Return to search

Inventory models with weather derivatives and weather-conditional rebates for seasonal products. / CUHK electronic theses & dissertations collection / ProQuest dissertations and theses

Key words. Newsvendor Model, Inventory Model, Seasonal Product, Weather Risk, Weather Option, Weather Derivative, Weather-Conditional Rebate, CVaR, Mean-CVaR. / The first model considers the problem of hedging inventory risk for a newsvendor who sells a seasonal product. The newsvendor not only decides the order quantity, but also adopts a weather hedging strategy. A typical hedging strategy is to use an option that is constructed on a weather index before the season begins, which will compensate the buyer of the option if the actual seasonal weather index is above (or below) a given strike level. We explore the joint decision problem in mean-variance, expected utility, conditional value-at-risk (CVaR), and mean-CVaR frameworks. We analyze the impact of weather hedging on optimal order quantity. It is proven that the newsvendor may order more than in the absence of weather options. Numerical analysis on the sensitivity of the optimal order quantity, the risk premium of the option, the portfolio selection and the comparison between the weather option hedging and a particular operational hedging are presented as well. / The second model investigates the advantages of early sales of a seasonal product. To induce early sales, the newsvendor adopts a weather-conditional rebate program, which will pay rebates to the customers who buy the product in the preselling period if a specified weather condition for normal selling season is realized. For an example, a certain amount of refund will be paid to early buyer if the seasonal average temperature falls below the past-three-year seasonal average. Two conditional rebate programs with early booking and early purchasing are investigated and compared. Both of them can price differentiate within a customer among his/her post valuation on the seasonal product, and thus increase the sales. For the early purchasing program, it can further save inventory holding cost and ordering cost. The expected profit can be improved by the programs. Moreover, combined with weather derivatives, the conditional rebate program can manage the financial risk with the expected profit being still improved. / To investigate the means that firms may adopt in managing the adverse impacts of weather on their businesses, this dissertation proposes and analyzes two inventory models for seasonal products when the demand is sensitive to the weather in the season. Both models are formulated under the newsvendor context. / Gao, Fei. / "October 2007." / Adviser: Youhua Frank Chen. / Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 5002. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 108-119). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest dissertations and theses, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_344134
Date January 2007
ContributorsGao, Fei, Chinese University of Hong Kong Graduate School. Division of Systems Engineering and Engineering Management.
Source SetsThe Chinese University of Hong Kong
LanguageEnglish, Chinese
Detected LanguageEnglish
TypeText, theses
Formatelectronic resource, microform, microfiche, 1 online resource (xiii, 119 p. : ill.)
RightsUse of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Page generated in 0.0022 seconds