arXiv research

Credit expansion led to stronger household leverage cycles during the U.S. business cycle.

The proposed model is aimed to reveal important patterns in the behavior of a simplified financial system. The patterns could be detected as regular cycles consisting of debt bubbles and crises. Financial cycles have a well defined structure and form periodic sequences along the axis of credit expansion while retaining…

Unified theory explains housing cycle across metros, showing credit expansion impacts.

Study shows credit expansion in mortgage markets influenced U.S. business cycle.

Study finds public procurement awards, especially NGEU-funded ones, boost new lending.

Approximates discounted moments for financial products using polynomial expansions.

With negative growth in real production in many countries and debt levels which become an increasing burden on developed societies, the calls for a change in economic policy and even the monetary system become louder and increasingly impatient. We research the consequences of a system of credit and debt, that still all…

Let $(X_t)_{t\ge0}$ be a continuous-time, time-homogeneous strong Markov process with possible jumps and let $τ$ be its first hitting time of a Borel subset of the state space. Suppose $X$ is sampled at random times and suppose also that $X$ has not hit the Borel set by time $t$. What is the intensity process of $τ$ ba…

We introduce closed-form transition density expansions for multivariate affine jump-diffusion processes. The expansions rely on a general approximation theory which we develop in weighted Hilbert spaces for random variables which possess all polynomial moments. We establish parametric conditions which guarantee existen…

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…

Study uses generative models to assess credit risk and determine loan sizes in e-commerce supply chain finance.

We develop an efficient method to calibrate CDS spreads using asymptotic approximations.

The importance of adequately modeling credit risk has once again been highlighted in the recent financial crisis. Defaults tend to cluster around times of economic stress due to poor macro-economic conditions, {\em but also} by directly triggering each other through contagion. Although credit default swaps have radical…

The classical reduced-form and filtration expansion framework in credit risk is extended to the case of multiple, non-ordered defaults, assuming that conditional densities of the default times exist. Intensities and pricing formulas are derived, revealing how information driven default contagion arises in these models.…

Model explains stock price bubbles through debt crises and financial crashes.

We consider the problem of computing the Credit Value Adjustment ({CVA}) of a European option in presence of the Wrong Way Risk ({WWR}) in a default intensity setting. Namely we model the asset price evolution as solution to a linear equation that might depend on different stochastic factors and we provide an approxima…

We derive a semi-analytic formula for the transition probability of three-dimensional Brownian motion in the positive octant with absorption at the boundaries. Separation of variables in spherical coordinates leads to an eigenvalue problem for the resulting boundary value problem in the two angular components. The main…

Paper introduces a new method for efficient portfolio risk quantification.

Paper improves CDO calibration using Magnus Expansion and Deep Learning.

The Basel II internal ratings-based (IRB) approach to capital adequacy for credit risk implements an asymptotic single risk factor (ASRF) model. Measurements from the ASRF model of the prevailing state of Australia's economy and the level of capitalisation of its banking sector find general agreement with macroeconomic…

In this paper, we study the non-linear diffusion equation associated with a particle system where the common drift depends on the rate of absorption of particles at a boundary. We provide an interpretation as a structural credit risk model with default contagion in a large interconnected banking system. Using the metho…

Study improves fraud detection in e-commerce with a stacked model combining CNNs, GNNs, and confidence gating.

Study shows how to better estimate credit provisions and economic capital.

Study shows how macroprudential policies affect credit growth in Israel, especially in housing and business sectors.

Unified view on selective credit assignment for reinforcement learning.

We apply Geometric Arbitrage Theory to obtain results in mathematical finance for credit markets, which do not need stochastic differential geometry in their formulation. We obtain closed form equations involving default intensities and loss given defaults characterizing the no-free-lunch-with-vanishing-risk condition …

This paper develops a machine learning model to assess credit risk in UAE commercial banks.

Study evaluates SHAP for credit card default model consistency.

Large corporate credit models may be adapted for small business risk assessment.

Credit scores misclassify borrowers, especially minorities, leading to inequitable access.

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…

Extracts credit-relevant information from earnings calls.

Bayesian and simulation methods predict credit default probabilities.

This work addresses the problem of optimal pricing and hedging of a European option on an illiquid asset Z using two proxies: a liquid asset S and a liquid European option on another liquid asset Y. We assume that the S-hedge is dynamic while the Y-hedge is static. Using the indifference pricing approach we derive a HJ…

Paper simplifies default process modeling and credit valuation.

Study optimizes classifiers for credit card mail campaigns and default prediction.

CERM calculates climate risks in bank loans.

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…

We present a dialogue on Counterparty Credit Risk touching on Credit Value at Risk (Credit VaR), Potential Future Exposure (PFE), Expected Exposure (EE), Expected Positive Exposure (EPE), Credit Valuation Adjustment (CVA), Debit Valuation Adjustment (DVA), DVA Hedging, Closeout conventions, Netting clauses, Collateral …

Method debiases alternative data for fair credit underwriting.

We consider a structural credit model for a large portfolio of credit risky assets where the correlation is due to a market factor. By considering the large portfolio limit of this system we show the existence of a density process for the asset values. This density evolves according to a stochastic partial differential…

A new model uses a Levy-driven process to value credit index swaptions.

Model assesses credit risk using behavioral data from Experian and Bank of Italy.

This paper examines the possibility of using derivative-implied risk premia to explain stock returns. The rapid development of derivative markets has led to the possibility of trading various kinds of risks, such as credit and interest rate risk, separately from each other. This paper uses credit default swaps and equi…

The paper analyzes Lending Club's loan applicants to predict default risk.

CCR-CNN uses CNN to predict corporate credit ratings from financial data.

This paper builds a machine learning model to predict credit defaults for unsecured lending.

Derives metrics for DeFi vaults, addressing credit risk.