Wavelet Optimized Finite-Difference Approach to Solve Jump-Diffusion type Partial Differential Equation for Option Pricing
Mohammad R. Rahman,
Ruppa K. Thulasiram () and
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Ruppa K. Thulasiram: Computer Science University of Manitoba
No 471, Computing in Economics and Finance 2005 from Society for Computational Economics
The sine and cosine functions used as the bases in Fourier analysis are very smooth (infinitely differentiable) and very broad (nonzero almost everywhere on the real line), and hence they are not effective for representing functions that change abruptly (jumps) or have highly localized support (diffusive). In response to this shortcoming, there has been intense interest in recent years in a new type of basis functions called wavelets. A given wavelet basis is generated from a single function, called a mother wavelet or scaling function, by dilation and translation. By replicating the mother wavelet at many different scales, it is possible to mimic the behavior of any function; this property of wavelets is called multiresolution. Wavelet is a powerful integral transform technique for studying many problems including financial derivatives such as options. Moreover, the approximation error is much smaller than that of the truncated Fourier expansion. Therefore, one can get better approximation of a function at jump discontinuity with the use of wavelet expansion rather than Fourier expansion. In the current study, we employ wavelet analysis to option pricing problem manifested as partial differential equation (PDE) with jump characteristics. We have used wavelets to develop an optimum finite differencing of the differential equations manifested by complex financial models. In particular, we apply wavelet optimized finite-difference (WOFD) technique on the partial differential equation. We describe how Lagrangian polynomial is used to approximate the partial derivatives on an irregular grid. We then describe how to determine sparse and dense grid with wavelets. Further work on implementation is going on.
Keywords: options; wavelets; jump-diffusion; finite-difference (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-fmk
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