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.
The paper uses CPI growth rates to improve LGD predictions for CRE loans.
CRE discovers interpretable subgroups with heterogeneous treatment effects.
Survival analysis models predict loan write-off risk under IFRS 9.
Paper calculates loan loss after default using Bayesian model.
A new model calculates LGD distribution based on firm value and credit market conditions.
A new methodology for incorporating LGD correlation effects into the Basel II risk weight functions is introduced. This methodology is based on modelling of LGD and default event with a single loss variable. The resulting formulas for capital charges are numerically compared to the current proposals by the Basel Commit…
LGD algorithm learns optimal hyperparameters for regression tasks.
Proposes a new method for determining LGD discount rates based on cost of capital.
Research improves LGD approximation using XGBoost for cash-flow-limited data.
There is empirical evidence that recovery rates tend to go down just when the number of defaults goes up in economic downturns. This has to be taken into account in estimation of the capital against credit risk required by Basel II to cover losses during the adverse economic downturns; the so-called "downturn LGD" requ…
The purpose of this paper is to identify a relevant statistical correlation between rate of default, RD, and loss given default, LGD, in a major Brazilian financial institution Retail Home Equity exposure rated using the IRB approach, so that we may find a causal relationship between the two risk parameters. Therefore,…
After the release of the final accounting standards for impairment in July 2014 by the IASB, banks will face the next significant methodological challenge after Basel 2. In this paper, first methodological thoughts are presented, and ways how to approach underlying questions are proposed. It starts with a detailed disc…
In this paper, we propose a two-sector Markovian infectious model, which is an extension of Greenwood's model. The central idea of this model is that the causality of defaults of two sectors is in both direction, which enrich dependence dynamics. The Bayesian Information Criterion is adopted to compare the proposed mod…
A censored transformed model for proportional outcomes with boundary mass and an application to loss given default modeling.
Develops a statistical framework for coherent risk estimation.
The utility of Potential Future Exposure (PFE) for counterparty trading limits is being challenged by new market developments, notably widespread regulatory Initial Margin (using 99% 10-day exposure), and netting of trade and collateral flows. However PFE has pre-existing challenges w.r.t. portfolios/distributions, col…
UAV enabled communications and networking can enhance wireless connectivity and support emerging services. However, this would require system-level understanding to modify and extend the existing terrestrial network infrastructure. In this paper, we integrate UAVs both as user equipment and base stations into existing …
Stochastic Gradient Descent or SGD is the most popular optimization algorithm for large-scale problems. SGD estimates the gradient by uniform sampling with sample size one. There have been several other works that suggest faster epoch-wise convergence by using weighted non-uniform sampling for better gradient estimates…
Attributing forecast gaps to component models in complex model suites
The paper tackles attributing forecast gaps in complex model suites.
We consider distributed optimization under communication constraints for training deep learning models. We propose a new algorithm, whose parameter updates rely on two forces: a regular gradient step, and a corrective direction dictated by the currently best-performing worker (leader). Our method differs from the param…
Modeling bank portfolio risk under climate transition impacts.