Paper Info
Title
An efficient and easily parallelizable algorithm for pricing weather derivatives
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.
Year
DOI
Venue
2005
10.1007/11666806_54
LSSC
Keywords
Field
DocType
parallel algorithm,times speedup,parallelizable algorithm,weather risk,parallel execution,cdd weather index,pricing weather derivative,fast gauss,various cdd derivative,new recurrence formula,conventional monte carlo method,cdd weather derivative,probability density function,monte carlo method,gaussian distribution,indexation
Monte Carlo method,Degree of parallelism,Parallel algorithm,Convolution,Algorithm,Gaussian process,Probability density function,Mathematics,Speedup,Weather derivative
Conference
Volume
ISSN
ISBN
3743
0302-9743
3-540-31994-8
Citations 
PageRank 
References 
1
0.42
6
Authors
1
Name
Order
Citations
PageRank
Yusaku Yamamoto15220.61