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Record W7011367299

Modelling Australian stock market volatility

2011· dissertation· en· W7011367299 on OpenAlexaboutno aff

Bibliographic record

VenueResearch Online (University of Wollongong) · 2011
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsnot available
Fundersnot available
KeywordsVolatility (finance)Stock marketStock (firearms)Stock market bubbleVolatility swapVolatility risk premiumForward volatilityStock exchangeVolatility smile
DOInot available

Abstract

fetched live from OpenAlex

This thesis examines the interplay between the Australian stock market and other interrelated international stock markets to evaluate the volatility contained within and across these markets. In particular, this thesis aims to: (1) shed some light into the asymmetry of volatility effect across different international stock markets; (2) assess the volatility transmission dynamics across international stock markets during different financial crises by comparing and contrasting the similarities and dissimilarities of those crises; and (3) examine the interaction between stock market volatility and the volatility of economic growth across a number of countries evaluated.\nBased on an extensive literature review, this thesis demonstrates that the asymmetry associated with volatility effects spread across various stock markets and Australia have not been fully investigated. A Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) model for weekly stock market data of Australia, Singapore, the United Kingdom (UK), and the United States (US) for the period spanning from January 1992 to June 2010 is adopted in this thesis. Firstly, the estimated results from the empirical analysis identifies that negative shocks in each market plays an important role in increasing both variances and covariances within and across these stock markets in contrast to positive shocks. Of note, for smaller markets (Australia and Singapore) the asymmetry coefficients in covariances are generally higher than the asymmetry coefficients in the variance equations, suggesting the volatility of these smaller stock markets will increase following negative shocks from other markets. Second, the findings from this study confirm that negative shocks from highly correlated markets can involve higher time-varying covolatility between those two markets. Thus, investors will be highly unlikely to benefit from diversifying their financial portfolio by investing their funds within these four markets only.\nThe second issue that has received little attention in the literature is how volatility between Australia and different international stock markets varies during two different financial crises. This thesis focuses on the 1997–98 Asian crisis and the 2008–09 Global Financial Crisis (GFC). A MGARCH model is augmented with two dummy variables to capture exact timing and possible effects on the volatility of stock markets of Australia, Singapore, the UK, and the US, from the two crises. There exists a significant influence arising from both crises on volatility in all four markets. Although both crises impacted on increasing own-volatility in these four markets, only the recent GFC contributed to increase the cross-volatilities across these four markets.\nFinally, it is found that the nature of the relationship between stock market and the output growth are mixed in relation to the interaction effect of volatility across stock market returns and growth rates of Gross Domestic Product (GDP). This thesis also employs the diagonal version of BEKK (Baba, Engle, Kraft, and Kroner, see Engle and Kroner, 1995) model using quarterly data from 1959 to 2010 for four Anglo-Saxon economies (namely Australia, Canada, the US, and the UK). The results from this empirical analysis indicate that although statistically significant own-mean spillover effects exist in all eight series, the cross-mean spillover effects exist: (1) from the US stock market to the Australian stock market; (2) from the US GDP growth to the US stock market; and (3) from the US GDP growth to GDP growth rates of all four countries. These empirical results confirm that the US stock market predominately influences the Australian stock market while the US economy impacts upon Australian economic growth.\nIn terms of second order moments (1) the own-volatility shocks exist for all eight series except for Australian and Canadian GDP growth series; (2) the covolatility shocks between stock markets and GDP growth rates are also positive and significant with the exception being the covolatility shocks between the Canadian GDP growth and other stock markets; (3) the covolatility across GDP growth rates is also positive and significant except for the covolatility shocks between the Canadian GDP growth and GDP growth rates of other countries; and (4) unlike own-volatility and covolatility shocks (ARCH effect), both the ownvolatility and covolatility spillovers (GARCH effect) within and across all eight series are positive and statistically significant indicating a strong relationship across stock market and the GDP growth series from different countries on increasing corresponding covolatilities.\nIn general, this thesis has made three significant contributions evaluating dynamics of stock market volatility transmission across different stock markets with particular focus on the Australian stock market. First, this thesis extends previous findings by identifying and quantifying the asymmetric volatility effects that exist within and across international stock markets. Second, this research is the first study to evaluate varying volatility implications on volatility transmission across international stock markets during different financial crises by comparing and contrasting their similarities and differences. Lastly, no previous study has simultaneously assessed the interaction effect of volatility across stock market returns and GDP growth rates of different countries.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.310
Teacher spread0.252 · 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 designNot applicable
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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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