The impacts of financial and macroeconomic factors on financial stability in emerging countries: evidence from Turkey’s nonperforming loans
The authors assess the impacts of financial and macroeconomic factors on financial stability in emerging economies, using Turkey's banking sector in the period 2005 Q1 to 2020 Q3 as their example.
Asymmetric risk spillovers between oil and the Chinese stock market: a Beta-skew-t-EGARCH-EVT-copula approach
The author uses the marginal expected shortfall method alongside the Beta-skew-t-exponential generalized autoregressive conditional heteroscedasticity-extreme value theory model and the CoVaR model to investigate risk spillover between the crude oil…
The authors offer a VIX pricing algorithm for stochastic Volterra rough volatility models where the volatility is dependent of the vol-of-vol which reproduces key features of real-world data.
The authors investigate the pricing of options using an EP-EL approach, finding that this methodology generates large amounts of useful information for option traders.
The authors examine the All-Weather portfolio in relation to other popular portfolios and investigate the impact of various static and dynamic portfolio-rebalancing strategies on the All-Weather portfolio.
The authors summarise ways that machine learning can help categorize textual descriptions of operational loss events into Basel II event types.
The author investigates the Royal Commission into Misconduct in the Banking, Superannuation and Financial Services Industry and its most prominent cases, as well as detailing examples of operational risk events that the commission did not cover.
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.
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.
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.
The authors identify a regime-switching Fréchet model which can be used to identify the behavior of extreme values in financial series.
Using new measure of systemic fragility, the author ranks euro area banks and sovereigns and according to their systemic risk contribution.
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.
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.
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.
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.
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.
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.
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.
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.
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…
The authors propose four new nonparametric estimators of static CoVar and compare their performance in simulation studies.