Technical paper
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.
Pricing American options under irrational behavior in a Markov regime-switching model with a finite-element method
The authors investigate the problem of pricing American options under an irrational strategy, putting forward a method to negate this problem and demonstrate the performance of this model against alternatives.
Deep equal risk pricing of illiquid derivatives with multiple hedging instruments
The authors propose the using equal risk pricing for market-consistent valuation of illiquid financial derivatives, transferring information in liquid hedging strategy prices into the price of the illiquid derivative.
We will shock you: a coherent Bayesian approach for stress testing
The authors propose a novel coherent Bayesian stress test method which preserves the mathematical properties of the risk measures.
Optimal trade execution with unknown drift
This paper demonstrates a means through which to adapt results for optimal trading strategies under different conditions when the drift of the asset is unknown.
Quantum two-sample test for investment strategies
Quantum algorithms display high discriminatory power in the classification of probability distributions
Advanced visualization for the quant strategy universe: clustering and dimensionality reduction
The authors present a novel visualisation model, based on 5000 quantitative investment strategies, which can identify nonlinear relationships and clustering strategies with similar risk factor exposures.
Expectile risk quadrangles and applications
The authors study the expectile risk measure within the fundamental risk quadrangle framework, constructing a new quadrangle where the expectile is both a statistic and a risk measure.
Central clearing and trade cancellation: the case of London Metal Exchange nickel contracts on March 8, 2022
This paper explores the 2022 nickel price event on the London Metal Exchange, examining LME’s response to the market stress, court verdicts and the potential impact on CCP rule books.
Retail payment technology and money demand: evidence from China
Using evidence from China between 1999 and 2020, the authors investigate the impact of retail payment technology on money demand.
Soft information in financial distress prediction: evidence of textual features in annual reports from Chinese listed companies
The authors use textual data in a model to predict financial distress, demonstrating that this can enhance prediction outcome versus traditional financial data alone.