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Record W2034493173 · doi:10.5539/ijef.v3n3p214

Empirical Analysis of the Impact of Foreign Institutional Investment on the Indian Stock Market Volatility during World Financial Crisis 2008-09

2011· article· en· W2034493173 on OpenAlexvenueno aff
Hojatallah Goudarzi, C S Ramanarayanan

Bibliographic record

VenueInternational Journal of Economics and Finance · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsCointegrationMonetary economicsGranger causalityFinancial crisisVolatility (finance)Exchange rateStock marketInterest rateForeign direct investmentMacroeconomicsFinancial economicsEconometrics

Abstract

fetched live from OpenAlex

Capital inflows play a substantial role in developing countries. It used to increase accumulation and rate of investments to create conditions for more intensive economic growth. Capital inflows are necessary for macroeconomic stability as capital inflows affect a wide range of macro economic variables such as exchange rates, interest rates, foreign exchange reserves, domestic monetary conditions as well as saving and investments. Capital inflows, however, are not without risk. The main risk posed by large and volatile capital inflows is that they may resulted in crisis and destabilize macroeconomic management. Given the role of FII flews and its associated risks, the main purpose of this paper was to investigate the cointegration and causality between the Indian stock market and foreign institutional investment (FII) In India during world financial turmoil of 2008. The cointegration and causal relationship using Engle-Granger (1987), Johansen (1991, 1995a) and Granger (1969) methodologies were investigated .The study found that BSE500 stock index and FII series are cointegrated and causality between them is bilateral.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.263
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations23
Published2011
Admission routes1
Has abstractyes

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