Technical paper
Analytic risk-free rates option pricing with smile and skew
An arbitrage-free short-rate model for backward-looking compounded rates is presented
Overfitting in portfolio optimization
The authors measure the performance of sample-based rolling-window neural network (NN) portfolio optimization strategies and demonstrate that correctly set up NN-based strategies can outperform the 1/N strategy.
The importance of being scrambled: supercharged quasi-Monte Carlo
The authors propose a randomized quasi-Monte Carlo method which outperforms both the Monte Carlo and standard quasi-Monte Carlo methods.
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.
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.
Smile-consistent basket skew
An analytic approximation for the implied volatility surface of basket options is introduced
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.
A robust stochastic volatility model for interest rates
A swaption pricing model based on a single-factor Cheyette model is shown to fit accurately
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.
Does the term structure of the at-the-money skew really follow a power law?
A power law can fit the ATM skew, but struggles with short maturities
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.