Most viewed recipes tagged "computational_finance" Code RecipesPricing Asian options using mpmath (Python) 2009-11-13T01:28:24-08:00Fernando Nieuwveldt <p style="color: grey"> Python recipe 576954 by <a href="/recipes/users/4172088/">Fernando Nieuwveldt</a> (<a href="/recipes/tags/computational_finance/">computational_finance</a>, <a href="/recipes/tags/laplace/">laplace</a>, <a href="/recipes/tags/mpmath/">mpmath</a>). </p> <p>I present a numerical method for pricing Asian options. The method is based on the numerical inversion of the Laplace transform. The inversion method that is used is based on Talbot contours. It is known that Geman and Yor's formula is computational expensive for low volatility cases. By using Talbots method we can reduce the timing for the low volatility cases, at least to \sigma ~ 0.05. Afterwards the method start to converge slowly. In the literature for \sigma = 0.1 the Geman and Yor formula converges slowly.</p> Pricing Asian options using mpmath with automatic precision control (Python) 2009-11-24T01:20:56-08:00Dieter Kadelka <p style="color: grey"> Python recipe 576964 by <a href="/recipes/users/4172107/">Dieter Kadelka</a> (<a href="/recipes/tags/computational_finance/">computational_finance</a>, <a href="/recipes/tags/laplace_inversion/">laplace_inversion</a>, <a href="/recipes/tags/mpmath/">mpmath</a>). </p> <p>In <a href="">Recipe 576954</a> presented a numerical method for pricing Asian options using mpmath and some code from <a href="">Recipe 576938</a>: Numerical Inversion of the Laplace Transform with mpmath. The code in <a href="">Recipe 576954</a> seems to have problems with the precision required for accurate computation of the integrals. To solve this problem, I changed the code in <a href="">Recipe 576938</a> and the code in <a href="">Recipe 576954</a>, which now uses</p> <p>The new and are in the code section.</p>