Latest recipes tagged "computational_finance" and "mpmath"http://code.activestate.com/recipes/tags/computational_finance+mpmath/new/2009-11-24T01:20:56-08:00ActiveState Code RecipesPricing Asian options using mpmath with automatic precision control (Python)
2009-11-24T01:20:56-08:00Dieter Kadelkahttp://code.activestate.com/recipes/users/4172107/http://code.activestate.com/recipes/576964-pricing-asian-options-using-mpmath-with-automatic-/
<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>).
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<p>In <a href="http://code.activestate.com/recipes/576954/">Recipe 576954</a> presented a numerical method for pricing Asian options using mpmath and some code from <a href="http://code.activestate.com/recipes/576938/">Recipe 576938</a>: Numerical Inversion of the Laplace Transform with mpmath. The code in <a href="http://code.activestate.com/recipes/576954/">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="http://code.activestate.com/recipes/576938/">Recipe 576938</a> and the code in <a href="http://code.activestate.com/recipes/576954/">Recipe 576954</a>, which now uses mp_laplace.py.</p>
<p>The new mp_laplace.py and asian.py are in the code section.</p>
Pricing Asian options using mpmath (Python)
2009-11-13T01:28:24-08:00Fernando Nieuwveldthttp://code.activestate.com/recipes/users/4172088/http://code.activestate.com/recipes/576954-pricing-asian-options-using-mpmath/
<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>).
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<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>