Browsing by Subject "Granger Causality"

Now showing 1 - 3 of 3

Open Access
Evaluation of linkages between equity indices : evidence from İstanbul Stock Exchange and Dow Jones
(2009) Ertan, Aytekin
This study investigates the linkage between the major stock market indices of Turkey (ISE National 100) and USA (Dow Jones Industrial Average). Main purpose of this research is to measure the interdependence and cointegration between these indices and figure out the significance and the direction of short run relationship, if there exists any. Cointegration analyses based on Johansen Method demonstrated that there is not any cointegrating vector between these indices, refuting an integrated long term relationship. On the other hand -in this case of no cointegration- Granger Causality studies on the first differenced VAR model pointed out a significant unidirectional effect of Dow Jones to Istanbul Stock Exchange in the short run; which would enable feasible forecasts of ISE via index data from the US. These findings could be valuable to investors holding long and short term investment portfolios in ISE and/or in Dow Jones.
Open Access
Inter-regional connectivity in the human brain during visual search
(2016-08) Dar, Salman UI Hassan
Separate groups of regions in the human brain are thought to be functionally specialized for representing specific categories of visual objects, and for controlling and deployments of visual attention. It is commonly assumed that the information ow between these regions is altered during visual search. However, little is known about the magnitude and extent of these changes during natural visual search. Here, we assess the changes in functional connectivity between the attention-control network and the category-selective regions during category-based visual search in natural movies. Brain activity was recorded using functional magnetic resonance imaging (fMRI) while subjects viewed natural movies. To investigate the changes in connectivity strength between pairs of brain regions, we employed coherence analysis. Coherence is a non-directional measure of association, which identifies correlation in frequency domain. To infer the in uence of attention-control areas on category-selective areas, Granger causality analysis was carried out. Granger causality uses the idea of temporal precedence that cause precedes the effect. Furthermore, to examine whether attention changes inter-regional connectivity after accounting for stimulus-driven brain activity, two separate encoding models were used to capture brain responses elicited by low-level structural and high-level category features in natural movies via L2-regularized linear regression. Response predictions of the structural and category models were removed from the recorded blood-oxygen-level dependent (BOLD) responses to obtain the residual responses. The connectivity analyses were repeated on the residuals to determine if the attentional changes in connectivity persist even after projecting out the stimulus-driven brain activity. The results indicate that performing visual search for a specific object category enhances the in uence of high-level attention-control network on category-selective areas in ventral temporal cortex. Furthermore, these connectivity patterns persist even after projecting out the stimulus-driven brain activity from the recorded BOLD responses.
Open Access
The relationship between stock price index and trading volume in the Istanbul Stock Exchange
(1995) Tokat, Fatma
In this study, the long-term relationship and the short-term causality between stock price index and the trading volume and the direction of the causality is investigated in the context of a small stock market, the Istanbul Stock Exchange in Türkiye by using cointegration theory and Vector Error Correction Model. The data used includes daily closing values of ISE composite index and daily aggregate number of share units traded for the period 29/02/1988-30/09/1994. The emprical results reveal evidence of strong linear impact from lagged stock prices to current and iliture trading volume, which can be explained by both non-tax-related trading models and noise trading models, whereas weak evidence of a linear impact from lagged volume to current and future stock prices, which can be explained by sequential information arrival models and the mixture of distributions model.