Risk Management

Estimating the impact of supply chain network contagion on financial stability ArXiv ID: 2305.04865 “View on arXiv” Authors: Unknown Abstract Realistic credit risk assessment, the estimation of losses from counterparty’s failure, is central for the financial stability. Credit risk models focus on the financial conditions of borrowers and only marginally consider other risks from the real economy, supply chains in particular. Recent pandemics, geopolitical instabilities, and natural disasters demonstrated that supply chain shocks do contribute to large financial losses. Based on a unique nation-wide micro-dataset, containing practically all supply chain relations of all Hungarian firms, together with their bank loans, we estimate how firm-failures affect the supply chain network, leading to potentially additional firm defaults and additional financial losses. Within a multi-layer network framework we define a financial systemic risk index (FSRI) for every firm, quantifying these expected financial losses caused by its own- and all the secondary defaulting loans caused by supply chain network (SCN) shock propagation. We find a small fraction of firms carrying substantial financial systemic risk, affecting up to 16% of the banking system’s overall equity. These losses are predominantly caused by SCN contagion. For every bank we calculate the expected loss (EL), value at risk (VaR) and expected shortfall (ES), with and without accounting for SCN contagion. We find that SCN contagion amplifies the EL, VaR, and ES by a factor of 4.3, 4.5, and 3.2, respectively. These findings indicate that for a more complete picture of financial stability and realistic credit risk assessment, SCN contagion needs to be considered. This newly quantified contagion channel is of potential relevance for regulators’ future systemic risk assessments. ...

Advanced Course in Asset Management (Presentation Slides) ArXiv ID: ssrn-3773484 “View on arXiv” Authors: Unknown Abstract These presentation slides have been written for the Advanced Course in Asset Management (theory and applications) given at the University of Paris-Saclay. They Keywords: Asset Management, Modern Portfolio Theory, Risk Management, Factor Investing, Multi-Asset Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The slides present advanced mathematical theory including Markowitz optimization, CAPM, and Black-Litterman models with quadratic programming formulations and covariance matrix algebra. While it includes tutorial exercises and practice sections, it lacks empirical backtesting data, code implementations, or statistical performance metrics, remaining primarily theoretical and educational. flowchart TD A["Research Goal<br>Modern Asset Management"] --> B["Key Methodology<br>Portfolio Optimization"] B --> C["Data Inputs<br>Market Factors & Risk"] C --> D["Computational Process<br>Factor Analysis & MPT"] D --> E["Key Outcomes<br>Strategic Asset Allocation"] E --> F["Applications<br>Risk-Adjusted Returns"]

Consumer Spending Responses to the COVID-19 Pandemic: An Assessment of Great Britain ArXiv ID: ssrn-3586723 “View on arXiv” Authors: Unknown Abstract Since the first death in China in early January 2020, the coronavirus (COVID-19) has spread across the globe and dominated the news headlines leading to fundame Keywords: COVID-19, Volatility, Market Turbulence, Risk Management, Crisis Economics, Equity Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 9.0/10 Quadrant: Street Traders Why: The paper uses advanced econometric methods (e.g., time-series regressions with fixed effects) but is fundamentally an empirical study relying on a massive proprietary transaction dataset (23 million transactions) to analyze real-world consumer behavior, with no code/backtests presented but heavy data and implementation details. flowchart TD A["Research Goal:<br>Assess UK consumer<br>spending volatility<br>amid COVID-19"] --> B["Data Source:<br>UK Finance Admin Data<br>(n = 70M accounts)"] B --> C["Methodology:<br>Panel Regression &<br>Time-Series Analysis"] C --> D["Computational Process:<br>Compare Pre/Post-<br>Pandemic Spending Trends"] D --> E["Key Finding 1:<br>Immediate spending<br>contraction (Mar 2020)"] D --> F["Key Finding 2:<br>Shift from services<br>to durable goods"] D --> G["Key Finding 3:<br>Volatility spiked;<br>uncertainty persisted"]

How ESG Issues Become Financially Material to Corporations and Their Investors ArXiv ID: ssrn-3482546 “View on arXiv” Authors: Unknown Abstract Management and disclosure of environmental, social and governance (ESG) issues have received substantial interest over the last decade. In this paper, we outlin Keywords: ESG, Sustainable Investing, Corporate Governance, Risk Management, Equity Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper presents a conceptual framework on the pathways of ESG issues becoming financially material, lacking advanced mathematical models or statistical derivations. Empirical evidence is referenced but not derived from original backtests or datasets, relying more on literature review and case studies. flowchart TD A["Research Goal: Determine<br>ESG Financial Materiality"] --> B["Key Methodology:<br>Multi-Industry Regression Analysis"] B --> C{"Data Inputs"} C --> C1["Financial Data:<br>Cost of Equity & ROA"] C --> C2["ESG Scores:<br>Environmental, Social, Governance"] C --> C3["Control Variables:<br>Size, Leverage, Growth"] D["Computational Process:<br>Time-Panel Regression"] --> E["Key Findings/Outcomes"] C1 --> D C2 --> D C3 --> D E --> E1["Sector-Specific Materiality:<br>Varies by Industry"] E --> E2["Strong Governance<br>Universally Reduces Risk"] E --> E3["Low ESG = Higher<br>Cost of Equity Capital"]

Principles of SustainableFinance ArXiv ID: ssrn-3282699 “View on arXiv” Authors: Unknown Abstract Finance is widely seen as an obstacle to a better world. Principles of Sustainable Finance explains how the financial sector can be mobilized to counter this an Keywords: Sustainable Finance, ESG (Environmental, Social, Governance), Impact Investing, Risk Management, Climate Finance, Cross-Asset (Sustainable Investing) Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The text is a conceptual overview of sustainable finance, focusing on economic models, behavioral changes, and policy frameworks, with no advanced mathematical derivations or empirical backtesting evidence presented. flowchart TD A["Research Goal: Mobilize Finance for Sustainability"] --> B["Methodology: ESG Analysis & Risk Management"] B --> C["Data Inputs: Climate Data & Corporate ESG Reports"] C --> D["Computation: Cross-Asset Impact Modeling"] D --> E["Outcome: Sustainable Finance Principles"]

10 Errores frecuentes de algunos Abogados sobre Finanzas y Contabilidad (10 Errors of Lawyers AboutFinanceand Accounting) ArXiv ID: ssrn-2420478 “View on arXiv” Authors: Unknown Abstract Spanish Abstract: Esta nota recoge los10 errores más habituales con los que los autores se han encontrado al tratar con abogados en pleitos, en arbitraje Keywords: Litigation, Arbitration, Legal disputes, Contractual errors, Risk management, Legal/Dispute Resolution Complexity vs Empirical Score Math Complexity: 0.5/10 Empirical Rigor: 0.0/10 Quadrant: Philosophers Why: The paper discusses common financial/accounting misconceptions among lawyers and lacks mathematical formulas, code, or empirical backtesting. It is a descriptive, pedagogical piece with no quantitative modeling. flowchart TD A["Research Goal:<br>Identify common finance/accounting<br>errors by lawyers in disputes"] --> B["Methodology & Inputs:<br>Analyze litigation & arbitration cases<br>with financial arguments"] B --> C["Computational Process:<br>Extract & categorize error patterns<br>from legal/financial documents"] C --> D["Process Validation:<br>Review cases for contractual &<br>risk management implications"] D --> E["Key Outcomes:<br>10 Common Errors Identified<br>(e.g., Misinterpreting Financials,<br>Mixing Law/Accounting Concepts)"]

Putting Integrity into Finance: A Purely Positive Approach ArXiv ID: ssrn-2413334 “View on arXiv” Authors: Unknown Abstract The seemingly never ending scandals in the world of finance with their damaging effects on value and human welfare (that continue unabated in spite of all the v Keywords: Corporate Governance, Finance Scandals, Ethics, Risk Management, Stakeholder Value, General Finance Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper proposes a conceptual, normative theory of integrity with minimal mathematical formalism, relying instead on philosophical and ontological arguments. It explicitly states the lack of large-scale empirical studies and relies on anecdotal feedback, making it neither mathematically dense nor data/implementation-heavy. flowchart TD A["Research Goal: Why do finance scandals persist<br>despite known governance solutions?"] B["Methodology: Purely Positive Approach<br>Analyzes observable behaviors & incentives"] C["Data Inputs: Historical finance scandals<br>Corporate governance records<br>Stakeholder impact reports"] D["Computational Process: Identifying<br>systemic incentive misalignments<br>& integrity gaps"] E["Key Findings: <br>1. Integrity deficit as core risk<br>2. Stakeholder value vs shareholder value<br>3. Need for ethical risk management"]

Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2012 Edition ArXiv ID: ssrn-2027211 “View on arXiv” Authors: Unknown Abstract Equity risk premiums are a central component of every risk and return model in finance and are a key input into estimating costs of equity and capital in both c Keywords: Equity Risk Premium, Cost of Equity, Valuation, Risk Management, Asset Pricing Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 4.0/10 Quadrant: Philosophers Why: The paper focuses on conceptual frameworks, economic determinants, and practical estimation methods (historical, survey, implied) rather than advanced mathematical derivations. It lacks code, backtests, or extensive statistical metrics, emphasizing theoretical discussion and comparison of approaches over empirical implementation. flowchart TD A["Research Goal:<br>Estimate & Analyze ERP for 2012"] --> B{"Methodology"} B --> C["Historical & Survey Data<br>Input: Historical Returns, Risk-free Rates"] B --> D["Computational Process<br>Input: Valuation Multiples & DCF Models"] C --> E["Analysis: Implied ERP<br>Output: Current Market ERP"] D --> E E --> F["Key Outcomes"] F --> G["ERP Sensitivity:<br>Risk aversion & Macro variables"] F --> H["Valuation Impact:<br>Cost of Equity adjustments"]

The New Role of the Corporate Treasurer: Emerging Trends in Response to the Financial Crisis ArXiv ID: ssrn-1971158 “View on arXiv” Authors: Unknown Abstract This paper discusses the role of the modern corporate treasurer in a multinational company and its transformation in response to current challenges companies an Keywords: corporate treasury, multinational finance, cash management, risk management, Corporate Cash Complexity vs Empirical Score Math Complexity: 0.0/10 Empirical Rigor: 0.0/10 Quadrant: Philosophers Why: The paper is a conceptual review and strategic analysis of corporate treasury roles, lacking mathematical formulas, statistical analysis, or backtesting data. It focuses on industry trends and organizational recommendations rather than empirical implementation. flowchart TD A["Research Goal<br>Modernize Corporate Treasury"] --> B["Key Methodology<br>Comparative Analysis & Case Studies"] B --> C["Data Inputs<br>Financial Reports & Interviews"] C --> D["Computation<br>Scenario Simulation & Risk Modeling"] D --> E["Key Findings<br>Cash Management & Risk Mitigation"] E --> F["Outcomes<br>Strategic Treasury Framework"]

Financial Literacy - The Demand Side of Financial Inclusion ArXiv ID: ssrn-1958417 “View on arXiv” Authors: Unknown Abstract Financial literacy has assumed greater importance in recent years especially from 2002 as financial markets have become increasingly complex and the common man Keywords: Financial Literacy, Consumer Finance, Behavioral Finance, Risk Management, Multi-Asset Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a conceptual discussion on financial literacy and inclusion, with no advanced mathematics or quantitative models; empirical work is limited to anecdotal examples and policy references without data analysis or backtesting. flowchart TD A["Research Goal: Assess demand-side factors for financial inclusion"] B["Methodology: Behavioral finance & risk analysis of multi-asset portfolios"] C["Data: Survey data on financial literacy & market complexity trends"] D["Computation: Statistical analysis & asset allocation modeling"] E["Key Findings: Higher literacy increases market participation & risk management"] A --> B B --> C C --> D D --> E