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Record W4254808016 · doi:10.12681/eadd/47001

Topics in applied financial economics

2019· dissertation· en· W4254808016 on OpenAlexaboutno aff
Κωνσταντίνος Τσιάρας

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicCredit Risk and Financial Regulations
Canadian institutionsnot available
Fundersnot available
KeywordsSpillover effectEquity (law)Financial marketEconomicsEmpirical evidenceFinancial economicsChinaFinancial contagionForeign exchange marketVolatility (finance)Monetary economicsExchange rateGeographyFinanceMacroeconomicsPolitical science

Abstract

fetched live from OpenAlex

This dissertation consists of four self-contained chapters in the form of papers. The first chapter investigates the volatility spillover effects and the contagion to sovereign CDS spread returns for Germany, France, China and Japan against USA. To the best of our knowledge, this is the first empirical research in the literature, which investigates potential spillovers and contagion effects among sovereign CDS markets. We use daily data from October 2011 to February 2018. Employing a fourvariate cDCC-AR-FIGARCH model, we find evidence of spillover effects for all the pairs of markets. Furthermore, we find empirical evidence of contagion for the pairs of markets: Germany – France, Germany – Japan and France – Japan. Regarding China’s CDS market we obtain little empirical support for contagion with the rest of the countries. The results are of interest to policymakers, who provide regulations for the CDS markets, as well as to market-makers. The second chapter investigates the spillover effects and the contagion to major equity and FOREX markets of G20. The financial markets under scrutiny are those of USA, Brazil, Italy, Germany and Canada. The frequency of the data is daily. We set the sample period from April 2010 to April 2018, namely after the GFC. Other related empirical work include Kanas (2000), who investigated the existence of spillovers between national equity and FOREX markets, by employing a trivariate AR-diagonal BEKK model for S&P 500, national equity markets and the respective FOREX markets. Our empirical results find evidence of spillovers and contagion effects for the pairs of markets: S&P500-BOVESPA, S&P500-FTSEMIB, S&P500-DAX30 and S&P500-S&PTSX. Moreover, the pairs of markets S&P 500-CAD/USD, S&P 5000BRL/USD and BOVESPA-BRL/USD present no contagion. The resultsare of interest to individual investors, who want to diversify their portfolios through international financial market investments. The third chapter investigates the spillovers and the financial contagion of four major FOREX markets. The FOREX markets are those of EUR/USD, JPY/USD, CHW/USD and GBP/USD. Lee (2010) investigates ten FOREX markets in Asia and Latin America to USD, among others and finds evidence of spillover effects from JPY/USD on Asian currency markets. A fourvariate dynamic Conditional Correlation Generalized ARCH (DCC-GARCH) model is employed for the period April 2011 to February 2018. The empirical results suggest contagion for all the pairs of markets. Additionally, we find that EUR/USD and GBP/USD present the strongest contagion effects, while CHW/USD show the lowest contagion levels with the rest of the markets.The fourth chapter analyses the spillover and the contagion effects of MSCI (global index), NIKKEI 400 (Japan), CSI 300 (China) and S&P 500 (USA). We consider a portofolio analysis in order to produce the standardized residuals using in a trivariate cDCC-GARCH framework. Other research work include Miyakoshi (2003), who suggests the existence of spillover effects between USA and Asian national equity markets. We extend the above analysis by taking into consideration the individual effects of MSCI on three of the most important national equity markets. We use daily data for the period 2008-2018. The main empirical results are the following: (1) portfolio analysis results suggest that MSCI has a significant positive influence on all equity market returns, (2) we find empirical evidence of spillovers on all pairs and (2) we find contagion for the pairs of markets: NIKKEI 400-CSI 300, NIKKEI 400-S&P 500 and S&P 500-CSI 300 that indicate risky positive correlations from an investor’s perspective.

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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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.016
GPT teacher head0.210
Teacher spread0.194 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2019
Admission routes1
Has abstractyes

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