Monte Carlo simulation
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
VAR models at odds on forex, commodities, credit risks – EBA
Interquartile distribution of VAR outputs highest for small banks, watchdog finds
On eve of Brexit, PPF’s chief risk officer isn’t too worried
Stephen Wilcox talks about getting pensions paid without the benefit of controlling ‘UK Plc’
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.
Clearing house innovation of the year: Ice Clear Credit
Risk Awards 2020: Clearing house lures fund business with efficient new Monte Carlo methodology
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.
Complex op risk models open to high error, study finds
Measuring 1-in-1,000 year loss events ‘unrealistic’, researchers say
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.
Deep hedging and the end of the Black-Scholes era
Quants are embracing the idea of ‘model free’ pricing and hedging
An efficient portfolio loss model
This paper develops a parsimonious model for evaluating portfolio credit derivatives dependent on aggregate loss.
Risk and finance – Better together
Changing regulations and new accounting standards are creating enormous challenges for financial organisations. Thorsten Hein, principal product marketing manager, risk research and quantitative solutions at SAS, explores why, to successfully meet these…
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.
Could machine learning improve CVA and IM calculations?
Banks have built ways to calculate CVA more quickly, but neural networks could offer more accurate method
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.