Pricing Weather Derivatives using a Predicting Power Time Series Process

NCUIR > School of Management at National Central University > Graduate Institute of Finance > journal & Dissertation > Item 987654321/29544

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29544

Title:	Pricing Weather Derivatives using a Predicting Power Time Series Process
Authors:	Chang,CC;Lin,JB;Shen,WM
Contributors:	財務金融所
Keywords:	MARKET
Date:	2009
Issue Date:	2010-06-29 20:30:33 (UTC+8)
Publisher:	中央大學
Abstract:	This paper extended the Cao-Wei (2004, JFM) model to construct a theoretical model for pricing weather derivatives in two significant ways. One adopted a time series model developed by Campbell and Diebold (2005, JASA) to describe the dynamics of temperature. The advantage of using Campbell and Diebold's time series model to describe the temperature dynamics is that it can not only take the conditional mean of temperature coming from trend, seasonal, and cyclical components but also allow for the conditional variance dynamics. The other purpose of this paper is to use an extended power utility function, instead of Cao and Wei's constant proportional risk aversion (CPRA) utility function. The extended power utility function could exhibit decreasing, constant, and increasing relative risk aversion. Eventually, we find that the prices of weather derivatives can be determined by weather conditions, discount factors, and forward premiums. Additionally, these sources have close relations with some risk aversion parameters. Furthermore, the results are consistent with Cao and Wei's condition under some specific parameter assumptions.
Relation:	ASIA-PACIFIC JOURNAL OF FINANCIAL STUDIES
Appears in Collections:	[Graduate Institute of Finance] journal & Dissertation

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