A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Model for corporate bond pricing with credit rating migration, solving a double free boundary problem.
Model credit ratings using economic states with Markov chains.
The paper develops ML algorithms for calibrating credit rating transition models for high and low default portfolios.
Simplified matrix generator resolves credit migration model calibration issues.
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.
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.
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.
Develops a new model to better predict corporate bond yields.
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.
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 …
Solves curve migration problem with elastic flows.
The paper examines how decentralized credit curators have taken over risk management from traditional protocols.
This paper uses PCA and FA for feature selection in credit rating.
CCR-CNN uses CNN to predict corporate credit ratings from financial data.
Framework integrates financial and annual report data for better corporate credit ratings.
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.
Bayesian and simulation methods predict credit default probabilities.
Study on migrating elastic flows of curves across half-planes.
This study uses machine learning to predict sovereign credit ratings and identifies key factors.
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.
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.
Survey examines machine learning for credit rating predictions.
This paper develops a machine learning model to assess credit risk in UAE commercial banks.
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.
This paper examines how ESG factors influence sovereign bond yields and credit ratings.
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.
LSTM outperforms traditional models in forecasting international migration.
Paper analyzes deep learning models for credit rating prediction using text and numerical data.
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.
Traditional methods outperform 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.
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.
We review different approaches for measuring the impact of liquidity on CDS prices. We start with reduced form models incorporating liquidity as an additional discount rate. We review Chen, Fabozzi and Sverdlove (2008) and Buhler and Trapp (2006, 2008), adopting different assumptions on how liquidity rates enter the CD…