Abstract The Hull-White binomial approach to pricing Executive Stock Options (ESOs) offers a practical framework that accounts for important features like vesting, early exercise, and forfeiture-elements not captured by traditional Black–Scholes methods. However, a known weakness of the Hull-White approach is its slow convergence, which can limit its usability in applied settings. This paper introduces enhanced versions of the Hull-White model, drawing on improvements proposed by Boyle-Lau and Tian, to speed up convergence when barrier-like features are present. These refinements, referred to as Hull-White-Boyle-Lau and Hull-White-Tian enable faster, more accurate valuations that are easier to audit and reproduce. In addition to improving computational performance, we move beyond the standard fixed-rule approach by allowing early exercise to be endogenously determined. In high-dividend regimes, where early exercise becomes economically rational, the HWBL and HWTian models can efficiently capture the optimal early exercise point. This may assist practitioners when rationalizing to peers the selection of parameter inputs. The enhanced models align well with current accounting standards in the US, Europe, and China, and can support auditors and other professionals working within evolving regulatory frameworks globally.

Abstract Investment analysis requires frameworks that combine theoretical rigor with real-world complexity. While financial mathematics offers powerful tools, its reliance on simplifying assumptions may limit practical applicability. Conversely, accounting and corporate finance provide transaction-level detail and managerial insight, but their integration with financial mathematics remains underexplored. This paper introduces a unifying framework, presenting foundational insights and architectural principles that integrate these disciplines through two universal concepts: the law of motion (governing capital dynamics) and the law of conservation (ensuring equilibrium). These principles, foundational in economics, converge in the Split Screen Matrix, a novel architecture that reveals connections across financial mathematics, accounting, and corporate finance. The Split Screen Matrix uncovers the mathematical linkage between financial statements, integrating balance sheets, income statements, and cash flow statements (traditionally treated in isolation) and shows how these connections mirror market-traded portfolios to capture economic profitability. It also acts as a diagnostic device, allowing validation of models and detection of internal inconsistencies. This interdisciplinary approach demonstrates that cash flows, incomes, and capital amounts are interdependent variables governed by these laws and must be modeled jointly. This integration unifies financial planning, valuation, and decision making, resolving theoretical gaps. Researchers can align their models with accounting and corporate finance principles, clarifying interdisciplinary relationships and exploring new research avenues. Pedagogically, the framework supports student understanding by reducing cognitive load and integrating traditionally fragmented areas of financial analysis into a unified paradigm that mirrors real-world transactions. This approach advances theory and practice, offering practitioners advanced tools, researchers new exploration avenues, and educators innovative teaching strategies, thereby reflecting AMASES’s enduring mission to bridge theory and practice, dissolve disciplinary boundaries, and illuminate economic realities through mathematics.

Abstract We investigate the role of climate risk in the Eurozone sovereign debt market, to evaluate the current pricing of different risk factors in the government spreads of each country. Particular attention is paid to differences between green and non-green bonds, in terms of reactions to climate risk. Weather variables are selected in line with the guidelines of Eurozone climate stress tests, and taken as proxies for acute and chronic physical risk, while EU carbon allowances are included to capture transition risk. Their significance is studied as potential drivers of mean spread variations and for their co-movement with spreads in the case of extreme events, to provide insights for climate risk management and financial policy, with the goal of enhancing the resilience and stability of the financial system. From both analyses, the same results emerge: climate risk is being priced by the market, but differently depending on the country, and green government bonds from different countries have a divergent reaction to climate risk factors. Dutch and French green spreads closely mirror their traditional counterparts, while German, Italian, and Spanish green bonds display lower reactivity. Various explanations are considered, including a “scarcity effect” linking the behavior of green bonds to their abundance relative to total outstanding government debt. Finally, the most relevant risk factors of each country are highlighted, comparing the results to known climate change challenges.

Abstract This paper presents a groundbreaking integration of Multiple Criteria Decision Making (MCDM) with explainable artificial intelligence (XAI) for tourism accommodation performance assessment, addressing fundamental limitations in traditional preference elicitation methods. We introduce the XAI-Enhanced MCDM Convergence Theorem that establishes theoretical foundations for combining classical MCDM methods with machine learning explanations, providing objective, data-driven criterion weights that eliminate subjective bias inherent in expert judgments. Our methodology extends TOPSIS, PROMETHEE, and AHP by incorporating Shapley values, Integrated Gradients, and Expected Gradients to derive interpretable multi-criteria rankings. Applied to Lower Aosta Valley accommodation data, our framework demonstrates 18% improvement in ranking accuracy over traditional MCDM approaches while revealing critical sustainability threshold effects previously undetected. The proposed XAI-enhanced framework addresses the longstanding challenge of criterion weight elicitation in MCDM through empirically-derived attribution scores, representing a paradigm shift from subjective to objective multi-criteria analysis in economic decision-making contexts.