FIIs and Indian Stock Market: A Causality Investigation

While the volatility associated with portfolio capital flows is well known, there is also a concern that foreign institutional investors might introduce distortions in the host country markets due to the pressure on them to secure capital gains. In this context, present chapter attempts to find out the direction of causality between foreign institutional investors (FIIs) and performance of Indian stock market. To facilitate a better understanding of the causal linkage between FII flows and contemporaneous stock market returns (BSE National Index), a period of nineteen consecutive financial years ranging from January 1992 to December 2010 is selected. Granger Causality Test has been applied to test the direction of causality.Aczkolwiek brak stabilności związany z przepływami kapitału portfelowego jest dobrze znany, to istnieje również obawa, że zagraniczni inwestorzy instytucjonalni mogą wprowadzać zakłócenia na rynkach krajów przyjmujących z uwagi na wywieraną na nich presję, aby zapewniać zyski kapitałowe. W tym kontekście niniejszy rozdział próbuje poznać kierunek przyczynowości pomiędzy zagranicznymi inwestorami instytucjonalnymi (FIIs) i działaniem indyjskiej giełdy. Aby ułatwić lepsze zrozumienie związku przyczynowego między przepływami FII i mającymi miejsce w tym samym czasie wynikami giełdy papierów wartościowych (BSE National Index), wybrany został okres dziewiętnastu kolejnych lat począwszy od stycznia 1992 do grudnia 2010. Do zbadania kierunku przyczynowości zastosowano test przyczynowości Grangera

Property fund flows and returns

This study is concerned with the impacts on property returns from property fund flows, and with the possibility of a reverse transmission from property fund flows to property returns. In other words this study investigates whether property returns “cause” fund flow changes, or whether fund flow changes “cause” property returns, or causality works in both directions

EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI

Causal links between trade, foreign direct investment and economic growth for Bangladesh

This study investigates empirically the causal relationship between trade, foreign direct investment (FDI) and economic growth of Bangladesh for the period of 1973 to 2008. To analyze this Johansen cointegration test and Granger causality test are used. The cointegration analysis suggests that there is a long run equilibrium relationship among the variables. The results of Granger causality test identifies that there is a causal relationship among the mentioned variables. According to the study, economic growth of Bangladesh leads both FDI and export growth and there is a unidirectional causal relationship between FDI and export with direction from export to FDI.gross domestic product, foreign direct investment, export, Johansen cointegration test and Granger causality

ARE EXPORTS CAUSING GROWTH? EVIDENCE ON INTERNATIONAL TRADE EXPANSION IN CUBA, 1960-2004

Economic development in Cuban economy in the last 50 years has been involved in the so called socialist revolution time. In the external sector, the COMECON arrangements have determined its international specialization trade pattern and balance of payments position until 1989. When the Berlin Wall fell down, Cuban economy collapsed showing the malfunctions of the previous external regulated period. In this paper, we analyzed the role of exports as an engine of economic growth in Cuba considering essential events in its commercial policy-making in the long period from 1960 to 2004. Our results show that the export led growth (ELG) hypothesis is not an appealing phenomenon. Causality proofs on the basis of error correction and augmented level VAR modellings show the imperious necesssity to import for the Cuban development. The inclusion of imports not only evidences the weakness in the feedback and interrelation between economic growth and exports but also their expansion has been precisely causing growth in most of the considered periods.Cuba, Export-led Growth, commercial agreements effects, cointegration, causality, error correction and augmented VAR modelling

A temporal precedence based clustering method for gene expression microarray data

Background: Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to certain characteristics. The majority of clustering techniques are based on distance or visual similarity measures which may not be suitable for clustering of temporal microarray data where the sequential nature of time is important. We present a Granger causality based technique to cluster temporal microarray gene expression data, which measures the interdependence between two time-series by statistically testing if one time-series can be used for forecasting the other time-series or not. Results: A gene-association matrix is constructed by testing temporal relationships between pairs of genes using the Granger causality test. The association matrix is further analyzed using a graph-theoretic technique to detect highly connected components representing interesting biological modules. We test our approach on synthesized datasets and real biological datasets obtained for Arabidopsis thaliana. We show the effectiveness of our approach by analyzing the results using the existing biological literature. We also report interesting structural properties of the association network commonly desired in any biological system. Conclusions: Our experiments on synthesized and real microarray datasets show that our approach produces encouraging results. The method is simple in implementation and is statistically traceable at each step. The method can produce sets of functionally related genes which can be further used for reverse-engineering of gene circuits

Information Flow in Computational Systems

We develop a theoretical framework for defining and identifying flows of information in computational systems. Here, a computational system is assumed to be a directed graph, with "clocked" nodes that send transmissions to each other along the edges of the graph at discrete points in time. We are interested in a definition that captures the dynamic flow of information about a specific message, and which guarantees an unbroken "information path" between appropriately defined inputs and outputs in the directed graph. Prior measures, including those based on Granger Causality and Directed Information, fail to provide clear assumptions and guarantees about when they correctly reflect information flow about a message. We take a systematic approach---iterating through candidate definitions and counterexamples---to arrive at a definition for information flow that is based on conditional mutual information, and which satisfies desirable properties, including the existence of information paths. Finally, we describe how information flow might be detected in a noiseless setting, and provide an algorithm to identify information paths on the time-unrolled graph of a computational system.Comment: Significantly revised version which was accepted for publication at the IEEE Transactions on Information Theor