• C++ Programming for Financial Engineering
    Highly recommended by thousands of MFE students. Covers essential C++ topics with applications to financial engineering. Learn more Join!
    Python for Finance with Intro to Data Science
    Gain practical understanding of Python to read, understand, and write professional Python code for your first day on the job. Learn more Join!
    An Intuition-Based Options Primer for FE
    Ideal for entry level positions interviews and graduate studies, specializing in options trading arbitrage and options valuation models. Learn more Join!

Inversion of Two-Sided Laplace Transforms

  • Thread starter Thread starter bcserna
  • Start date Start date
Joined
5/22/09
Messages
1
Points
11
Hi All,

does anybody know how to invert a two-sided Laplace transform (numerically)? The one-sided Laplace transform ($F$) of the function ($f$) is given by

($$F(s)=\int_0^{+\infty} \exp(-s\cdot t)\cdot f(t) dt$$),

which can be easily inverted numerically using the inversion formula of (Dubner and Abate, 1968, Numerical Inversion of Laplace Transforms by Relating Them to the Finite Fourier Cosine Transform) given by:

($$f(t) =\frac{\exp(A/2)}{2} \cdot \left[ \frac{1}{2} \cdot F \large( \frac{A}{2 \cdot t} \right) + \sum_{k=1}^{+\infty} (-1)^k \cdot \Re \left\{ F\large( \frac{A+ 2 \cdot \pi \cdot k \cdot \sqrt{-1}}{2 \cdot t} \right) \right\} \right] $$),

where ($A$) is a positive constant and ($\Re$) denotes the real part. This method is extended by (Petrella, 2004, An Extension of the Euler Laplace Transform Inversion Algorithm with Applications in Option Pricing) for functions on the entire real line if those satisfy specific conditions.

The two-sided Laplace transform ($\hat{F}$) of the function ($f$) is given by

($$\hat{F}(s)=\int_{-\infty}^{+\infty} \exp(-s\cdot t)\cdot f(t) dt$$),

which is used, for instance, within the option pricing model of (Kou, Petrella, Wang, 2005, Pring Path-Dependent Options with Jump Risk via Laplace Transforms).

(Kou, Petrella, Wang 2005) say that they apply the extention of (Petrella, 2004) but the extention still refers to the one-sided Laplace transform since the inversion formula remains unchanged except that the discretization error is now on both sides of the real axis. Furthermore (Petrella, 2004) unfortunately does not mention how his extended version can be applied for inverting two-sided Laplace transforms.

Does anyone know the answer? Thanxx for helping me.
 
I almost have the same problem. I am trying to compute the sensitivies in the paper of (Kou, Petrella, Wang 2005) page 5. But the numerical inversion of Laplace Transform (using Stehfest algorithm) doesn't give me good results!!
 
Back
Top