Technical paper
Looking beyond SA-CCR
An alternative calculation of exposure at default that handles complex portfolios is presented
Forecasting the loss given default of bank loans with a hybrid multilayer LGD model by extending multidimensional signals
The authors employ signaling theory and machine learning methods to investigate loss given default predictions of commercial banks and propose a method to improve the accuracy of these predictions.
Performance validation of representative sample-balancing methods in loan credit-scoring scenarios
The authors validate 12 of the most representative sample-balancing methods used for credit-scoring models, finding that a combined SMOTE and Editor Nearest Neighbor method is optimal.
Scenario design for macrofinancial stress testing
The author presents an empirical approach to scenario design for selecting a stress scenario for international macrofinancial variables and compares this approach with a historical scenario approach.
Modeling maxima with a regime-switching Fréchet model
The authors identify a regime-switching Fréchet model which can be used to identify the behavior of extreme values in financial series.
Assessing systemic fragility: a probabilistic perspective
Using new measure of systemic fragility, the author ranks euro area banks and sovereigns and according to their systemic risk contribution.
Asset allocation with inverse reinforcement learning
Using reinforcement learning to help replicate asset managers' allocation strategy
Falling use of cash and population age structure
The authors investigate the reduction of cash use across 25 countries, using three means of measurement and argue that one method is more appropriate than the others.
Imbalanced data issues in machine learning classifiers: a case study
The author outlines characteristics of machine learning classifiers, compares methods for dealing with imbalanced data issues, and proposes terms of best practice in model development, evaluation, and validation.
Semiparametric GARCH models with long memory applied to value-at-risk and expected shortfall
The authors introduce and apply new semiparametric GARCH models with long memory to obtain rolling one-step ahead forecasts for the value-at-risk and expected shortfall (ES) for market risk assets.
Modeling very large losses. II
This paper presents a means to estimate very large losses by supposing the event is the result of a succession of factors and estimating the probability of each factor.
Vega decomposition for the LV model: an adjoint differentiation approach
Introducing an algorithm for computing vega sensitivities at all strikes and expiries
Enhanced expected impact cost model under abnormally high volatility
The authors extend their impact cost model beyond the typical factors to address the larger transaction costs brought on by stock market crowding effects in times of market turbulence.
Dynamic initial margin estimation based on quantiles of Johnson distributions
The authors compare JLSMC DIM estimates with those produced by two other methods, finding that the JLSMC algorithm is accurate and efficient, producing results comparable with nested Monte Carlo with an order of magnitude less computational effort.
Explainable artificial intelligence for credit scoring in banking
The authors put forward an explainable machine learning model predicting credit default using a real-world data set provided by a Norwegian bank.
Estimating correlation parameters in credit portfolio models under time-varying and nonhomogeneous default probabilities
This paper proposes new maximum likelihood estimation methods that offer greater flexibility than current methods and can account for finite portfolio sizes, scarce default data and time varying, nonhomogeneous default probabilities.
Sovereign probabilities of default in the euro area
This paper decomposes credit default swap spreads of euro area members into their risk premium and default risk elements and forecast one year probabilities of default.
Analytical conversion between implied volatilities based on different dividend models
The authors propose an explicit formula for the conversion of implied volatilities corresponding to dividend modelling assumptions which covers a wide range of strikes and maturities.
The Compliance Index: a behavioral approach to compliance risk management in the (post-) Covid-19 era
This paper proposes the Compliance Index - a behavioral measurement system for controlling and monitoring the effectiveness of compliance programs to mitigate compliance risk - designed in response to the shift to remote working during the Covid-19…
Nonparametric estimation of systemic risk via conditional value-at-risk
The authors propose four new nonparametric estimators of static CoVar and compare their performance in simulation studies.
Energy trading efficiency in ERCOT’s day-ahead and real-time electricity markets
This paper uses hourly prices to study energy trading efficiency in ERCOT's electricity markets and proposes means to improve trading efficiency and investment inventive.
Measuring the effect of corrective short-term updates for wind energy forecasts on intraday electricity prices
This paper investigates the impact of wind energy updates on intraday prices and proposes the use of merit-order-based models to counter price uncertainties stemming from updates.
Risks of long-term auto loans
The authors investigate the borrower risk factors, delinquency rates, yield curves, and interest rates of long-term auto loans.
Forecasting the realized volatility of stock markets with financial stress
This paper investigates the impact of financial stress on the predictability of the realized volatility of five stock markets