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Found 14 papers, 2 papers with code
Consistent asset modelling with random coefficients and switches between regimes
To establish our framework, we initially construct a model with random parameters, where the switching between regimes can be dictated either by random variables or deterministically.
Accelerated Computations of Sensitivities for xVA
Sensitivity calculations which require shocked and unshocked valuations in bump-and-revalue schemes exacerbate the computational load.
Randomization of Short-Rate Models, Analytic Pricing and Flexibility in Controlling Implied Volatilities
We show that with the randomized short-rate models, the shapes of implied volatility can be controlled and significantly improve the quality of the model calibration, even for standard 1D variants.
On Pricing of Discrete Asian and Lookback Options under the Heston Model
We propose a new, data-driven approach for efficient pricing of - fixed- and float-strike - discrete arithmetic Asian and Lookback options when the underlying process is driven by the Heston model dynamics.
On Randomization of Affine Diffusion Processes with Application to Pricing of Options on VIX and S&P 500
Any non-affine model must pass the strict requirement of quick calibration -- which is often challenging.
Sensitivities and Hedging of the Collateral Choice Option
We obtain sensitivities of the collateral choice option price under both the deterministic and the stochastic model, and we show that the stochastic model attributes risks to all involved collateral currencies.
Efficient Pricing and Calibration of High-Dimensional Basket Options
This paper studies equity basket options -- i. e., multi-dimensional derivatives whose payoffs depend on the value of a weighted sum of the underlying stocks -- and develops a new and innovative approach to ensure consistency between options on individual stocks and on the index comprising them.
Fast Sampling from Time-Integrated Bridges using Deep Learning
We propose a methodology to sample from time-integrated stochastic bridges, namely random variables defined as $\int_{t_1}^{t_2} f(Y(t))dt$ conditioned on $Y(t_1)\!=\! a$ and $Y(t_2)\!=\! b$, with $a, b\in R$.
Pricing and Hedging Prepayment Risk in a Mortgage Portfolio
Here, in the setting of a Dutch mortgage provider, we propose to include non-linear financial instruments in the hedge portfolio when dealing with mortgages with the option to prepay part of the notional early.
Sparse Grid Method for Highly Efficient Computation of Exposures for xVA
This is the bottleneck of every simulation of xVA.
Monte Carlo Simulation of SDEs using GANs
We compare the input-output map obtained with the standard GAN and supervised GAN and show experimentally that the standard GAN may fail to provide a path-wise approximation.
Cheapest-to-Deliver Collateral: A Common Factor Approach
We develop a scalable and stable stochastic model of the collateral spreads under the assumption of conditional independence.
The Seven-League Scheme: Deep learning for large time step Monte Carlo simulations of stochastic differential equations
With a method variant called the compression-decompression collocation and interpolation technique, we can drastically reduce the number of neural network functions that have to be learned, so that computational speed is enhanced.
A neural network-based framework for financial model calibration
A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN).
