Original research
Key impact deep dive (KIDD)
This paper proposes a KIDD (key impact deep dive) approach for assessing extreme risks based on assessing key impact types.
Monitoring intraday liquidity risks in a real-time gross settlement system
This paper proposes an intraday liquidity risk indicator (LRI) for each participant in a real-time gross settlement system (RTGS).
Impact of changes in the global environment on price differentials between the US crude oil spot markets for the periods before and after 2008–9
This paper uses threshold cointegration to examine price differentials between crude oil spot markets in the US for the periods before (2000–2007) and after (2010–17) the advent of major technological and other changes impacting the oil sector.
Validation nightmare: the slotting approach under International Financial Reporting Standard 9
This paper makes an important contribution to the practice of validation by focusing on an under-researched area of the slotting approach to real estate specialized lending under the International Financial Reporting Standard 9 (IFRS 9) framework.
Quantization-based Bermudan option pricing in the foreign exchange world
This paper proposes two numerical solutions based on product optimal quantization for the pricing of Bermudan options on foreign exchange rates.
Deep learning for discrete-time hedging in incomplete markets
This paper presents several algorithms based on machine learning to solve hedging problems in incomplete markets.
A fractional Brownian–Hawkes model for the Italian electricity spot market: estimation and forecasting
This paper proposes a new model for the description and forecast of gross prices of electricity in the liberalized Italian energy market via an additive two-factor model.
The relationship between oil prices, global economic policy uncertainty and financial market stress
This paper introduces two models: the first analyzes the impacts of global economic policy uncertainty, gold prices and three-month US Treasury bill rates on oil prices between 1997 and 2020, and the second examines the effects of oil prices and US…
Nonconvex noncash risk measures
This paper looks at nonconvex, noncash risk measures with p-norm (1 ≤ p ≤ ∞) for nonweak cone-type acceptable sets.
Review of credit risk and credit scoring models based on computing paradigms in financial institutions
This paper provides an overview of some prominent credit scoring models used in financial institutions and provides an insight into how the use and integration of popular computing paradigms based on NNs, machine learning, game theory and BDA in credit…
An interpretable Comprehensive Capital Analysis and Review (CCAR) neural network model for portfolio loss forecasting and stress testing
This paper proposes an interpretable nonlinear neural network model that translates business regulatory requirements into model constraints.
Three ways to improve the systemic risk analysis of the Central and Eastern European region using SRISK and CoVaR
This paper proposes three modifications to two well-established measures of systemic risk, SRISK and CoVaR.
Pricing American options under negative rates
This paper derives a new integral equation for American options under negative rates and shows how to solve this new equation through modifications to the modern and efficient algorithm of Andersen and Lake.
Performance measures adjusted for the risk situation (PARS)
This paper proposes the use of a new class of performance measures adjusted for the risk situation (PARS), as the perception of risk depends on the individual situation including risk preferences.
Fast pricing of American options under variance gamma
This research develops a new fast and accurate approximation method, inspired by the quadratic approximation, to get rid of the time steps required in finite-difference and simulation methods, while reducing error by making use of a machine learning…
On modeling contagion in the formation of operational risk loss
This paper models an overall operational risk loss caused by the accumulation of intermediate losses incurred at each process via a mechanism of network contagion across distinct processes within the boundary of a bank.
Correlated idiosyncratic volatility shocks
To capture the commonality in idiosyncratic volatility, the authors propose a novel multivariate generalized autoregressive conditional heteroscedasticity (GARCH) model called dynamic factor correlation (DFC).
What can we learn from what a machine has learned? Interpreting credit risk machine learning models
This paper studies a few popular machine learning models using LendingClub loan data, and judges these on performance and interpretability
Empirical validation of the credit rating migration model for estimating the migration boundary
In this paper, a structural model for credit rating migration is developed and validated, by which the migration boundary is recovered for the first time.
Measure twice before you cut: differences in Furfine-type algorithm implementations
This study focuses on the practical implementation aspects of “Furfine-type” algorithms used to identify money market loans from payments data.
Explaining credit ratings through a perpetual-debt structural model
This paper calibrates a perpetual-debt structural model (PDSM) by using Moody’s historical credit ratings.
A numerical approach to the risk capital allocation problem
The aim of this paper is to use a model-free, nonparametric approach based on the method of maximum entropy in the mean to solve the capital risk allocation problem.
Expansion method for pricing foreign exchange options under stochastic volatility and interest rates
This paper applies the smart expansion method to the Heston–Hull–White model, which admits stochastic interest rates to enhance the model, and obtains the expansion formula for pricing options in the model up to second order.
Ensemble methods for credit scoring of Chinese peer-to-peer loans
This study aims to conduct credit scoring by focusing on a Chinese P2P lending platform and selecting the optimal subset of features in order to find the best overall ensemble model.