Μεταπτυχιακές Εργασίες

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Topics of conformal prediction in time series
(2025-10-23) Kokkinis, Dimitrios; Κοκκίνης, Δημήτριος; Karlis, Dimitris; Ntzoufras, Ioannis; Kechagias, Stefanos
Conformal Prediction is an uncertainty quantification framework for Statistical and Machine Learning problems. In particular, Conformal Prediction (CP) offers a distribution-free approach, which typically requires data exchangeability. Under this assumption, the joint probability of observations is not affected by changing their sequence. However, the reliance on exchangeability limits its direct application to Time Series data. This thesis reviews three recent adaptations of CP designed for non-exchangeable data, namely Ensemble Batch Prediction Intervals (EnbPI), Aggregate Conformal Inference (AgACI), and Weighted Conformal Prediction. After presenting these methodologies in detail, we evaluate their performance through simulation studies. In the first study, we apply them to non-linear data with varying degrees of correlation, while in the second study, we examine their behavior under distribution drift. Our findings suggest that EnbPI is the best option for weakly correlated data, since it can provide valid and informative prediction intervals, while simultaneously being easy to implement. For strongly correlated data on the other hand, AgACI is the preferred choice, due to its validity and low variance of its coverage distribution. Finally, in the distribution drift case, Weighted CP stands out from the rest, because its implementation is straight-forward and its results, coverage and efficiency wise, are less affected by the distribution drifts.
Polling misses: causes and treatment
(2025-09-18) Barali, Foteini; Μπαραλή, Φωτεινή; Psarakis, Stelios; Chasiotis, Vasileios; Papageorgiou, Ioulia
This thesis explores the recurring phenomenon of polling inaccuracies, or "polling misses," in election forecasting, examining the systemic, methodological, and behavioral factors contributing to these failures. It begins by establishing the historical context and evolution of election polling, highlighting its critical role in modern democratic processes, media narratives, and campaign strategies. Despite significant advancements in survey technology—from telephone-based to digital and multi-mode platforms—recent elections such as the 2016 U.S. presidential election, the Brexit referendum, and the 2018 Quebec provincial vote have demonstrated notable inaccuracies that challenge the reliability and legitimacy of polls. Central to the analysis is an investigation of the structural vulnerabilities inherent in polling methods, including sampling errors, nonresponse bias, coverage gaps, and inadequate weighting procedures. It underscores the challenges posed by rapidly evolving communication habits and demographic shifts, illustrating how these factors systematically exclude or misrepresent key voter segments, thus skewing poll results. Additionally, the thesis identifies psychological phenomena such as social desirability bias, the "shy voter" effect, late-decider volatility, and the "bandwagon effect," emphasizing their roles in distorting polling accuracy. Through detailed case studies—including notable polling failures in the United States, the United Kingdom, Quebec, and Australia—the thesis demonstrates that polling misses rarely result from isolated errors but rather from a complex interplay of methodological shortcomings and dynamic voter behaviors. It critically assesses contemporary methodological innovations designed to mitigate these errors, such as Multilevel Regression with Post-stratification (MRP), hybrid sampling designs, adaptive fieldwork, and real-time weighting adjustments. The research ultimately advocates for a dual approach: continual methodological refinement paired with heightened transparency and ethical standards. By integrating rigorous statistical techniques with an understanding of voter psychology and behavior, pollsters can better navigate the complexities of modern electorates. This thesis contributes valuable insights and recommendations aimed at enhancing the accuracy, credibility, and utility of public opinion polling, ensuring it remains a vital and trusted component of democratic discourse and decision-making.
The application of machine learning algorithms in the study of out-of-wedlock fertility patterns in Thrace, Greece
(2025-09-29) Kontogiannis, Georgios; Κοντογιάννης, Γεώργιος; Psarakis, Stelios; Panousis, Konstantinos P.; Ntzoufras, Ioannis
This MSc thesis (Applied Statistics, Athens University of Economics and Business) examines out-of-wedlock fertility in Thrace, Greece (2000–2018) using population-level anonymized birth microdata from the Hellenic Statistical Authority (ELSTAT) (N = 67,706 births). The study combines exploratory demographic analysis with predictive modelling to investigate the socio-demographic, cultural, and geographic determinants of extramarital births in a culturally diverse and socioeconomically disadvantaged region. Descriptive findings reveal substantial spatial variation across municipalities and a strong concentration of nonmarital births among adolescents, low-educated mothers, and Roma populations, alongside an emerging pattern among older, highly educated women, indicating heterogeneous pathways into nonmarital family formation. Methodologically, the thesis integrates logistic regression with ensemble machine learning approaches (XGBoost, Random Forests, LightGBM, and CatBoost) to capture nonlinear relationships and complex interactions. Model performance is assessed under pronounced class imbalance using precision, recall, F1-score, balanced accuracy, ROC-AUC, and AUPRC, with classification thresholds optimized for substantive relevance. Model interpretability is addressed through feature importance measures and SHAP values, allowing for transparent comparison between traditional statistical models and machine learning techniques. Overall, the results support a dual interpretation of extramarital fertility in Thrace: as both a manifestation of social disadvantage and inequality and a reflection of changing family behaviors consistent with Second Demographic Transition perspectives. The thesis highlights the need for targeted social policies supporting vulnerable mothers and ensuring equal legal and social protection for children regardless of parental marital status.
From accuracy to profitability: evaluating credit rating models’ economic impact
(2025-09-30) Papadopoulos, Nikolaos A.; Παπαδόπουλος, Νικόλαος Α.; Ntzoufras, Ioannis; Giudici, Paolo; Karlis, Dimitrios
This study focuses on the economic value of various predictive accuracy metrics in credit rating models. The general logic of the banks is that they rely on regression-based approaches, while more recently, it’s common to see the use of machine learning techniques to assess borrower risk. However, the question of whether investing in higher-performing models or not generates countable financial benefits remains underexplored, and most importantly, underanalysed. This paper addresses this gap by examining how more enhanced discriminatory power in models affects not only profitability, but also the lending quality and regulatory capital requirements of the banks. The analysis identifies three primary transmission channels, that are explained thoroughly in the main text, through which the models’ accuracy can influence different economic outcomes given that: (1) improved loan origination reduces defaults and enhances margins by better identifying low-risk borrowers; (2) stronger models mitigate adverse selection, a very vast sector in banking, helping retain creditworthy clients who might be lost to competitors otherwise; and (3) more accurate models, so more higher value metrics and, by extension, risk assessments can reduce Risk-Weighted Assets (RWA), freeing regulatory capital. In order to address this request in a more direct way, we are using simulation-based methods, generating synthetic loan portfolios (50,000 loans at 3% default and 10,000 prospects at 10% default) and evaluating models across Area Under the Receiver Operating Characteristic curve (AUROC) bands from 65% gradually increasing to 90%. In order to do that, based on references in the bibliography, we are confident to use different logistic distributions that were applied to mimic predictive scores, and they were calibrated to ensure consistent risk levels. In the end, the results show that defaults among top-approved loans decline sharply with better accuracy models - from nearly 6% at AUROC with 65% accuracy to less than 1% at AUROC with 90%. Proceeding to the adverse selection analysis, we can confirm that stronger models attract and retain more profitable clients. The capital impact is smaller but meaningful, with average RWA reductions of around 8% between lower-and higher-accuracy scenarios. Lastly, the profitability that was measured from the previous analysis gives further value to the model improvements. By applying a realistic arithmetical example, on a €3.5 billion retail portfolio, each 5-point AUROC percentage increase can generate approximately €0.8-1 million in addition to the annual profit, with relative gains of 5-12% depending on competitive dynamics. These effects can compound over time as new loans are added annually, while the findings show us that even with incremental improvements in model discrimination can yield and generate significant economic returns, reinforcing the strategic importance of continuous model enhancement. Banks, regulators, and model developers at the same time can use these insights to firstly justify investments, then set performance benchmarks, and also better understand the link between model validation metrics and real-world financial outcomes.
Electricity market modelling and Gaussian process regression
(2025-12-22) Mourikis, Georgios; Μουρίκης, Γεώργιος; Yannacopoulos, Athanasios; Vakeroudis, Stavros; Papaioannou, Panagiotis
Electricity price modelling constitutes an essential challenge for energy trading and risk management, relevant to different types of energy market participants – producers, retailers, and traders. The interest in going beyond point-forecasting approaches to estimate confidence intervals around the point price can apply to the needs of most participants. This thesis reviews the scientific literature to describe the different methods used for electricity price forecasting, the growing interest in probabilistic approaches, and the positioning of Gaussian Processes among them. Considering relevant literature that encourages transition from the traditional benchmark autoregressive and linear models to other algorithms - and among them – to the Gaussian Process Regression, we explore the application of the algorithm to the estimation of daily electricity prices using the Matern covariance function and assessing the accuracy and reliability of the point and interval prediction for the estimation of daily electricity prices. Our focus is on the electricity markets of Germany, France, and Italy, for which we will also explore the application of our predictions against the daily electricity futures.
Functional data analysis: an application to FTIR spectroscopy and parchment artificial ageing
(2025-10-17) Maliaritis, Efthymios; Μαλιαρίτης, Ευθύμιος; Malea, Aikaterini; Chasiotis, Vasileios; Karlis, Dimitrios
Parchment, a fundamental medium of documentary cultural heritage, requires reliable tools for assessing degradation under environmental stress. In this study, 48 new goat-hide parchment samples were artificially aged under controlled exposure to relative humidity, nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ageing duration, and order of gas exposure. Fourier Transform Infrared (FTIR) spectroscopy was employed to monitor molecular-level changes in collagen. Departing from traditional peak-based analyses, a Functional Data Analysis (FDA) framework was adopted, treating FTIR spectra as continuous curves. Functional regression models were applied to evaluate the effects of environmental factors across the full spectral domain. Scalar-on-function regression revealed statistically significant differences between artificial aged and control samples, while function-on-scalar regression identified interpretable and significant effects—particularly in Amide II, lipid, and carbonate bands—associated with humidity, SO₂, and NO₂ exposure. These results align with prior peak-based findings while extending interpretability through smooth coefficient functions and bootstrap-based simultaneous confidence bands. FDA thus provides a robust framework for interpreting complex spectral changes and enhances the analytical power of FTIR spectroscopy in heritage science, particularly when integrated with structured experimental design and nonparametric inference.
Directional predictability of U.S. stock market returns using econometric and machine learning techniques
(2025-12-19) Anagnostopoulos, Vasileios; Αναγνωστόπουλος, Βασίλειος; Chasiotis, Vasileios; Panousis, Konstantinos; Vrontos, Ioannis
This thesis investigates the directional predictability of U.S. stock market returns using both econometric and machine learning models. The analysis compares traditional binary response models, such as Logit and Probit, with regularized regressions (Ridge, LASSO, and Elastic Net) and tree-based ensemble methods, including Bagging, Random Forest, Gradient Boosting, and Extreme Gradient Boosting. Using monthly data and a large set of financial and macroeconomic predictors, the models are estimated in an expanding window framework that mimics real-time forecasting. Model performance is evaluated out of sample using classification metrics, with particular focus on the Area Under the Curve (AUC). The results show that forecasting the direction of monthly stock returns remains a difficult task. Machine learning methods provide some improvement in predictive accuracy compared to traditional econometric models, mainly during crisis periods such as the COVID-19 episode, when relationships between predictors and returns temporarily strengthen. However, in stable market conditions, all models exhibit limited predictive power, consistent with the efficient market hypothesis. Overall, the findings highlight the episodic nature of return predictability and the value of flexible, data-driven methods for detecting changes in market dynamics.
Composite endpoints in clinical trials
(2025-09-22) Kanellakopoulou, Adamantia; Kanellakopoulou, Adamantia; Demiris, Nikolaos; Thomadakis, Christos; Karlis, Dimitrios
Clinical trials represent a fundamental process in the evaluation of the efficacy of new therapies. However, it is important to note that, in many clinical trials, a single primary endpoint is often insufficient to fully capture the therapeutic effect, particularly when events are rare, necessitate extended follow-up, or only reflect a fraction of the treatment's clinically meaningful impact, often resulting in increased trial costs. Consequently, composite endpoints are commonly used, combining two or more clinically relevant outcomes into a single measure. The individual outcomes, known as components of the composite endpoint, represent the possible events associated with the disease and its treatment. This approach provides several advantages, most notably an increased number of observed events and therefore an increase in the power of the clinical trial. However, composite endpoints also introduce considerable challenges, particularly at the design stage, since the target sample size is often subject to a high level of uncertainty, while at the same time the interpretation of the observed effect for the composite endpoint does not necessarily reflect the effects of the individual components. This thesis initiates with a review of the broader context of clinical trial methodology, with particular emphasis on the pivotal role of the primary endpoint in guiding trial design and interpretation, while also aiming to improve efficiency and feasibility in practice. The subsequent introduction of composite endpoints as a methodological development can increase statistical efficiency, reduce required sample sizes, and provide a more complete evaluation of treatment effects. Specifically, the two principal types of composite endpoints—binary composite endpoints and time-to-first-event endpoints are examined, discussing their construction and interpretation, along with the main challenges that arise in practice. In order to facilitate the connection between theoretical concepts and their practical applications, the thesis employs illustrative examples from the fields of cardiology and oncology. These two disciplines have come to play a significant role in clinical practice and regulatory frameworks, owing to the use of composite endpoints. The presented examples show how key design parameters, such as event probabilities, hazard structures, and correlation assumptions, affect sample size determination, statistical power, and the reliability of trial conclusions. Finally, simulation studies are employed to evaluate the performance of the proposed methods under realistic clinical conditions. Overall, the analysis presents the main methodological contributions of the thesis while also discussing the practical and interpretational challenges associated with the use of composite endpoints. By combining theoretical developments, applied examples, and simulation studies, it provides a clear perspective on the design of contemporary clinical trials and illustrates how composite endpoints, when carefully defined and appropriately modelled, can be both statistically reliable and clinically meaningful.
Solving partial integro-differential equations using physics-informed neural networks
(2025-10-16) Georgakopoulos, Nikolaos; Γεωργακόπουλος, Νικόλαος; Vakeroudis, Stavros; Yannacopoulos, Athanasios; Georgiou, Kyriakos Christopher
This thesis investigates the application of Physics-Informed Neural Networks (PINNs) to solve partial integro-differential equations (PIDEs) arising in financial mathematics, with particular focus on credit risk modeling. Traditional numerical methods for solving PIDEs, such as finite difference schemes, face computational challenges when dealing with jump-diffusion processes, especially in real-time applications requiring rapid probability-of-default calculations. This work develops a comprehensive framework for approximating solutions to PIDEs governing Lévy-driven Ornstein–Uhlenbeck processes using deep neural networks. The methodology incorporates the governing equations directly into the neural network training process through a composite loss function that enforces the PIDE residual, boundary conditions, and terminal conditions simultaneously. The experimental validation demonstrates that PINNs successfully learn accurate approximations of probability-of-default functions for jump-diffusion models. Comparison with Monte Carlo simulations validates that the solution learned by the PINN is indeed realistic. Most significantly, the trained PINN achieves computational speedups of over 3,600 times compared to traditional finite difference methods, reducing inference time from approximately 98 seconds to 0.027 seconds while maintaining comparable accuracy. The results establish PINNs as a viable alternative to conventional numerical methods for solving financial PIDEs, particularly in scenarios requiring rapid evaluation across varying market conditions. The computational efficiency gains make sophisticated jump-diffusion models practically viable for real-time risk management applications, including algorithmic trading, portfolio optimization, and regulatory stress testing. This work contributes to the growing intersection of physics-informed machine learning and quantitative finance, demonstrating how modern deep learning techniques can address fundamental computational challenges in modeling dynamic systems governed by physical laws.
Comparative study on analyzing univariate count time series data
(2025-11-18) Spanou, Varvara; Σπανού, Βαρβάρα; Besbeas, Panagiotis; Chasiotis, Vasilios; Vrontos, Ioannis
This thesis investigates statistical forecasting models for daily counts of incoming customer conversations, collected from a UK-based fintech company. The data exhibit strong overdispersion and positive autocorrelation, with evident weekly, biweekly, and roughly monthly seasonal patterns. The initial exploratory analysis indicated that the Poisson distribution could not account for the overdispersion present in the data, whereas the Negative Binomial distribution provided a better fit. Diagnostic checks using generalized linear models (GLMs) and generalized additive models (GAMs) revealed nonlinear effects of temporal lags, suggesting that models restricted to linear autoregression—such as integer-valued autoregressive (INAR) processes—were inadequate. In practice, only the INAR(1) model achieved a satisfactory fit, as the first lag exhibited an almost linear relationship, while higher-order lags could not be appropriately incorporated. To overcome these limitations, the thesis adopts a Negative Binomial state-space model (SSM). The fitted model confirmed the importance of lags 1, 7, 16, and 21, consistent with the autocorrelation structure identified in the time-series analysis. The study concludes that Negative Binomial state-space models provide a flexible framework for discrete-valued time series exhibiting overdispersion, outperforming traditional Poisson and INAR approaches in this context. Future research could extend the model by introducing dynamic latent states such as stochastic trends or seasonal components, explore Hidden Markov structures to allow switching between states, or compare its predictive performance with machine-learning methods such as random forests and neural networks. Overall, these approaches appear promising for improving operational forecasts of incoming communication volumes within customer-service operations in fintech firms and related service industries.
Investigating sewage overflow patterns in British rivers with hidden Markov models
(2025-11-10) Tsourma, Maria-Eleni; Τσούρμα, Μαρία-Ελένη; Psarakis, Stelios; Besbeas, Panagiotis
This thesis studies daily sewage‐overflow counts and how they fluctuate over time. We use data from Thames Water with rainfall by location and daily spill duration. After testing various models, we focus on Hidden Markov Models (HMMs). In an HMM the data originate from unobserved states with the assumption they consist a Markov chain with a transition matrix. We fit models with distinct numbers of states, allow covariates to act either on state transitions or on the mean count, and handle overdispersion with negative binomial emissions. Models are estimated in R with hmmTMB package. For consistency, we compared results to a simple Poisson–HMM estimated by closed-form MLE. Closing the analysis, we generate forecasts and simulate series from the top-performing HMMs. Selected by their in-sample fit, these models capture persistent regimes and shifts driven by rainfall and spill duration. The simulations reproduce key empirical features, offering a direct check against the observed data.
"Carry-informed" FX pair trading using machine learning
(2025-11-25) Kontopoulos, Stefanos P.; Κοντόπουλος, Στέφανος; Vakeroudis, Stavros; Yannacopoulos, Athanasios; Papaioannou, Panagiotis
This thesis studies and compares three systematic FX strategies: (i) a cross-sectional carry sleeve that combines spot moves with daily interest accrual from policy-rate differentials, (ii) a simple SMA momentum rule (42/252), and (iii) a relative-value machine-learning approach (Spread-ML) that trades price pairs only when cointegration is statistically supported. The backtest spans 2015–2025 across six USD crosses, uses daily rebalancing, a one-day execution lag, and the 3-month U.S. T-bill as the risk-free leg for annualized Sharpe computation. Methodologically, we apply a 90-business-day rolling Engle–Granger filter on log prices, estimate a contemporaneous OLS hedge ratio to construct the spread, and train a rolling classifier that outputs {-1,0,+1} signals on daily spread changes. Out-of-sample results indicate that Spread-ML delivers the strongest risk-adjusted profile (Sharpe near one with moderate drawdowns), carry is modestly positive, while SMA underperforms in sideways markets. Returns are reported gross of transaction costs; costs would compress net performance, especially for momentum. The contribution is a transparent, econometrically grounded trading pipeline that blends macro information, regime gating, and lightweight ML.
Modeling and predicting U.S. recessions using threshold - tree structured - logit models
(2025-11-18) Mitrakou, Theofania; Μητράκου, Θεοφανία; Besbeas, Panagiotis; Chasiotis, Vasileios; Vrontos, Ioannis
Forecasting economic recessions is a key component of macroeconomic analysis and policy planning. This thesis focuses on modeling and predicting U.S. economic recessions using binary regression models and modern machine learning techniques. The analysis begins with the application of classical Logit and Probit models and is extended to more advanced and flexible methods such as LASSO, Elastic Net, Random Forests, and Boosting. Emphasis is placed on capturing non-linear relationships through tree-based and threshold models. The empirical study is based on a wide range of macroeconomic and financial indicators, which are incorporated in lagged form to predict the probability of recession at various forecasting horizons: 1, 3, 6, and 12 months. Model performance is evaluated using standard statistical metrics such as accuracy, sensitivity (recall), and the ROC curve. Finally, the aim of the thesis is to identify the most effective predictive models and to better understand which economic indicators are most associated with the onset of recessions.
Introduction to hidden Markov models and their application to financial theory
(2025-11-04) Barkolias, Evangelos-Panagiotis; Μπαρκολιάς, Ευάγγελος-Παναγιώτης; Vrontos, Ioannis; Giannakopoulos, Thanasis; Besbeas, Panagiotis
Hidden Markov Models (HMMs) emerged in the late ’60s as a statistical framework designed to extract latent information from data characterized by uncertainty. Their ability to capture hidden structure beyond observable variables soon made them highly relevant for financial applications, where volatility clustering, regime shifts, and non-normality are pervasive. Before turning to empirical application, it is important to first review the theoretical background that underpins HMMs,ensuring a clear understanding of the statistical concepts on which they are built. Building on this foundation, the thesis investigates the modeling of stock returns, beginning with models without temporal dependence and gradually extending to fully Markovian structures, highlighting the crucial role of state dependence in improving both interpretability and predictive power. Methodologically, the research employs Direct Numerical Maximization for parameter estimation and evaluates state sequences. The results show that incorporating state dependence not only improves the statistical characterization of stock return distributions but also yields interpretable latent states corresponding to calm and turbulent regimes. Furthermore, the analysis emphasizes the importance of approaching financial time series from a purely statistical perspective while also ensuring robust optimization and reliable inference.
Model misspecification in semi-parametric survival models
(2025-10-24) Papastasinopoulou, Katerina; Παπαστασινοπούλου, Αικατερίνη; Pateras, Konstantinos; Pedeli, Xanthi; Besbeas, Panagiotis
Survival analyses in medical research often relies on the Cox proportional hazards (PH) model and related parametric approaches, yet routine violations of modeling assumptions can distort inference. This thesis investigates the consequences of model misspecification for both semi-parametric and parametric survival regression, using simulation. Time-to-event data are generated under prespecified data-generating mechanisms (DGMs), namely, Exponential, Weibull, and Logistic, as well as mixtures that induce both proportional and nonproportional baseline hazards. Then, commonly used models were fitted, and a spectrum of misspecifications was also examined, including omitted covariates and violations of the PH assumption. Two motivating case studies in radiation oncology use patient-level data to illustrate how misspecification appears in practice and how it biases variable effect estimates. The findings highlight that while the Cox model shows relative robustness in some scenarios, substantial bias and assumption violations occur in heterogeneous or mixed populations. Collectively, the results provide practical guidance for model selection and sensitivity analysis in time-to-event studies, emphasizing simulation as a principled means to probe and mitigate misspecification.
Analysis of temporal drug prescription data to detect extremes and patterns using statistical process control tools
(2025-10-07) Taratsas, Andreas; Ταράτσας, Ανδρέας; Pateras, Konstantinos; Vrontos, Ioannis; Psarakis, Stelios
This dissertation analyzes temporal prescription data from the national ePrescription system (IDIKA) using Statistical Process Control (SPC) and time series modelling techniques to identify deviations, structural changes, and underlying prescribing patterns. Data were provided by IDIKA and included multiple ATC-coded pharmaceutical substances. A descriptive statistical analysis was first conducted to explore variability and behavioral dynamics across drugs. Four representative substances (M04AC01, M09AX01, L04AC16, and L04AC18) were selected based on diversity in variability, periodicity, and pattern change. Subsequently, an ARIMA modelling framework was applied to the daily prescription counts to extract residuals free of autocorrelation, which were then analyzed through Shewhart control charts. The Phase I–II approach ensured unbiased estimation of control limits and allowed for process stability assessment over time. Results for M09AX01 demonstrated that the prescribing process remained largely in control, with transient fluctuations attributed to external factors such as policy or supply effects. The findings highlight the applicability of SPC methods—traditionally used in industrial quality control—to healthcare analytics, offering an operational early-warning mechanism for monitoring pharmaceutical utilization and supporting data-driven decision-making within IDIKA.
Ανάλυση καταστροφικών γεγονότων της τελευταίας δεκαετίας μέσα από τα στοιχεία της Ένωσης Ασφαλιστικών Εταιριών Ελλάδος
(2025-11-04) Χαμαλίδου, Χάιδω; Γιαννακόπουλος, Αθανάσιος; Λουλούδης, Εμμανουήλ; Ζυμπίδης, Αλέξανδρος
Οι φυσικές καταστροφές αποτελούν ένα από τα σημαντικότερα πεδία μελέτης στο πλαίσιο της σύγχρονης διαχείρισης κινδύνου, καθώς οι κοινωνικές και οικονομικές τους επιπτώσεις είναι ολοένα και πιο εμφανείς σε παγκόσμιο αλλά και εθνικό επίπεδο. Ενδεικτικά περίπου το 85% του παγκόσμιου πληθυσμού έχει επηρεαστεί από τουλάχιστον μία φυσική καταστροφή τα τελευταία 30 χρόνια ενώ κατά μέσο όρο 60.000 θάνατοι ετησίως αποδίδονται σε φυσικές καταστροφές, με το 90% αυτών να συμβαίνει σε αναπτυσσόμενες χώρες. Η Ελλάδα, λόγω της γεωγραφικής της θέσης και των ιδιαίτερων φυσικών και κλιματικών χαρακτηριστικών της, συγκαταλέγεται στις ευρωπαϊκές χώρες με υψηλή έκθεση και συχνότητα εμφάνισης φυσικών φαινομένων, όπως σεισμοί, πλημμύρες, δασικές πυρκαγιές, χιονοπτώσεις και ανεμοστρόβιλοι. Η κατανόηση της σύνδεσης των οικονομικών επιπτώσεων αυτών των φαινομένων εξαιτίας της τρωτότητας των ασφαλισμένων οντοτήτων (πχ κτιρίων, αυτοκινήτων) σε αυτά αποτελεί κρίσιμη προϋπόθεση για την ανάπτυξη πολιτικών πρόληψης, την ορθολογική κατανομή ασφαλιστικών πόρων και τη θωράκιση των τοπικών κοινωνιών απέναντι σε μελλοντικούς κινδύνους.
Ρευστότητα χρηματοδότησης και τραπεζικός κίνδυνος: μια παγκόσμια βιβλιογραφική ανασκόπηση
(2025-10-22) Νταραγιάννης, Χαράλαμπος; Ρομπόλης, Λεωνίδας; Τσεκρέκος, Αντριανός; Επίσκοπος, Αθανάσιος
Το χρηματοπιστωτικό σύστημα αποτελεί βασικό πυλώνα της οικονομίας κάθε κράτους, επηρεάζοντας καθοριστικά τον ρυθμό και την πορεία της ανάπτυξης. Η λειτουργία του βασίζεται στην ύπαρξη θεμελιωδών θεσμών οι οποίοι ενεργούν ως δίαυλοι μεταφοράς κεφαλαίων από πλεονάζουσες οικονομικές μονάδες σε μονάδες που δύνανται να τα αξιοποιήσουν παραγωγικά. Μέσω αυτής της ανακατανομής, διευκολύνεται όχι μόνο η επένδυση αλλά και η δημιουργία νέου χρήματος. Η αποτελεσματικότητα των χρηματοπιστωτικών φορέων καθορίζει τη ροή κεφαλαίου της οικονομίας. Ιδιαίτερη σημασία δίνεται στον ρόλο του τραπεζικού συστήματος ως μηχανισμού διαχείρισης και καταμερισμού του κινδύνου. Οι τράπεζες αξιολογούν τον πιστωτικό κίνδυνο, υποθέτοντας την ομαλή αποπληρωμή των δανείων. Αυτή η λειτουργία αυξάνει τον κίνδυνο, καθιστώντας αναγκαία την ύπαρξη εποπτικών και ρυθμιστικών αρχών. Ιδιαίτερα κρίσιμη για την εύρυθμη λειτουργία των τραπεζικών ιδρυμάτων είναι η διατήρηση επαρκούς ρευστότητας. Η ρευστότητα προσδιορίζει την ικανότητα ενός χρηματοπιστωτικού ιδρύματος να ανταποκρίνεται άμεσα στις υποχρεώσεις του, χωρίς να επιβαρύνει σημαντικά τη χρηματοοικονομική του θέση. Η ανεπαρκής ρευστότητα συνδέεται άμεσα διαχρονικά με τραπεζικές κρίσεις και απώλεια εμπιστοσύνης από τους καταθέτες. Η θεωρητική βάση για την κατανόηση της σημασίας της ρευστότητας αποτυπώνεται στο κλασικό υπόδειγμα των Diamond & Dybvig (1983). Στο πλαίσιο αυτό, η ανάγκη για αυστηρούς κανονισμούς ρευστότητας, όπως αυτοί που εισήγαγε η Βασιλεία ΙΙΙ με τον δείκτη κάλυψης ρευστότητας (LCR) και τον δείκτη καθαρής σταθερής χρηματοδότησης (NSFR), συνδέεται άμεσα με τη θεωρητική ανάλυση του Diamond-Dybvig, καθώς στοχεύει στη μείωση της πιθανότητας τραπεζικού πανικού και να ενισχύσει τη σταθερότητα του χρηματοπιστωτικού συστήματος. Η παρούσα εργασία έχει ως σκοπό να αναλύσει το ρυθμιστικό πλαίσιο της θέσπισης της εποπτικής επιτροπής της Βασιλείας, πως διαμορφώθηκε το πλαίσιο έως τη Βασιλεία ΙΙΙ και ποια η σχέση με τη ρευστότητα χρηματοδότησης. Γίνεται μια εκτενής καταγραφή και σύγκριση μεταξύ των αποτελεσμάτων που έχουν βρεθεί σε 63 επιστημονικά άρθρα και μελέτες οι οποίες αφορούν τραπεζικά ιδρύματα ανά τον κόσμο αναφορικά με τα επίπεδα ρευστότητας που τους επιβάλλει η Βασιλεία ΙΙΙ, με στόχο τη μείωση της πιθανότητας τραπεζικής κρίσης ώστε να διασφαλιστεί η μακροπρόθεσμη σταθερότητα στον τραπεζικό κλάδο. Η διπλωματική συμβάλλει στην ελληνόγλωσση βιβλιογραφία καθώς δεν υπάρχει δημοσιευμένη βιβλιογραφική επισκόπηση στο θέμα της ρευστότητας χρηματοδότησης των τραπεζών.
Actuarial risk assessment: modeling COVID-19 mortality in Europe
(2025-10-24) Constantinou, Vasilia; Κωνσταντίνου, Βασιλεία; Pedeli, Xanthi; Pateras, Konstantinos; Besbeas, Panagiotis
This thesis models weekly COVID-19 mortality across 20 European countries using a spline-based Negative Binomial Generalized Linear Model (NB-GLM). The model incorporates B-splines for time, country fixed effects and key epidemiological, demographic and healthcare predictors, explaining 78% of the variation in mortality. New cases, ICU occupancy and lagged deaths emerged as strong predictors, reflecting both infection trends and healthcare strain. Demographic factors, particularly the proportion of elderly population and diabetes prevalence, were linked to higher baseline mortality. Vaccination effects were wave-dependent, with a protective impact in wave 2 but more complex dynamics in wave 3 due to timing and interactions with other factors. Country-specific effects showed substantial geographic disparities, with Bulgaria and Romania exhibiting higher mortality than countries like Italy and Portugal. Model diagnostics revealed mild residual autocorrelation and slight underestimation in extreme mortality weeks. For actuarial applications, the results support dynamic mortality loadings, wave and country-specific adjustments and the use of epidemiological indicators as early-warning signals for pricing and reserving. The framework provides a flexible, interpretable approach to pandemic mortality modeling, offering insights for both COVID-19 risk management and broader actuarial applications to public health crises.
An introduction to synthetic survival data
(2025-09-22) Κατσαρού, Βαρβάρα-Γρηγορία; Katsarou, Varvara-Grigoria; Pedeli, Xanthi; Thomadakis, Christos; Demiris, Nikolaos
Οι συνθετικές ομάδες ελέγχου (Synthetic Controls) αποτελούν μια ταχέως αναπτυσσόμενη προσέγγιση, ικανή να αντιμετωπίσει προκλήσεις στον σχεδιασμό των κλινικών ερευνών. Οι ομάδες αυτές κατασκευάζονται χρησιμοποιώντας εξωτερικά δεδομένα προηγούμενων μελετών και αποτελούνται αποκλειστικά απο εικονικούς ασθενείς. Μέσω στατιστικών και υπολογιστικών μεθόδων, καθώς και τεχνικών προσομοίωσης, αναπαράγονται τα χαρακτηριστικά των ασθενών αντιμετωπίζοντας προβλήματα όπως η δυσκολία συγκέντρωσης επαρκούς αριθμού ασθενών και η περιορισμένη πρόσβαση σε δεδομένα σχετικά με αυτούς. Σκοπός της παρούσας διατριβής είναι η παρουσίαση των μεθόδων για την δημιουργία συνθετικών ομάδων ελέγχου, καθώς και η επέκταση του πλαισίου της σύνθεσης δεδομένων επιβίωσης για την δημιουργία συνθετικών ασθενών, τόσο για τις ομάδες ελέγχου όσο και για τις ομάδες θεραπείας. Δύο εφαρμογές σε ογκολογικά δεδομένα παρουσιάζονται: η πρώτη αφορά γυναίκες με καρκίνου του μαστού, ενώ η δεύτερη εξετάζει ένα σύνολο ασθενών με καρκίνο του ήπατος και των χοληφόρων. Και οι δύο εφαρμογές αναλύουν τα κλινικά χαρακτηριστικά των ασθενών, τις τάσεις επιβίωσης τους, αναδεικνύοντας την χρησιμότητα των συνθετικών ομάδων ελέγχου. Κεντρικό στοιχείο ήταν η επαναξιολόγηση των θεραπειών, capecitabine για τον καρκίνο του μαστού και sorafenib για τον καρκίνο τους ήπατος και των χοληφόρων. Οι επιδράσεις των θεραπειών ενσωματώθηκαν ως σταθερές επιδράσεις στα μοντέλα επιβίωσης χρησιμοποιώντας τους αναφερόμενους λόγους κινδύνου (hazard ratios), ενισχύοντας έτσι τη στατιστική ισχύ των αρχικών μελετών και διαφυλάσσοντας τα ευαίσθητα δεδομένα των ασθενών. Στην εφαρμογή για τον καρκίνο του μαστού, η μέση αύξηση επιβίωσης από την capeciatbine ήταν 1,5 έτη για τις ασθενείς με τον τύπο HER2(-) Negative και 2,03 έτη για τις ασθενείς με τον τύπο Triple(-) Negative. Για το ηπατοκυτταρικό καρκίνωμα, η sorafenib αύξησε τη μέση επιβίωση κατά 0,78 έτη (10 μήνες). Συμπερασματικά, τα αποτελέσματα αναδεικνύουν την σημασία των συνθετικών ομάδων ελέγχου και ευρύτερα των συνθετικών δεδομένων επιβίωσης ως καινοτόμα προσέγγιση για την κλινική έρευνα ενισχύοντας την ακρίβεια μελετών, αντιμετωπίζοντας περιορισμούς διαθέσιμων ασθενών και βελτιώνοντας τις εκτιμήσεις της αποτελεσματικότητας θεραπειών.

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