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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.
Competitive intelligence and counterintelligence in the shipping industry: the emergence of the chief intelligence officer
(2025-10-30) Kanellopoulos, Anastasios-Nikolaos N.; Κανελλόπουλος, Αναστάσιος-Νικόλαος Ν.; Kardaras, Dimitrios; Manolopoulos, Dimitrios; Siomkos, Georgios; Konstantopoulos, Ioannis; Sklias, Pantelis; Flouros, Floros; Ioannidis, Antonios
The purpose of this thesis is to investigate the operational integration of Competitive Intelligence and Counterintelligence in the Shipping industry, emphasizing the rise of the Chief Intelligence Officer (CINO) role. The rationale for this research is a result of escalating geopolitical uncertainty, cyber threats and competition in the market, addressing the demand for systematic intelligence leadership in the top-tier Shipping companies. This study develops and operationalizes a conceptual framework to analyze the feasibility and operationalization of the CINO role, outlining several actionable pathways for its establishment and institutionalization. The research follows a mixed-method research approach (MMR) consisting of two sequential stages. The first stage applies a methodology based on social constructivism and evidence-based research (EBR) to collect quantitative and qualitative data from Shipping leaders, to capture the state of current intelligence practices in the top-tier Greek Shipping companies. The results indicate a disjointed and inconsistent approach to the application of intelligence activities. The systemic failure in terms of organizational centralization and alignment of intent and strategy is a key shortfall that the proposed CINO role is designed to address. The second stage employs the Analytic Hierarchy Process (AHP), to ascertain the organizational strategic motivation for employing a CINO. AHP analysis was conducted with the assistance of eighteen (18) intelligence experts and asses the key criteria, sub-criteria and alternatives associated with the implementation of the CINO role. The results provided clear support for a centralized role led by an intelligence expert, advancing an Embedded Intelligence Officer model that combines the intelligence expertise and global perspectives with Shipping industry deep knowledge. This embedded approach presents to be a pathway for strategic flexibility; as well as enhancing longer-term knowledge transfer, while reducing internal resistance to initiating strategic organizational change.
Option pricing: an empirical evaluation of higher-moment and risk-neutral approaches
(2025-10-06) Tasioudis, Diamantis; Τασιούδης, Διαμαντής; Demos, Antonios; Varthalitis, Petros; Topaloglou, Nikolaos
This thesis examines discrete distribution–based approaches to option pricing, focusing on the comparative performance of three methodologies: the Black–Scholes (BS) model, the Corrado–Su (CS) approach incorporating higher moments such as skewness and kurtosis, and a Risk–Neutral (RN) approach implemented through scenario generation and constrained optimization. The study is motivated by the limitations of the BS model, particularly its assumptions of lognormal asset prices and constant volatility, which have been repeatedly challenged by empirical evidence. By extending the analysis to approaches that incorporate higher moments or risk neutral probabilities, the thesis seeks to assess whether these alternatives provide more accurate and robust pricing methods. The empirical application considers European-style options on the S&P 500 index across two distinct market environments: September 30, 2022, representing a bearish environment characterized by prolonged downturns, and October 31, 2024, reflecting a bullish environment marked by consecutive recovery and optimism. For each regime, estimated prices from the three models were compared against observed market prices for multiple strike prices spanning ITM, ATM, and OTM regions. Model performance was evaluated using mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percentage error (MAPE). The results confirm the limitations of the BS framework. While it provides a solid benchmark and performs reasonably well for ITM options, it systematically underprices OTM options and fails to capture higher-moment effects, particularly in bearish conditions. The CS approach improves pricing accuracy across both regimes, especially near the ATM region, by incorporating skewness and kurtosis. Its effectiveness is most evident under the bearish environment, where return distributions exhibit heavy left tails. However, in the bullish environment, CS occasionally mispriced OTM options, particularly overpricing ITM puts and OTM calls. The RN approach displayed mixed performance, where it underpriced options and delivered higher errors in the bearish environment compared to the other approaches but performed comparatively better in the bullish environment, especially for deep OTM calls and deep ITM puts, where it provided valuations close to market prices. Overall, the findings demonstrate that model choice depends on both market conditions and option moneyness. CS emerges as the most consistent alternative to BS, offering more accurate pricing with modest computational complexity, while the RN approach contributes valuable insights at the extremes of the distribution. The study underscores the need for option pricing models that go beyond the restrictive assumptions of BS and incorporate empirical features of asset returns. By providing a comparative evaluation of discrete distribution–based methodologies, this thesis contributes to the ongoing discussion in both academia and practice regarding optimal approaches for pricing options under different market environments.