Technical paper/Monte Carlo simulation
Deep learning profit and loss
The P&L distribution of a complex derivatives portfolio is computed via deep learning
SABR smiles for RFR caplets
The SABR model for volatility is adapted to price risk-free rate caplets
Solving final value problems with deep learning
Pricing vanilla and exotic options with a deep learning approach for PDEs
Numerical techniques for the Heston collocated volatility model
In this paper, the authors discuss all aspects of derivative pricing under the Heston–CLV model: calibration with an efficient Fourier method; a Monte Carlo simulation with second-order convergence; and accurate partial differential equation pricing…
Bank leverage and capital bias adjustment through the macroeconomic cycle
The author assesses the quantitative effects of the recent proposal for more robust bank capital adequacy.
Differential machine learning: the shape of things to come
A derivative pricing approximation method using neural networks and AAD speeds up calculations
Elliptical and Archimedean copula models: an application to the price estimation of portfolio credit derivatives
This paper explores the impact of elliptical and Archimedean copula models on the valuation of basket default swaps.
A closed-form solution for optimal mean-reverting strategies
The heat potentials method is used to find the optimal profit-taking and stop-loss levels
Art-secured lending: a risk analysis framework
In this study, the authors identify the three types of risks involved in an art-secured lending operation and present a framework to assess their combined effects via a Monte Carlo simulation.
Monte Carlo pathwise sensitivities for barrier options
In this work, we present a new Monte Carlo algorithm that is able to calculate the pathwise sensitivities for discontinuous payoff functions.
An adaptive Monte Carlo approach
This paper proposes a new, flexible framework using Monte Carlo methods to price Parisian options not only with constant boundaries but also with general curved boundaries.
The market generator
A generative neural network is proposed to create synthetic datasets that mantain the statistical properties of the original dataset
A simulation-based model for optimal demand response load shifting: a case study for the Texas power market
This paper describes a case study of analyzing DR load-shifting strategies for a retail electric provider for the Texas (ERCOT) market using a Monte Carlo simulation with stochastic loads and settlement prices.
Estimation of value-at-risk for conduct risk losses using pseudo-marginal Markov chain Monte Carlo
The authors propose a model for conduct risk losses, in which conduct risk losses are characterized by having a small number of extremely large losses (perhaps only one) with more numerous smaller losses.
Variance optimal hedging with application to electricity markets
In this paper, the author uses the mean–variance hedging criterion to value contracts in incomplete markets.
Nonparametric tests for jump detection via false discovery rate control: a Monte Carlo study
The main goal of this paper is to perform a comprehensive nonparametric jump detection model comparison and validation. To this end, the authors design an extensive Monte Carlo study to compare and validate these tests.
The standard market risk model of the Swiss solvency test: an analytic solution
This paper derives an alternative fast Fourier transform-based computational approach for calculating the target capital of the SST that is more than 600 times faster than a Monte Carlo simulation.
Dynamic volatility management: from conditional volatility to realized volatility
In this paper, the authors present a multiperiod portfolio management strategy that can be used to directly manage the realized volatility over a long time horizon.
An efficient portfolio loss model
This paper develops a parsimonious model for evaluating portfolio credit derivatives dependent on aggregate loss.
Skewed target range strategy for multiperiod portfolio optimization using a two-stage least squares Monte Carlo method
In this paper, the authors propose a novel investment strategy for portfolio optimization problems.
Application of the Heath–Platen estimator in the Fong–Vasicek short rate model
In this paper, the authors construct a Heath-Platen-type Monte Carlo estimator that performs extraordinarily well compared with the crude Monte Carlo estimation.
CVA and IM: welcome to the machine
Henry-Labordere proposes a neural networks-based technique to price counterparty risk and initial margin
Keep it real: tail probabilities of compound distributions
Igor Halperin proposes new approach to compute probabilities of heavy-tailed distributions
Fast stochastic forward sensitivities in Monte Carlo simulations using stochastic automatic differentiation (with applications to initial margin valuation adjustments)
In this paper, the author applies stochastic (backward) automatic differentiation to calculate stochastic forward sensitivities.