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.
Study shows economic downturn increases default rates for Moroccan banks.
Model predicts loan default risk using dynamic multilayer graph neural networks.
Systemic risks of default contagion in the Russian interbank market are investigated. The analysis is based on considering the bow-tie structure of the weighted oriented graph describing the structure of the interbank loans. A probabilistic model of interbank contagion explicitly taking into account the empirical bow-t…
Intuitively, the default risk of a single borrower is higher when her or his assets and debt are denominated in different currencies. Additionally, the default dependence of borrowers with assets and debt in different currencies should be stronger than in the one-currency case. By combining well-known models by Merton …
LIBOR-linked borrowing exposes venture banks to systemic risk without improving profitability.
This paper builds a recommendation system for borrowers on P2PL platforms to lower interest rates.
The writers propose a mathematical Method for deriving risk weights which describe how a borrower's income, relative to their debt service obligations (serviceability) affects the probability of default of the loan. The Method considers the borrower's income not simply as a known quantity at the time the loan is made, …
Paper calculates loan loss after default using Bayesian model.
Paper uses BERT to assess P2P borrowers' credit risk from loan descriptions.
This paper uses LSTM to predict P2P lending default rates, improving accuracy with macroeconomic data.
The authors examine the concept of probability of default for asset-backed loans. In contrast to unsecured loans it is shown that probability of default can be defined as either a measure of the likelihood of the borrower failing to make required payments, or as the likelihood of an insufficiency of collateral value on…
Workflow improves credit default prediction using machine learning.
The article explains the probabilistic method of default probability estimation by Pluto and Tasche.
For credit risk management purposes in general, and for allocation of regulatory capital by banks in particular (Basel II), numerical assessments of the credit-worthiness of borrowers are indispensable. These assessments are expressed in terms of probabilities of default (PD) that should incorporate a certain degree of…
Model assesses loan profitability under changing credit conditions.
New method estimates corporate default probabilities using indirect data.
The study calculates securities lending haircuts and indemnification costs.
Study optimizes interbank lending and borrowing to reduce systemic risk.
In our model, private actors with interbank cash flows similar to, but nore general than (Carmona, Fouque, Sun, 2013) borrow from the outside economy at a certain interest rate, controlled by the central bank, and invest in risky assets. Each private actor aims to maximize its expected terminal logarithmic utility. The…
Credit scores misclassify borrowers, especially minorities, leading to inequitable access.
We propose a model of inter-bank lending and borrowing which takes into account clearing debt obligations. The evolution of log-monetary reserves of banks is described by coupled diffusions driven by controls with delay in their drifts. Banks are minimizing their finite-horizon objective functions which take into a…
New approach predicts microfinance borrower risk to manage portfolios.
We propose a simple model of inter-bank borrowing and lending where the evolution of the log-monetary reserves of banks is described by a system of diffusion processes coupled through their drifts in such a way that stability of the system depends on the rate of inter-bank borrowing and lending. Systemic risk is ch…
New mortgage contracts reduce underwater default by adjusting loan balances, but must balance prepayment incentives.
The existence of asymmetric information has always been a major concern for financial institutions. Financial intermediaries such as commercial banks need to study the quality of potential borrowers in order to make their decision on corporate loans. Classical methods model the default probability by financial ratios u…
During the last two years, Europe has been facing a debt crisis, and Greece has been at its center. In response to the crisis, drastic actions have been taken, including the halving of Greek debt. Policy makers acted because interest rates for sovereign debt increased dramatically. High interest rates imply that defaul…
The paper explores how the probability of default estimation changes with temporal correlation decay.
Study uses AI to refine loan assessments, improving credit default predictions.
Machine learning outperforms crowd investors in predicting loan defaults and investment returns.
FSL-BDP models time-to-default without centralizing data, improving privacy mechanisms in federated settings.
Mobile phone data predicts loan repayment risk.
The events of the last few years revealed an acute need for tools to systematically model and analyze large financial networks. Many applications of such tools include the forecasting of systemic failures and analyzing probable effects of economic policy decisions. We consider optimizing the amount and structure of a b…
Within the framework of maximum entropy principle we show that the finite-size long-range Ising model is the adequate model for the description of homogeneous credit portfolios and the computation of credit risk when default correlations between the borrowers are included. The exact analysis of the model suggest that w…
Horseshoe priors improve small area estimation by borrowing strength globally but locally.
Study interbank lending and borrowing dynamics with heterogeneous mean field model.
A new credit scoring method using Gaussian Mixture Models.
Paper examines constraints on cryptocurrency networks to improve liquidity and capital costs.
The DebtRank algorithm has been increasingly investigated as a method to estimate the impact of shocks in financial networks, as it overcomes the limitations of the traditional default-cascade approaches. Here we formulate a dynamical "microscopic" theory of instability for financial networks by iterating balance sheet…
Model assesses credit risk using behavioral data from Experian and Bank of Italy.
We develop a novel framework for computing the total valuation adjustment (XVA) of a European claim accounting for funding costs, counterparty credit risk, and collateralization. Based on no-arbitrage arguments, we derive the nonlinear backward stochastic differential equations (BSDEs) associated with the replicating p…
In his book with Alan Jolis, Vers un monde sans pauvreté (1997) Yunus gives the example of a microcredit loan of 1000BDT reimbursed via 50 weekly settlements of 22BDT and correctly claims that this corresponds to the annual interest rate of 20%. But this is without taking into account that if the borrower has good reas…
Study identifies two borrowing patterns in UK payday loan users.
This study compares logistic regression and XGBoost for predicting credit risk.
Proposes dynamic borrowing method for historical data in clinical trials.
Study optimal strategies under uncertain market parameters and borrowing costs.
Paper models financial contagion with fire sales and borrowing.
A repurchase agreement lets investors borrow cash to buy securities. Financier only lends to securities' market value after a haircut and charges interest. Repo pricing is characterized with its puzzling dual pricing measures: repo haircut and repo spread. This article develops a repo haircut model by designing haircut…
Threadneedle is a multi-agent simulation framework, based on a full double entry book keeping implementation of the banking system's fundamental transactions. It is designed to serve as an experimental test bed for economic simulations that can explore the banking system's influence on the macro-economy under varying a…