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.
In this paper, the authors construct a Heath-Platen-type Monte Carlo estimator that performs extraordinarily well compared with the crude Monte Carlo estimation.
Banks have built ways to calculate CVA more quickly, but neural networks could offer more accurate method
Henry-Labordere proposes a neural networks-based technique to price counterparty risk and initial margin
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.
In this paper, the authors give a preprocessing step for Fourier methods that involves projecting the Green’s function onto the set of linear basis functions.
Pricing fast-responding electric storage assets in the presence of negative prices and price spikes: a simulation-and-regression approach
This study focuses on the use of batteries for real-time power trading and proposes a simulation-and-regression-based valuation model.
In this paper, the authors develop a new local correlation model that uses a generic function 'g' to describe the correlation between all asset–asset pairs for a basket of underlyings.
In this paper, the authors construct strategies for an American option portfolio by exercising options at optimal timings with optimal weights determined concurrently.
CCP aims for Q1 2019 roll-out of new Monte Carlo-based methodology as it plans launch of index swaptions
This paper proposes a methodology to quantify capital charges for concentration risk when economic capital calculations are conducted within a multifactor Merton framework.
This study reviews the various statistical methodologies that are in place to test multiple systematic trading strategies and implements these methodologies under simulation with known artificial trading rules in order to critically compare and evaluate…
Vibrato and automatic differentiation for high-order derivatives and sensitivities of financial options
This paper deals with the computation of second-order or higher Greeks of financial securities. It combines two methods, vibrato and automatic differentiation (AD), and compares these with other methods.
StanChart quant proposes new technique to compute MVA quicker
Algorithmic differentiation are used to simulate sensitivities to calculate MVA
This paper seeks to contribute a simple and (almost) model-free way of assessing the economic value of the Bermudan exercise right derived from a “minimal” local volatility enhanced interest rate model.
This paper develops efficient importance sampling schemes for a class of jump–diffusion processes that are commonly used for modeling stock prices.
Research on AAD is not complete until it becomes easier to implement, says quant
In this paper, the authors examine the problem of validating and calibrating FHS VaR models, focussing in particular on the Hull and White (1998) approach with EWMA volatility estimates, given its extended use in the industry.
New machines have big potential in AI, valuations and VAR, but tech giants like IBM need help from practitioners
Adjoint algorithmic differentiation tool support for typical numerical patterns in computational finance
This paper demonstrates the flexibility and ease in using C++ algorithmic differentiation (AD) tools based on overloading to numerical patterns (kernels) arising in computational finance.