Journals
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.
Operational risk and regulatory capital: do public and private banks differ?
The authors investigate relationships between operational risk and regulatory capital in Indian public and private banks.
A text analysis of operational risk loss descriptions
The authors put forward a workflow for using text analysis to identify underlying risks in operational risk event descriptions.
On the mitigation of valuation uncertainty risk: the importance of a robust proxy for the “cumulative state of market incompleteness”
The author put forwards a means to mitigate asset risk and valuation uncertainty risk which relies on investors conditioning valuations of new assets on a dynamically evolving intertemporal mechanism
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.
Understanding and predicting systemic corporate distress: a machine-learning approach
The authors construct a machine-learning-based early-warning system to predict, one year in advance, risks of systemic distress and demonstrate factors which can predict corporate distress.
Evaluating the performance of energy exchange-traded funds
The authors investigate the performance of energy exchange-traded funds between January 2000 and August 2022, finding a relatively high degree of correlation with the performance of US and global equities.
A two-stage nonlinear approach for modeling hourly spot power prices with an application to spot market risk valuation of the power yield of a solar array in Germany
This paper combines a seasonal autoregressive moving average model with a Markov regime-switching model approach for power spot prices, allowing intraday and weekly seasonalities to be incorporated.
Emulating the Standard Initial Margin Model: initial margin forecasting with a stochastic cross-currency basis
The authors propose a stochastic cross-currency basis model extension to resolve the impact of missing risk factors when estimating initial margin and margin valuation adjustments in cross-currency basis swaps.
Pricing default risk in stochastic time
This paper explores credit derivative pricing through the structural modeling framework and seeks to improve on how accurately such models value derivative securities.
Neural stochastic differential equations for conditional time series generation using the Signature-Wasserstein-1 metric
Using conditional neural stochastic differential equations, the authors propose a means to improve the efficiency of generative adversarial networks and test their model against other classical approaches.
Toward a unified implementation of regression Monte Carlo algorithms
The authors put forward a publicly available computational template for machine learning, named mlOSP, which presents a unified numerical implementation of RMC approaches for optimal stopping.
An approach to capital allocation based on mean conditional value-at-risk
The authors put forward a means of Euler capital allocation where the probability level is adjusted such that the total capital is equal to the reference quantile-based capital level.
Throwing green into the mix: how the EU Emissions Trading System impacted the energy mix of French manufacturing firms (2000–16)
This paper investigates links between environmental policy and production decisions, with a focus on firms' energy mixes.
Using a skewed exponential power mixture for value-at-risk and conditional value-at-risk forecasts to comply with market risk regulation
The authors investigate a method that combines two skewed exponential power distributions and models the conditional forecasting of VaR and CVaR and is in compliance with the recent Basel framework for market risk.
Default forecasting based on a novel group feature selection method for imbalanced data
The authors construct a group feature selection method which combines optimal instance selection with weighted comprehensive precision in an effort to improve the performance of prediction models in relation to defaulting firms.
The realized local volatility surface
The authors put forward a Bayesian nonparametric estimation method which reconstructs a counterfactual generalized Wiener measure from historical price data.
A general control variate method for time-changed Lévy processes: an application to options pricing
The authors put forward a novel control variate method for time-changed Lévy models and demonstrate an efficient reduction of the variance of Monte Carlo in numerical experiments.
Sherman ratio optimization: constructing alternative ultrashort sovereign bond portfolios
This paper explores the Sherman ratio and find that it has merit in the optimization of portfolio construction.
Uncovering the hidden impact: noninvestor disagreement and its role in asset pricing
The authors investigate the link between noninvestors and financial returns using data from a social media platform.
The informativeness of risk factor disclosures: estimating the covariance matrix of stock returns using similarity measures
The authors examine 10-K and 10-Q filings for risk factor disclosures and investigate if these disclosures can be used to improve estimations of the covariance matrix of stock returns.
Trading robots and financial markets trading solutions: the role of experimental economics
The authors investigate and summarize experimental studies on automated trading strategies in financial markets.
Modeling the bid and ask prices of options
The authors investigate and partially solve theoretical and empirical problems for the joint modelling of bid and ask prices.
Efficient numerical valuation of European options under the two-asset Kou jump-diffusion model
The authors extend a technique proposed by Toivanen (2008), arriving at an algorithm evaluating the nonlocal double integral appearing in the two-dimensional Kou PIDE and perform several numerical experiments to demonstrate actual convergence behavior…