Original research
The role of banks’ digital transformation in operational risk management: evidence from China
The authors investigate the impact of banks' digital transformation on operational risk, finding that in most cases, this reduces operational risk,
Risk measures associated with insurance losses in Ghana
The authors investigate VaR and TVaR comprehensive motor insurance claims paid by an insurance company in Ghana and compare the estimates obtained by these risk measures.
Let’s speak the same language: a formally defined model to describe and compare payment system architectures
The authors propose a means with which to represent and compare three key functions of payment system architectures: issuance/withdrawal, holding and transfer of funds.
Toward immediacy and continuity in money and finance?
This paper investigates the relationship between developments in information and communication technology and changes in the time structure of money and finance.
Fintech adoption and economic growth: exploring the global landscape
The authors argue that increased fintech adoption has a causal relationship with a growth in GDP per capita using data from 112 countries.
How concentrated is the clearing ecosystem and how has it changed since 2007?
This paper uncovers changes to concentration of the clearing ecosystem and how it has changed since the 2007-9 financial crisis.
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
The authors explore how the China systemic financial risk composite index might contribute to the development of the Chinese manufacturing industry.
A three-stage fusion model for predicting financial distress considering semantic and sentiment information
The authors apply sentiment analysis to management discussion and analysis texts to aid the prediction of financial distress with an innovative three-phase fusion model.
Randomization of spectral risk measures and distributional robustness
The authors offer a means to describe a decision maker's risk preferences with a randomized spectral risk measure.
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".