Technical paper
Operational risk, capital regulation and model risk
The author proposes seven basic properties for operational risk modelling to form an operational risk management framework.
Navigating risk horizons: a comprehensive bibliometric analysis of corporate risk management
The authors conduct a bibliometric analysis of 100 research papers to identify trends within corporate risk management.
The power of neural networks in stochastic volatility modeling
The authors apply stochastic volatility models to real-world data and demonstrate how effectively the models calibrate a range of options.
Option market-making and vol arbitrage
The agent’s view is factored in to a realised-vs-implied vol model
A tale of two tail risks
This paper investigates the relationship between banking credit risk and the financial market jump hazard rate, finding the two risks to have opposing behaviors.
The impact of divergence in communication tone on investors’ willingness to invest in eurozone small- to medium-sized enterprises
The authors analyze the tone of central bank communications and how this can impact investor readiness to invest in euro areas SMEs.
Fintech lending and firm bankruptcies
The authors use data from small business bankruptcy at a county level in California to investigate the impact of fintech lending.
Dissecting initial margin forecasts: models, limitations and backtesting
The authors demonstrate that initial margin is not value-at-risk, but its approximation, and suggest a generic backtesting and verification framework that accommodates both forecasting limitations and existing models.
Incorporating financial reports and deep learning for financial distress prediction: empirical evidence from Chinese listed companies
The authors investigate the use of text information processing methods for financial distress prediction and how this method can be combined with traditional means to improve prediction accuracy.
Operational risk modeling under the loss distribution approach: estimation of operational risk capital by business line versus risk category
The authors apply the loss distribution approach to operational risk data, contributing to understandings of the composition and distribution of operational risk data across risk classes and the corresponding operational risk capital requirements
Overcoming Markowitz’s instability with hierarchical risk parity
Portfolio optimisation via HRP provides stable and robust weight estimates
Operational risk and non-life insurers’ performance
The authors assess operational risk in the non-life-insurance sector, finding operational lapses and the cost–income ratio to have negative effects on premium growth and financial performance.
Lessons for academic research from model risk management in financial institutions
The authors suggest that model risk management practices used in financial institutions can be applied to academic research and enhance research outcomes.
Operational risks: trends and challenges
The authors carry out a systematic literature review of operational risk research to determine the current state of operational risk research in financial institutions.
Funding arbitrages and optimal funding policy
Stochastic control can be used to manage a bank’s net asset income
Pricing time-capped American options using a least squares Monte Carlo method
This paper uses a modified least squares Monte Carlo method to price time-capped American options.
Determination of the fraction of losses and their probabilities by type of risk and business line from aggregate loss data
This paper proposes a novel means to derive the individual loss severities and the frequency of these losses per business line and risk type.
The effects of climate transition risk on an investment portfolio
The author proposes a means to value portfolios under a climate transition stress test, showing which sectors are likely to be more severely impacted by a transition to a net-zero economy.
Earnings moves and pre-earnings implied volatility
The authors investigate the relationship between return realizations and pre-earnings implied volatility, finding the distribution of returns over earnings windows to be symmetrical.
The prediction of mortgage prepayment risks in the early stages of loan origination: a machine learning approach
The authors put forward a machine learning model for the prediction of mortgage prepayment risks at the loan origination phase.
Herding behavior in energy commodity futures markets amid turmoil and turmoil-free periods
This paper extends typical research on herding behavior to commodity futures markets, investigating five markets and finding herding behavior during the global financial crisis and at the beginning of the Russia - Ukraine conflict.