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Weather Derivatives (weather + derivative)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting75%

Selected Abstracts

Pricing Weather Derivatives using a Predicting Power Time Series Process,

ASIA-PACIFIC JOURNAL OF FINANCIAL STUDIES, Issue 6 2009
Chuang-Chang Chang
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. [source]

Weather derivatives: Managing risk in a climate of change

NATURAL GAS & ELECTRICITY (PREVIOUSLY : NATURAL GAS), Issue 7 2000
Richard G. Morgan
[source]

Weather derivatives valuation and market price of weather risk

THE JOURNAL OF FUTURES MARKETS, Issue 11 2004
Melanie Cao
This paper has two objectives: (1) to propose and implement a valuation framework for temperature derivatives (a specific class of weather derivatives); and (2) to study the significance of the market price of weather risk. The objectives are accomplished by generalizing the Lucas model of 1978 to include the weather as another fundamental source of uncertainty in the economy. Daily temperature is modeled by incorporating such key properties as seasonal cycles and uneven variations throughout the year. The temperature variable is related to the aggregate dividend or output through both contemporaneous and lagged correlations, as corroborated by the data. Numerical analysis shows that the market price of weather risk is significant for temperature derivatives. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:1065,1089, 2004 [source]

Pricing Weather Derivatives using a Predicting Power Time Series Process,

ASIA-PACIFIC JOURNAL OF FINANCIAL STUDIES, Issue 6 2009
Chuang-Chang Chang
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. [source]