interest rates. Month-to-month data from the KSE 100 index and monetary variables were extracted for the period January 1992 to November 2015. We transformed the data series into a stationary form by employing the augmented Dickey-Fuller method. The Johansen cointegration approach reinforces the long-haul association between equity pr… Show more

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Cited by 10 publications
(8 citation statements)
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“…The core concept behind cointegration is to see if there is long-run relationship between the movements of variables over a period of time. In presence of long-run interrelation between the variables implies that two variable's movement is mutually influenced by movement of each other's variable (Dwyer, 2015;Ahmed et al, 2017). In following example, the cointegration is used to determine if the money demand function's movements have mutual interrelation with the other macroeconomic indicators.…”
Section: Co-integrationmentioning
confidence: 99%
See 1 more Smart Citation
“…The core concept behind cointegration is to see if there is long-run relationship between the movements of variables over a period of time. In presence of long-run interrelation between the variables implies that two variable's movement is mutually influenced by movement of each other's variable (Dwyer, 2015;Ahmed et al, 2017). In following example, the cointegration is used to determine if the money demand function's movements have mutual interrelation with the other macroeconomic indicators.…”
Section: Co-integrationmentioning
confidence: 99%
“…Granger Causality is a statistical method to approach to determine causality between the variables of the time series even when there is unit root in the data. The core concept behind the Granger causality is to identify the patterns of relationship between the variables in the data, and then determine causality of the variables (Ahmed et al, 2017). The Granger Causality is somehow based on the cause-and-effect idea; but is not entirely related to this.…”
Section: Granger-causalitymentioning
confidence: 99%
“…This analysis allows us to investigate causation and direction of causality with an improved Wald approach (Toda & Yamamoto, 1995). This approach could be used regardless of the cointegration (Ahmed, Veinhardt, Streimikiene, & Fayyaz, 2017). We add in m additional lags of each of the variables into each of the equations [following Giles (2011)] and Granger non-causality test results (52 V.A.R.…”
Section: Toda and Yamamoto Wald Causality Technique -Finalise Var mentioning
confidence: 99%
“…Since PSX stages a vital role for the expansion of the country, researchers in the past have employed various methods to examine the impact of macroeconomic variables on Pakistan stock market (see e.g. Nishat & Shaheen, 2004;Sohail & Zakir, 2010;Shahbaz, 2013;Ahmed, Vveinhardt, Streimikiene, & Fayyaz, 2017). However, many studies have adopted network science theories to analyse a complex system such as stock market.…”
Section: Introductionmentioning
confidence: 99%