Model for corporate bond pricing with credit rating migration, solving a double free boundary problem.
problem Corporate bond pricing with credit rating migration risks.
method Established a pricing model as a double free boundary problem, proving existence, uniqueness, and regularity of the solution.
result Two free boundaries are shown to be smooth and converge to a traveling wave solution as time goes to infinity.
Model credit ratings using economic states with Markov chains.
problem Credit rating migration influenced by economic state changes.
method Developed a Markov chain model for credit ratings conditional on economic states.
result Derived asymptotic behavior of the rating process using Markov theory.
The paper develops ML algorithms for calibrating credit rating transition models for high and low default portfolios.
problem Calibration of credit rating transition models for high and low default portfolios.
method Developed Maximum likelihood (ML) algorithms, including Laplace approximation for high-default portfolios and particle filter with Gaussian process regression for low-default portfolios.
result Both algorithms produce accurate approximations of the likelihood function and ML estimates of model parameters.
Simplified matrix generator resolves credit migration model calibration issues.
problem Fundamental difficulties in calibrating Markovian credit migration models.
method Simplified matrix generator and elementary ideas from differential geometry.
result Risk-neutral calibration requires volatility information and is unstable.
We consider the problem of constructing an appropriate multivariate model for the study of the counterparty credit risk in credit rating migration problem. For this financial problem different multivariate Markov chain models were proposed. However the markovian assumption may be inappropriate for the study of the dyna…
The paper uses filtering techniques to predict rating transitions.
problem Analyzing the effect of business cycles on rating transitions.
method Point process filtering framework to infer latent factor states.
result Efficient estimation of latent factor parameters for real-time detection of economic changes.
We present two methodologies on the estimation of rating transition probabilities within Markov and non-Markov frameworks. We first estimate a continuous-time Markov chain using discrete (missing) data and derive a simpler expression for the Fisher information matrix, reducing the computational time needed for the Wald…
Method determines credit transition matrix from cumulative default probabilities.
problem Quantifying changes in bond credit ratings.
method Setup an ill-posed, linear inverse problem with entropy minimization.
result Method successfully determines CTM from cumulative default probabilities.
In this paper we discuss the issue of computation of the bilateral credit valuation adjustment (CVA) under rating triggers, and in presence of ratings-linked margin agreements. Specifically, we consider collateralized OTC contracts, that are subject to rating triggers, between two parties -- an investor and a counterpa…
Copula models for sovereign ratings improved by incorporating climate risk.
problem Modeling nonlinear dependence and clustering in sovereign rating migrations.
method Mixed-difference transformation, MAGMAR(1,1) copula process, consistent and asymptotically normal estimators.
result Gumbel MAGMAR(1,1) specification outperforms other models in empirical performance.
Develops a new model to better predict corporate bond yields.
problem Persistent shifts in interest rates undermine single-regime models.
method Regime-switching generalized CIR model with two-state short-rate process and credit factors.
result The model improves joint curve fit and delivers interpretable probabilities.
We propose a dynamic model of dependence structure between financial institutions within a financial system and we construct measures for dependence and financial instability. Employing Markov structures of joint credit migrations, our model allows for contagious simultaneous jumps in credit ratings and provides flexib…
Quantum MC simulations generate financial risk distributions efficiently.
problem High computational cost in traditional Monte Carlo simulations.
method Integrates quantum amplitude estimation with stochastic models for equity, rate, and credit risk factors.
result Quantum advantage in scenario generation for financial risk analytics.
In banking practice, rating transition matrices have become the standard approach of deriving multi-year probabilities of default (PDs) from one-year PDs, the latter normally being available from Basel ratings. Rating transition matrices have gained in importance with the newly adopted IFRS 9 accounting standard. Here,…
We introduce a dynamic model of the default waterfall of derivatives CCPs and propose a risk sensitive method for sizing the initial margin (IM), and the default fund (DF) and its allocation among clearing members. Using a Markovian structure model of joint credit migrations, our evaluation of DF takes into account the…
This paper presents two cases of random banking data generators based on migration matrices and scoring rules. The banking data generator is a new hope in researches of finding the proving method of comparisons of various credit scoring techniques. There is analyzed the influence of one cyclic macro--economic variable …
The paper examines how decentralized credit curators have taken over risk management from traditional protocols.
problem Risk management in decentralized credit has shifted from centralized protocols to decentralized curators.
method Analysis of ERC 4626 vaults and third-party curators, focusing on capital utilization, concentration, and fee margins.
result Curators have a significant impact on the risk profile of decentralized credit systems, with a small set of curators handling a disproportionate share of system TVL.
Solves curve migration problem with elastic flows.
problem Curve migration problem with natural boundary conditions.
method Constructing migrating elastic flows.
result Extends previous work to purely local flow.
This paper uses PCA and FA for feature selection in credit rating.
problem Selecting important features for credit rating prediction.
method Principal Component Analysis and Factor Analysis.
result Factor Analysis reduces feature set significantly without losing much accuracy.
CCR-CNN uses CNN to predict corporate credit ratings from financial data.
problem Lack of data and limited model performance in predicting corporate credit ratings.
method Transform corporations into images and use CNN to analyze complex feature interactions.
result CCR-CNN outperforms state-of-the-art methods in predicting corporate credit ratings.
Framework integrates financial and annual report data for better corporate credit ratings.
problem Lack of insights from non-financial data in credit rating models.
method Uses FinBERT to extract features from annual reports and combines them with financial data.
result Improves credit rating accuracy by 8-12%.
This paper explores the relationships between migration and trade using a complex-network approach. We show that: (i) both weighted and binary versions of the networks of international migration and trade are strongly correlated; (ii) such correlations can be mostly explained by country economic/demographic size and ge…
Study finds implicit government guarantee improves municipal investment bond ratings.
problem Questioning the objectivity of municipal investment bond ratings due to implicit government guarantee.
method Text mining of policy documents and PMC index model for implicit guarantee strength calculation.
result Implicit government guarantee boosts municipal investment bond ratings, especially in less developed regions.
Bayesian and simulation methods predict credit default probabilities.
problem Assessing credit risk in large customer portfolios.
method Two-phase approach: Bayesian estimation followed by Monte Carlo simulations.
result Estimation of true default rates through simulations.
Study on migrating elastic flows of curves across half-planes.
problem Migrating elastic flows of curves from upper to lower half-planes.
method Analytical and numerical construction of migrating elastic flows.
result Construction of various migrating elastic flows.
This study uses machine learning to predict sovereign credit ratings and identifies key factors.
problem Predicting sovereign credit ratings and identifying important factors.
method Used Multilayer Perceptron (MLP), Classification and Regression Trees (CART), Support Vector Machines (SVM), Naïve Bayes (NB), and Ordered Logit (OL) models.
result MLP is the best model for predicting sovereign credit ratings with a 68% accuracy.
Knowing and modelling the migration phenomena and especially the social and economic consequences have a theoretical and practical importance, being related to their consequences for development, economic progress (or as appropriate, regression), environmental influences etc. One of the causes of migration, especially …
Credit risk management in Italy is characterized, in the period June 2008 to June 2012, by frequent (frequency=0.5 cycles per year) and intense (peak amplitude: mean=39.2 billion Euros, s.e.=2.83 billion Euros) quarterly contractions and expansions around the mean (915.4 billion Euros, s.e.=3.59 billion Euros) of the n…
This study compares neural networks, SVM, and decision trees for corporate credit rating predictions.
problem Predicting corporate credit ratings using machine learning methods.
method Applied four machine learning techniques (Bagged Decision Trees, Random Forest, SVM, MLP) to credit rating datasets.
result Decision tree-based models outperformed other techniques in terms of 'Notch Distance' measure.
In this paper we develop a methodology to analyze and compare multiple global networks. We focus our analysis on the relation between human migration and trade. First, we identify the subset of products for which the presence of a community of migrants significantly increases trade intensity. To assure comparability ac…
Study finds no significant impact of US sovereign credit rating downgrade on equity market.
problem Impact of US sovereign credit rating downgrade on US equity market.
method Event study methodology using three companies and S&P500 index.
result No significant effects of US sovereign credit rating downgrade on US equity market.
Survey examines machine learning for credit rating predictions.
problem Manual loan approvals are slow and error-prone.
method Examines sentiment analysis techniques in credit rating.
result Machine learning improves credit rating predictions.
AI improves credit rating predictions over traditional methods.
problem Improving credit rating predictions for global corporate entities.
method Applying deep learning techniques, specifically neural networks with categorical embeddings, to a large dataset of corporate obligations.
result Deep learning models achieve adequate accuracy in predicting different credit rating classes.
This paper develops a machine learning model to assess credit risk in UAE commercial banks.
problem Lack of precision in conventional credit rating tools for accurate credit risk prediction.
method Constructs a credit risk assessment model using Linear Discriminant Analysis.
result Demonstrates improved accuracy in predicting good and bad creditors compared to conventional methods.
A market with defaultable bonds where the bond dynamics is in a Heath-Jarrow-Morton setting and the forward rates are driven by an infinite number of Levy factors is considered. The setting includes rating migrations driven by a Markov chain. All basic types of recovery are investigated. We formulate necessary and suff…
Model estimates LIBOR rates and finds COVID-19 spread spike due to credit risk.
problem Estimating LIBOR rates and understanding the factors affecting them.
method Developed a joint model for various LIBOR-related rates and used it to decompose spreads.
result Credit risk mainly caused the spike in LIBOR-OIS spread during the COVID-19 onset, with equal contributions from credit and funding-liquidity risks on average.
This paper examines how ESG factors influence sovereign bond yields and credit ratings.
problem The impact of ESG factors on sovereign bond yields and credit ratings is not fully understood.
method The study identifies relevant ESG indicators and compares their importance in bond pricing and credit ratings.
result ESG factors, particularly the G and S pillars, are more important for credit ratings than the E pillar.
Population migration is valuable information which leads to proper decision in urban-planning strategy, massive investment, and many other fields. For instance, inter-city migration is a posterior evidence to see if the government's constrain of population works, and inter-community immigration might be a prior evidenc…
Study evaluates neural networks for corporate credit rating assessment.
problem Improving machine learning algorithms for credit assessment.
method Analysis of four neural network architectures (MLP, CNN, CNN2D, LSTM) on financial data from energy, financial, and healthcare sectors.
result LSTM architecture consistently outperforms others in predicting corporate credit ratings.
LSTM outperforms traditional models in forecasting international migration.
problem Precise forecasting of international migration for policymaking.
method Replaced a gravity linear model with an LSTM approach using Google Trends data.
result LSTM approach combined with Google Trends data outperforms existing models.
Paper analyzes deep learning models for credit rating prediction using text and numerical data.
problem Improving credit rating prediction using multi-modal deep learning.
method Testing different deep learning models and fusion strategies for structured and unstructured datasets.
result CNN-based multi-modal model with two fusion strategies outperformed other models.
We explicitly test if the reliability of credit ratings depends on the total number of admissible states. We analyse open access credit rating data and show that the effect of the number of states in the dynamical properties of ratings change with time, thus giving supportive evidence that the ideal number of admissibl…
The AAA credit rating may have been overly precise given available data.
problem The feasibility of achieving high reliability targets for structured credit products.
method Bayes' theorem and historical data analysis.
result High reliability targets for structured products require substantial statistical discrimination, which was not achievable with available data.
Traditional methods outperform LLMs in forecasting corporate credit ratings.
problem Forecasting corporate credit ratings using LLMs.
method Comparison of traditional methods (XGBoost) and LLMs (LLaMA) on credit rating forecasting.
result XGBoost outperforms LLMs in forecasting corporate credit ratings.
This work aims mainly to present a project of research about the identification of the determinants that affect the mobility of labor. The empirical part of the work will be performed for the NUTS II and NUTS III of Portugal, from 1996 to 2002 and for 1991 and 2001, respectively (given the availability of statistical d…
This paper improves credit risk analysis by incorporating state-dependent recovery rates into a factor model.
problem Accurate default forecasting in credit risk analysis.
method Extends a one-factor Gaussian copula model to include state-dependent recovery rates and a common factor.
result The proposed model outperforms other models in default prediction, especially during hectic periods.
In this paper, we have discussed initial findings and results of our experiment to predict sexual and reproductive health vulnerabilities of migrants in a data-constrained environment. Notwithstanding the limited research and data about migrants and migration cities, we propose a solution that simultaneously focuses on…
A simplified model for fixed income portfolio optimisation.
problem Modeling interest rates and credit risk in fixed income portfolios.
method Proposes a two-factor model for the time evolution of the efficient frontier.
result The efficient frontier is mainly controlled by linear constraints, with standard deviation less important.