Original research
Model risk quantification for machine learning models in credit risk
This paper analyses bank-specific model risk measurement methods with a focus on implemented model risk rating solutions for MLMs and discusses challenges faced by the validation function.
Research on the dynamic early warning effect on the manufacturing industry from the perspective of systemic financial risks: evidence from the Chinese market
A three-stage fusion model for predicting financial distress considering semantic and sentiment information
Randomization of spectral risk measures and distributional robustness
How confident are we of margin model procyclicality measurements?
This paper investigates procyclicality models, bringing attention to that fact that typical measures of model responsiveness are random variables and impacted by uncertainty.
Convexity adjustments à la Malliavin
This paper puts forward a novel means to approximate convexity adjustments in a general interest rate model using Malliavin calculcus.
An explicit scheme for pathwise cross valuation adjustment computations
The authors put forward a simulation/regression scheme for a class of anticipated backward stochastic differential equations, where the coefficient entails a conditional expected shortfall of the martingale part of the solution.
The fate of zombie firms: prediction, determinants and exit paths
This paper examines how machine learning and statistical methods may be used to predict whether or not zombie firms will escape their fate as zombies.
Enhancing default prediction in alternative lending: leveraging credit bureau data and machine learning
The authors apply machine learning techniques to credit bureau data and loan-specific variables to improve default prediction in the alternative lending sector.
Overcoming issues with time-scaling value-at-risk
The authors investigate the impact of different time-scaling techniques on the accuracy of value-at- risk models, emphasising the importance of carefully scaling methods and considering alternative risk modeling approaches.
Disaster insurance swaps
This paper proposes a novel contract, called disaster insurance swaps, to help insurance and reinsurance firms hedge extreme-weather-related liabilities.
A flexible commodity skew model with maturity effects
The authors propose an extension to the Andersen commodity curve and calibrate the model to market data for West Texas Intermediate crude oil and for natural gas.
How magic a bullet is machine learning for credit analysis? An exploration with fintech lending data
The authors apply machine learning techniques to consumer fintech loan data to assess how such techniques can improve out-of-sample default prediction.
Researching new financial products: using survey evidence to gain insight into buy now, pay later
The authors investigate early adopters of "buy now, pay later" payments and how the use of this service may effect a person's credit.
Did fintech loans default more during the Covid-19 pandemic? Were fintech firms “cream-skimming” the best borrowers?
The authors propose a model which can be used to identify the "invisible prime" consumers from the nonprime pool for fintech loans.
Investment decisions driven by fine-tuned large language models and uniform manifold approximation and projection-supported clustering and hierarchical density-based spatial clustering
The author proposes an investment strategy using LLMs and text from social media posts and business and economic news and demonstrate that the strategy outperforms the chosen benchmark.
Deciphering bankruptcy risk in fintech firms: exploring key factors and implications
The authors delve into bankruptcy risk of Indian fintech firms, identifying the factors that most impact bankruptcy risk.
Skin in the game: risk analysis of central counterparties
This paper proposes a novel framework to design the capital contribution of a central counterparty (CCP) to its default waterfall - CCP "skin in the game".
Timing minimum-variance investment in the Canadian stock market
This paper proposes a novel explanation of the variation in idiosyncratic volatility anomaly return and its use in minimum-variance investing in the Canadian stock market.
Digital money and finance: a critical review of terminology
The authors put forward an etymology of key concepts and review key terminology and definitions within the sphere of decentralized finance to facilitate discussions about their merits and use cases/
Till def(ault) do us part: reassessing counterparty risk between global systemically important banks and central counterparties
The authors investigate how far liquidity at G-SIBs may be available to CCPs prior to a G-SIB resolution beginning and before a forced closeout is necessary, allowing the G-SIB to continue trading with a CCP until a payment default occurs.
Examining intersector risk synchronization in the Indian stock market: evidence from a time-varying connectedness approach
The authors investigate volatility spillover across the Covid-19 pandemic, Russia-Ukraine conflict and the collapse of Silicon Valley Bank and demonstrate how different sectors act as shock absorbers and transmitters.
Survival analysis in credit risk management: a review study
This paper offers a systematic literature review of survival analysis in credit risk assessment and suggests potential future avenues for research.
Uncertainty in the macroeconomic environment, corporate tax avoidance and corporate credit financing: evidence from high-tech listed companies in China
Using data from Chinese high-tech enterprises, the authors investigate links between corporate tax avoidance and bank credit financing.