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20052023
most citedDeep learning calibration of option pricing models: some pitfalls and solutions

15 citations · 66 across the 31 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

q-fin.CP2019

Four-factor model of Quanto CDS with jumps-at-default and stochastic recovery

Andrey Itkin, Fazlollah Soleymani

In this paper we modify the model of Itkin, Shcherbakov and Veygman, (2019) (ISV2019), proposed for pricing Quanto Credit Default Swaps (CDS) and risky bonds, in several ways. Firs…

q-fin.CP2019

A model-free backward and forward nonlinear PDEs for implied volatility

Peter Carr, Andrey Itkin, Sasha Stoikov

We derive a backward and forward nonlinear PDEs that govern the implied volatility of a contingent claim whenever the latter is well-defined. This would include at least any contin…

q-fin.CP2019★ 15 cited

Deep learning calibration of option pricing models: some pitfalls and solutions

A Itkin

Recent progress in the field of artificial intelligence, machine learning and also in computer industry resulted in the ongoing boom of using these techniques as applied to solving…

q-fin.MF2019★ 1 cited

ADOL - Markovian approximation of rough lognormal model

Peter Carr, Andrey Itkin

In this paper we apply Markovian approximation of the fractional Brownian motion (BM), known as the Dobric-Ojeda (DO) process, to the fractional stochastic volatility model where t…

q-fin.CP2019

Pricing foreign exchange options under stochastic volatility and interest rates using an RBF--FD method

Fazlollah Soleymani, Andrey Itkin

This paper proposes a numerical method for pricing foreign exchange (FX) options in a model which deals with stochastic interest rates and stochastic volatility of the FX rate. The…