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https://olympias.lib.uoi.gr/jspui/handle/123456789/37649Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Τσιούτσιος, Αλέξανδρος | el |
| dc.date.accessioned | 2024-05-17T08:29:39Z | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/37649 | - |
| dc.identifier.uri | http://dx.doi.org/10.26268/heal.uoi.17357 | - |
| dc.rights | CC0 1.0 Universal | * |
| dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | * |
| dc.subject | Econometric analysis | en |
| dc.subject | Garch models | en |
| dc.subject | FCVAR model | en |
| dc.subject | Financial stabiblity | en |
| dc.subject | Wavelet coherence analysis | en |
| dc.subject | Risk management | en |
| dc.subject | Financial analysis | en |
| dc.subject | Financial econometrics | en |
| dc.title | Essays in Applied Financial Econometrics | en |
| dc.type | doctoralThesis | en |
| heal.type | doctoralThesis | - |
| heal.type.en | Doctoral thesis | en |
| heal.type.el | Διδακτορική διατριβή | el |
| heal.dateAvailable | 2027-05-16T21:00:00Z | - |
| heal.language | en | - |
| heal.access | embargo | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Οικονομικών και Διοικητικών Επιστημών | el |
| heal.publicationDate | 2024-02-01 | - |
| heal.abstract | This dissertation centers on the use of econometric and statistical models in the analysis of financial time series. It utilizes a wide array of methodologies, including models from the multivariate GARCH (Generalized Autoregressive Conditional Heteroskedasticity) family and Local Gaussian Correlations. Additionally, it applies Fractionally Cointegrated Vector Autoregressive (FC-VAR) models, along with spectral techniques such as wavelet coherence analysis and Fourier transforms. The primary objective of these methods is to analyze co-movement in volatility and dynamic correlations. These econometric specifications are applied to empirical investigations with a multifaceted set of objectives. Notably, the dissertation explores volatility spillover effects, non-synchronous trading patterns, and long-memory features in market volatility. Furthermore, it aims to elucidate the critical role that green investments play in financial markets and assesses the dynamic interplay between economic policies and financial stability. A particular emphasis is placed on the disaggregated analysis of shipping markets, offering novel perspectives for market complexity. The dissertation contributes substantially to the academic discourse on financial econometrics and offer actionable insights for policy makers and for the investors. This dissertation consists of five self-contained chapters with different applications in the field of applied financial econometrics. The first chapter empirically examines the effect of non-synchronous trading on volatility spillover among G7 equity markets during the Eurozone Sovereign Debt Crisis and the Covid-19 crisis . For data synchronization the MA(1) BEKK and MA(1) DCC models are applied according to Burns et al. (1998). We extend this analysis with realized kernels in variance equation as explanatory variables. Also, we test the results through the wavelet coherence analysis. Furthermore, estimating both DCC and BEKK allows us to exploit the advantages of each model according to Caporin & McAleer (2012). In addition, this is the first article to estimate the Wavelet Coherence Analysis for G-7 major indices with realized kernels. Thus, the contagion effect between the markets is more perceptible, as the spikes are visible. Our main results are indicative of the validity of the synchronization phenomenon which is between (USA-Canada), (USA-Germany), (USA-France), (USA-Italy) and (US-UK) and for (USA-Japan). Finally, we confirm the results employing the Fourier transforms to take the phase differences between synchronous and non-synchronous data. The results provide crucial implications for portfolio diversification strategies and highlight the need for some form for international investors and policy makers. The second chapter introduces the fractionally cointegrated vector auto-regressive model (FCVAR) aimed to study the fractional long-run relationship using the realized kernels of G-7. The data covers a period of 2.329 daily observations from 1st April 2012 to 27th June 2022. We use a Fractionally Cointegrated Vector Autoregressive (FC-VAR) model on realized kernels, rather than standard VAR or DCC/BEKK type models on simple returns, to address the limitations of existing literature and provide robust insights. The main results indicate that Canadian and UK markets demonstrate a strong equilibrium relationship with the US, exhibiting prompt adjustments towards equilibrium. Conversely, Germany, France, Italy, and Japan display divergent trends from the US market, indicating distinct market dynamics. This research contributes to the understanding of market dynamics, long-memory effects, and the role of realized kernels in financial markets, offering valuable guidance for risk management, portfolio allocation and investment decisions. Chapter III investigates Green Bond Index, a financial asset linked to climate change. Climate change is undoubtedly one of the most urgent problems of the 21st century. The need for action has led to a global shift towards green investment, aimed at reducing emissions and supporting sustainable technologies. In this context, green bonds have emerged as a powerful financial tool that offers investors the opportunity to support green growth by combining their financial interests with their environmental responsibilities. The purpose of this paper is to empirically investigate the connectedness among green bond index and S&P 500 (US), FTSE 100 (UK), Nikkei 225 (Japan) and ASE (Greece) indices for the period spanning from January 2014 to June 2022. Using the VAR and causality, the dynamic conditional correlation (DCC) model in order to detect possible co-movements and the wavelet coherence analysis for the robustness check; the results suggest a trend towards achieving the goals of the Paris Agreement and a global push for a net-zero strategy. However, it also implies that markets, are more vulnerable to global risks diminishing any diversification benefits of investing in green bonds. The fourth chapter combines the literature of finance and macroeconomics utilizing the methodology of financial econometrics. In this study, we investigate the relationship between economic growth and key monetary and financial variables, including 10-year bond yields, interest rates, and spreads. We employ the Industrial Production Index (IPI) as a proxy for economic growth and a multivariate GARCH-DCC as econometric framework. This specific choice of econometric framework allows us to assess these relationships across distinct time periods. The main findings imply weak correlation between economic growth, as indicated by the IPI, and the monetary variables under investigation. In almost all cases economic growth is not affected by any changes in various interest rates, bond and spread. The pandemic period presents a negative relationship for spread and bond in relation to IPI, following the mainstream theory, while the 3-months rate follows a different pattern, contrary to other time periods. Maybe the unique characteristics of the pandemic crisis leads to these diverse behavior for the 3-month rate. Additionally, our study extends existing research by incorporating explanatory variables in variance equations and applying wavelet coherence analysis, delving deeper in these complex relationships. The last chapter conducts an empirical investigation of the interrelated volatility among multiple shipping indices—including BDI, BDTI, BCTI, Handysize, and Panamax—and significant financial commodities such as copper, corn, crude oil, wheat, and the S&P 500. As methodological tools BEKK model along with wavelet coherence analysis was used for the aims of the investigation. The research analyzes both short-term and long-term volatility linkages from 2007 to 2023. This timeframe includes crucial global events such as the Global Financial Crisis, the COVID-19 pandemic, and the Russo-Ukrainian war. This timeframe offers the opportunity to explore the contagion impact and inherent risks associated with the shipping industry and financial assets associated with this industry. The results underline the volatility connections, indicating that correlation vary in different crisis scenarios. This study not only enhances our comprehension of the shipping market's complexity but also delves deep into critical perspectives for risk mitigation strategies and portfolio diversification considerations. | en |
| heal.advisorName | Σίμος, Θεόδωρος | el |
| heal.committeeMemberName | Συμεωνίδης, Σπυρίδων | el |
| heal.committeeMemberName | Καινούργιος, Δημήτριος | el |
| heal.committeeMemberName | Μπένος, Νικόλαος | el |
| heal.committeeMemberName | Δότσης, Γεώργιος | el |
| heal.committeeMemberName | Λογοθέτης, Βασίλειος | el |
| heal.committeeMemberName | Πελαγίδης, Θεόδωρος | el |
| heal.academicPublisher | Πανεπιστήμιο Ιωαννίνων. Σχολή Οικονομικών και Διοικητικών Επιστημών. Τμήμα Οικονομικών Επιστημών | el |
| heal.academicPublisherID | uoi | - |
| heal.numberOfPages | 160 | - |
| heal.fullTextAvailability | true | - |
| Appears in Collections: | Διδακτορικές Διατριβές - ΟΕ | |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| Tsioutsios-PHD-uoi-2024.pdf | 6.87 MB | Adobe PDF | View/Open Request a copy |
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