Credit Risk

A Unifying Approach for the Pricing of Debt Securities ArXiv ID: 2403.06303 “View on arXiv” Authors: Unknown Abstract We propose a unifying framework for the pricing of debt securities under general time-inhomogeneous short-rate diffusion processes. The pricing of bonds, bond options, callable/putable bonds, and convertible bonds (CBs) is covered. Using continuous-time Markov chain (CTMC) approximations, we obtain closed-form matrix expressions to approximate the price of bonds and bond options under general one-dimensional short-rate processes. A simple and efficient algorithm is also developed to price callable/putable debt. The availability of a closed-form expression for the price of zero-coupon bonds allows for the perfect fit of the approximated model to the current market term structure of interest rates, regardless of the complexity of the underlying diffusion process selected. We further consider the pricing of CBs under general bi-dimensional time-inhomogeneous diffusion processes to model equity and short-rate dynamics. Credit risk is also incorporated into the model using the approach of Tsiveriotis and Fernandes (1998). Based on a two-layer CTMC method, an efficient algorithm is developed to approximate the price of convertible bonds. When conversion is only allowed at maturity, a closed-form matrix expression is obtained. Numerical experiments show the accuracy and efficiency of the method across a wide range of model parameters and short-rate models. ...

CVA Hedging by Risk-Averse Stochastic-Horizon Reinforcement Learning ArXiv ID: 2312.14044 “View on arXiv” Authors: Unknown Abstract This work studies the dynamic risk management of the risk-neutral value of the potential credit losses on a portfolio of derivatives. Sensitivities-based hedging of such liability is sub-optimal because of bid-ask costs, pricing models which cannot be completely realistic, and a discontinuity at default time. We leverage recent advances on risk-averse Reinforcement Learning developed specifically for option hedging with an ad hoc practice-aligned objective function aware of pathwise volatility, generalizing them to stochastic horizons. We formalize accurately the evolution of the hedger’s portfolio stressing such aspects. We showcase the efficacy of our approach by a numerical study for a portfolio composed of a single FX forward contract. ...

Towards a data-driven debt collection strategy based on an advanced machine learning framework ArXiv ID: 2311.06292 “View on arXiv” Authors: Unknown Abstract The European debt purchase market as measured by the total book value of purchased debt approached 25bn euros in 2020 and it was growing at double-digit rates. This is an example of how big the debt collection and debt purchase industry has grown and the important impact it has in the financial sector. However, in order to ensure an adequate return during the debt collection process, a good estimation of the propensity to pay and/or the expected cashflow is crucial. These estimations can be employed, for instance, to create different strategies during the amicable collection to maximize quality standards and revenues. And not only that, but also to prioritize the cases in which a legal process is necessary when debtors are unreachable for an amicable negotiation. This work offers a solution for these estimations. Specifically, a new machine learning modelling pipeline is presented showing how outperforms current strategies employed in the sector. The solution contains a pre-processing pipeline and a model selector based on the best model calibration. Performance is validated with real historical data of the debt industry. ...

Improved Financial Forecasting via Quantum Machine Learning ArXiv ID: 2306.12965 “View on arXiv” Authors: Unknown Abstract Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we use classical and quantum Determinantal Point Processes to enhance Random Forest models for churn prediction, improving precision by almost 6%. Second, we design quantum neural network architectures with orthogonal and compound layers for credit risk assessment, which match classical performance with significantly fewer parameters. Our results demonstrate that leveraging quantum ideas can effectively enhance the performance of machine learning, both today as quantum-inspired classical ML solutions, and even more in the future, with the advent of better quantum hardware. ...

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. ...

ESG Lending ArXiv ID: ssrn-3865147 “View on arXiv” Authors: Unknown Abstract Firms increasingly borrow via sustainability-linked loans (SLLs), contractually tying spreads to their ESG performance. SLLs vary widely in transparency of disc Keywords: Sustainability-linked loans, ESG performance, Loan spreads, Greenwashing, Credit risk Complexity vs Empirical Score Math Complexity: 4.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper employs advanced econometric methods (DID, PSM, stacked DID, synthetic controls) and handles large datasets (LSEG DealScan, Refinitiv, S&P Trucost) with robustness checks, indicating high empirical rigor, but uses standard statistical models without heavy mathematical derivations. flowchart TD A["Research Goal: How do ESG performance & SLL transparency affect loan spreads & credit risk?"] --> B["Methodology: Empirical analysis of loan contracts"] B --> C["Data: SLLs & ESG data from 2016-2022"] C --> D["Computation: Regression models on spread determinants"] D --> E["Outcomes: Lower spreads for ESG performance, less for opaque SLLs"] E --> F["Risk: No significant credit risk reduction, potential greenwashing"]

Theoretical Review of the Role of Financial Ratios ArXiv ID: ssrn-3472673 “View on arXiv” Authors: Unknown Abstract Purpose – Financial ratios are an instrumental tool in the world of finance and hence comprehensive knowledge of its various aspects is mandated for its user. T Keywords: Financial Ratios, Fundamental Analysis, Credit Risk, Financial Statement Analysis, Solvency, Fixed Income Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a qualitative literature review that discusses historical concepts and applications of financial ratios without presenting novel mathematical derivations, statistical models, or backtesting results. flowchart TD A["Research Goal:<br>Review Financial Ratios' Theoretical Role"] --> B["Key Methodology:<br>Theoretical Review & Analysis"] B --> C["Data/Inputs:<br>Finance Literature & Financial Statements"] C --> D["Computational Processes:<br>Ratio Calculation & Fundamental Analysis"] D --> E["Key Outcomes:<br>Credit Risk, Solvency & Fixed Income Assessment"]

Where Did the Risk Go? How Misapplied Bond Ratings Cause Mortgage Backed Securities and Collateralized Debt Obligation Market Disruptions ArXiv ID: ssrn-1027475 “View on arXiv” Authors: Unknown Abstract Many of the current difficulties in residential mortgage-backed securities (RMBS) and collateralized debt obligations (CDOs) can be attributed to a misapplicati Keywords: Residential Mortgage-Backed Securities, Collateralized Debt Obligations, Misapplication of Models, Credit Risk, Structured Finance, Fixed Income / Structured Credit Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper focuses on conceptual critique and narrative analysis of financial models without introducing advanced mathematics or providing empirical backtests and code. It addresses market mechanisms and regulatory failures, which aligns more with theoretical discussion than quantitative implementation. flowchart TD A["Research Goal<br>Determine why misapplied bond ratings<br>cause RMBS & CDO market disruptions"] --> B{"Key Methodology<br>Literature Review & Case Study<br>on Rating Models"}; B --> C["Data / Inputs<br>Historical RMBS & CDO data<br>Rating agency methodologies"]; C --> D["Computational Process<br>Analysis of model assumptions<br>vs. actual credit risk"]; D --> E{"Key Findings & Outcomes"}; E --> F["Overreliance on flawed models<br>underestimated systemic risk"]; E --> G["Misapplication led to<br>mispriced securities"]; E --> H["Triggered market disruptions<br>in structured finance"];

How and Why Credit Rating Agencies are Not Like Other Gatekeepers ArXiv ID: ssrn-900257 “View on arXiv” Authors: Unknown Abstract This article revisits some issues I raised in a 1999 article on credit rating agencies, which increasingly are the focus of scholars and regulators. I discuss h Keywords: Credit Rating Agencies, Regulatory Reform, Information Asymmetry, Credit Risk, Fixed Income Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is an analytical critique of the credit rating agency business model and regulatory environment, relying on economic theory, legal argument, and historical evidence rather than advanced mathematical modeling or empirical backtesting. flowchart TD A["Research Goal: Why are Credit Rating Agencies unique vs. other gatekeepers?"] --> B["Methodology: Comparative Legal & Economic Analysis"] B --> C["Data: Historical Regulatory Frameworks (1999 vs. Present)"] C --> D["Computational Process: Analyze Information Asymmetry & Liability Structures"] D --> E["Outcome 1: CRA's 'Disseminator' Status (vs. 'Verifier')"] D --> F["Outcome 2: Limited Impact of Standard Liability Regimes"] D --> G["Outcome 3: Unique Regulatory Dependence on CRA Output"]