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Strong Taylor Schemes for Stochastic Volatility

Introduction

Ito and Stratonovich Stochastic Calculus

Ito-Stratonovich drift conversion

Strong Numerical Schemes for SDE

Milstein scheme for commutative noise

Approximations of Volatility Models

General 2D Milstein scheme for stochastic volatility models

Approximations of the Double Integral

Subdivision (Kloeden - IC = 0)

Fourier Lévy formulae

Exact Fourier Lévy formulae

Real Variance formulae

Simulation of the Double Integral

Conclusions and Observations

Ornstein-Uhlenbeck Process

Formulae derivation for Heston Volatility

The fundamental solution

Derivation of the 2D Milstein Scheme

Numerical Data of the Double Integral

References

Books Related

Strong Taylor Schemes for Stochastic Volatility

Exact Fourier Lévy formulae

In the Fourier method described above, we use uniform and normal independent random numbers (Q & Z) to calculate the samples of X and Y. However, if we assume the opposite, that Q and Z are functions of the original random numbers that create the Wiener process Wi:

(34)

and using exactly the same criteria of the Fourier Lévy formulae described above (page 16), we get a better approximation to calculate the probability for our double integral {24}:

(35)(36)(37)

Because the equation {35} can not be defined at some point, we replace it with the following:

(38)

where C1 is a constant to avoid having division or logarithm of zero or close to it.

The assumption {34} is strong, but as well the results in the simulations. The use of {37} to calculate the double integral gives us a better and more stable approximation for all time steps n than the simple Fourier formulae shown in {33}. Furthermore, if we use only Z1 or Z2 and/or the combination with other independent random numbers, the numerical results are never as good as using the original formulae {33}. All these strong conclusions convince us that {37} is the best combination and, therefore, is called the Exact Fourier Lévy formulae.

Prof. Klaus Schmitz

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