An efficient and easily parallelizable algorithm for pricing weather derivatives

Authors:
Yusaku Yamamoto
Affiliations:
Department of Computational Science & Engineering, Nagoya University, Nagoya, Japan
Venue:
LSSC'05 Proceedings of the 5th international conference on Large-Scale Scientific Computing
Year:
2005

Citing 6
Cited 1

The fast Gauss transform

SIAM Journal on Scientific and Statistical Computing
The fast Gauss transform with variable scales

SIAM Journal on Scientific and Statistical Computing
Numerical Recipes in FORTRAN: The Art of Scientific Computing

Numerical Recipes in FORTRAN: The Art of Scientific Computing
A New Error Estimate of the Fast Gauss Transform

SIAM Journal on Scientific Computing
Application of the Fast Gauss Transform to Option Pricing

Management Science
A Double-Exponential Fast Gauss Transform Algorithm for Pricing Discrete Path-Dependent Options

Operations Research

Efficient parallel implementation of a weather derivatives pricing algorithm based on the fast gauss trasform

IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a fast and highly parallel algorithm for pricing CDD weather derivatives, which are financial products for hedging weather risks due to higher-than average temperature in summer. To find the price, we need to compute the expected value of its payoff, namely, the CDD weather index. To this end, we derive a new recurrence formula to compute the probability density function of the CDD. The formula consists of multiple convolutions of functions with a Gaussian distribution and can be computed efficiently with the fast Gauss transform. In addition, our algorithm has a large degree of parallelism because each convolution can be computed independently. Numerical experiments show that our method is more than 10 times faster than the conventional Monte Carlo method when computing the prices of various CDD derivatives on one processor. Moreover, parallel execution on a PC cluster with 8 nodes attains up to six times speedup, allowing the pricing of most of the derivatives to be completed in about 10 seconds.