Technical paper/Risk management
Artificial intelligence in crisis management: a bibliometric analysis
The authors carry out a bibliometric analysis of academic papers in the field of artificial intelligence applications in crisis management and propose potential new directions for researchers in this field.
Credit risk management: a systematic literature review and bibliometric analysis
The authors undertake a literature review and bibliometric analysis of 774 credit risk research papers.
New proxy schemes for swing contracts
The authors investigate the valuation of swing contracts for energy markets and propose two methods which offer more accurate calculated prices than commonly used methods.
Dynamic margining long/short equity trading strategies
A repo haircut model extends a previous solution for long-only strategies
Better anti-procyclicality? From a critical assessment of anti-procyclicality tools to regulatory recommendations
The authors carry out quantitative and qualitative analysis of anti-procyclicality tools and suggest policy measures intended to make APC tools more effective.
Peak-to-valley drawdowns: insights into extreme path-dependent market risk
The authors investigate risk in relation to peak-to-valley market drawdowns and aim to gain insights into the drawdown behaviour of asset classes across time intervals.
Exchange rate risk management for contractors within a hybrid payment scheme: a case study in Punta del Este, Uruguay
The author proposes methods for how contractors may attempt to mitigate exchange rate risks in hybrid payment systems and validates these with empirical data from a hypothetical project.
Legal risk management in the Polish banking sector
We carry out a review of the management of legal risk in Polish banks and use empirical research to demonstrate how these risks are managed.
Estimating the impact of climate change on credit risk
The author investigates the relationship between climate change and credit risk characteristics of individual obligors and portfolios of credit obligations.
A new automated model validation tool for financial institutions
The authors put forward a novel automated validation tool, based on US Federal Reserve and Office of the Comptroller of the Currency regulatory guidance, which is used to to validate predictive models for financial organizations.
How to choose the dependence types in operational risk measurement? A method considering strength, sensitivity and simplicity
The authors put forward a method for banks to choose the most appropriate dependence type based on an empirical analysis of the Chinese Operational Loss Database.
Construction of hypothetical scenarios for central counterparty stress tests using vine copulas
Using the vine copula, the authors put forward a nonparametric means to generate and/or validate hypothetical stress scenarios.
Integrating text mining and analytic hierarchy process risk assessment with knowledge graphs for operational risk analysis
This paper proposes a new method, entitled the risk-based knowledge graph, which is designed to make analysis of safety records from an operational risk perspective easier and more efficient.
The information value of past losses in operational risk
The authors argue that past operational losses inform future losses at banks and that the information provided by past losses results from their capturing factors that are hard to quantify in other tests.
Does board diversity mitigate firm risk-taking? Empirical evidence from China
The authors explore the relationship between firm risk and both demographic and cognitive-oriented board diversity.
Dynamic class-imbalanced financial distress prediction based on case-based reasoning integrated with time weighting and resampling
The authors put forward a dynamic class-imbalanced CBR FDP model which is shown, using data from Chinese listed companies, to outperform static and dynamic CBR FDP models without resampling or time weighting.
Operational risk: a global examination based on bibliometric analysis
The authors quantitively assess the quality of research on operational risk and find that research in this area has grown in popularity in recent years.
Machine learning for categorization of operational risk events using textual description
The authors summarise ways that machine learning can help categorize textual descriptions of operational loss events into Basel II event types.
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.
“Closing the gaps: moving forward on tail risks in central clearing”: a central bank of issue perspective
The authors explain the priorities for CCP recovery and resolution from a central bank of issue perspective, focussing on structural barriers and how gaps could be overcome.
Choice of margin period of risk and netting for computing margins in central counterparty clearinghouses: a Monte Carlo investigation
The authors provide a quantitative comparison for evaluating the impact of collecting margins in a gross-versus-net system with the margin period of risk (MPOR) set to between one and five days.