我院青年教师Ahmet Goncu博士的题为“Generating low-discrepancy sequences from the normal distribution: Box-Muller or inverse transform?”的论文于2010年11月4日被国际核心刊物Mathematical and Computer Modelling（SCI收录）接受。另一篇题为“Pricing temperature-based weather contracts: an application to China”的论文也于2010年11月1日被国际核心刊物Applied Economics Letters（SSCI收录）接受。
Generating low-discrepancy sequences from the normal distribution: Box-Muller or inverse transform?
G. Okten and A. Goncu
ForthcomingonMathematical and Computer Modelling
Accepted onNovember 4th, 2010
Abstract
Quasi-Monte Carlo simulation is a popular numerical method in applications, in particular, economics and finance. Since the normal distribution occurs frequently in economic and financial modeling, one often needs a method to transform low-discrepancy sequences from the uniform distribution to the normal distribution. Two well known methods used with pseudorandom numbers are the Box-Muller and the inverse transformation methods. Some researchers and financial engineers have claimed that it is incorrect to use the Box-Muller method with low-discrepancy sequences, and instead, the inverse transformation method should be used. In this paper we prove that the Box-Muller method can be used with low-discrepancy sequences, and discuss when its use could actually be advantageous. We also present numerical results that compare Box-Muller and inverse transformation methods.
Pricing temperature-based weather contracts: an application toChina
A. Goncu
Forthcoming on Applied Economics Letters
Accepted onNovember 1st, 2010
Abstract
This paper is the first study to price temperature-based weather derivatives based on the daily average temperatures of Chinese cities, namelyBeijing,Shanghai, and Shenzhen. The dynamic model with the piecewise constant volatility function, which is proposed by Alaton et al. (2002), is used for pricing heating degree days and cooling degree days options. Price estimates for these options are obtained usingMonte Carlosimulation and analytical approximation methods.
