Monte Carlo simulation
Fake data can help backtesters, up to a point
Synthetic data made with machine learning will struggle to capture the caprice of financial markets
In fake data, quants see a fix for backtesting
Traditionally quants have learnt to pick data apart. Soon they might spend more time making it up
Most EU banks use historical simulation approach to VAR
Few lenders favour Monte Carlo or parametric methodologies
Model change pumps up Deutsche’s VAR capital charge
Switch to historical simulation approach increases requirement by 71%
Deutsche Börse eyes quantum computing
Pilot application to model enterprise risks cuts computation time from 10 years to 30 minutes
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.
Ice tees up CDS options launch for November 9
Fight for CDS market share heats up as Ice begins clearing options and LCH preps CDX offering
Science friction: some tire of waiting for quantum’s leap
Use cases for new tech are piling up – from CVA to VAR. But so are the obstacles
Danske quants discover speedier way to crunch XVAs
Differential machine learning produces results “thousands of times faster and with similar accuracy”
Dutch banks seek quantum edge for stress tests
ABN, ING and Rabobank working together; US quantum developer seeks patent for CCAR
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
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.