Journal of Credit Risk
ISSN:
1744-6619 (print)
1755-9723 (online)
Editor-in-chief: Linda Allen and Jens Hilscher
Volume 19, Number 3 (September 2023)
Editor's Letter
Lewis O’Sullivan
Managing Editor, Risk Journals
On behalf of the board and the journals team, I am delighted to announce that Professor Jens Hilscher has taken over as Co-Editor-in-Chief of The Journal of Credit Risk.
Jens has been an Associate Editor with the journal for several years and he is therefore familiar with its workings and readership. He brings with him a wealth of knowledge, and we are pleased to report that he has already put in a great deal of work over the past few months to ensure a smooth transition.
Jens is currently a professor at the University of California, Davis. He holds a PhD in economics from Harvard University and a BSc and an MSc from the London School of Economics. His research has investigated the determinants of both firmand country-level credit risk, the pricing and returns of distressed securities, information about future default and systematic risk in credit ratings, information flows between credit default swap and equity markets, the measurement of possible debt deflation using inflation derivatives, and corporate bond pricing.
Jens will be working closely with his fellow Co-Editor-in-Chief Professor Linda Allen.
We would also like to take this opportunity to thank Nikunj Kapadia for his efforts with the journal over the past three years. This period has seen the journal go from strength to strength, thanks in no small part to Nikunj’s diligence and hard work.We are sure you will join us in wishing Nikunj all the best with his future endeavours.
We look forward to the journal’s continued development and growth under the new editorial team.
Papers in this issue
Emulating the Standard Initial Margin Model: initial margin forecasting with a stochastic cross-currency basis
The authors propose a stochastic cross-currency basis model extension to resolve the impact of missing risk factors when estimating initial margin and margin valuation adjustments in cross-currency basis swaps.
Pricing default risk in stochastic time
This paper explores credit derivative pricing through the structural modeling framework and seeks to improve on how accurately such models value derivative securities.
Default forecasting based on a novel group feature selection method for imbalanced data
The authors construct a group feature selection method which combines optimal instance selection with weighted comprehensive precision in an effort to improve the performance of prediction models in relation to defaulting firms.
Understanding and predicting systemic corporate distress: a machine-learning approach
The authors construct a machine-learning-based early-warning system to predict, one year in advance, risks of systemic distress and demonstrate factors which can predict corporate distress.
