Originally published in 2005, Weather Derivative Valuation covers all the meteorological, statistical, financial and mathematical issues that arise in the pricing and risk management of weather derivatives. There are chapters on meteorological data and data cleaning, the modelling and pricing of single weather derivatives, the modelling and valuation of portfolios, the use of weather and seasonal forecasts in the pricing of weather derivatives, arbitrage pricing for weather derivatives, risk management, and the modelling of temperature, wind and precipitation. Specific issues covered in detail include the analysis of uncertainty in weather derivative pricing, time-series modelling of daily temperatures, the creation and use of probabilistic meteorological forecasts and the derivation of the weather derivative version of the Black-Scholes equation of mathematical finance. Written by consultants who work within the weather derivative industry, this book is packed with practical information and theoretical insight into the world of weather derivative pricing.

​Weather derivatives are financial instruments that can be used by organizations or individuals as part of a risk management strategy to minimize risk associated with adverse or unexpected weather conditions. Just as traditional contingent claims, a weather derivative has an underlying measure, such as: rainfall, wind, snow or temperature. Nearly $1 trillion of the U.S. economy is directly exposed to weather-related risk. More precisely, almost 30% of the U.S. economy and 70% of U.S. companies are affected by weather. The purpose of this monograph is to conduct an in-depth analysis of financial products that are traded in the weather market. Presenting a pricing and modeling approach for weather derivatives written on various underlying weather variables will help students, researchers, and industry professionals accurately price weather derivatives, and will provide strategies for effectively hedging against weather-related risk. This book will link the mathematical aspects of the modeling procedure of weather variables to the financial markets and the pricing of weather derivatives. Very little has been published in the area of weather risk, and this volume will appeal to graduate-level students and researchers studying financial mathematics, risk management, or energy finance, in addition to investors and professionals within the financial services industry. ​

We discuss how weather forecasts can be used in the pricing of weather derivatives and derive results for the most important types of weather index and contract. We show that calculating the expected payoff of linear contracts on linear indices requires only forecasts of the mean temperature over the contract period. Calculating the expected payoff of linear contracts on non-linear indices requires forecasts of both the mean and the distribution of temperatures, but not of the dependence between temperature distributions on different days. Calculating the expected payoff of non-linear contracts requires forecasts of the full multivariate distribution of temperature over the whole contract. For contracts that extend beyond the end of the available forecasts, correlations between the forecast and the post-forecast periods must be taken into account when estimating this distribution. We present two methods by which this can be achieved, both of which combine information from climatological models of daily temperature with information from probabilistic forecasts.

Bachelor Thesis from the year 2013 in the subject Business economics - Investment and Finance, grade: 4/4, , language: English, abstract: 1. Abstract This paper analyses weather derivatives and the issue of pricing these financial instruments. The non-tradability of the underlying makes their pricing not straightforward and even if the Chicago Mercantile Exchange began trading the first weather contract in 1999, the market still witnesses very low volumes and is relatively illiquid. This theoretical analysis is focused on instruments whose underlying is temperature, since they are the most traded. Due to the assumption of informational efficient markets, all available information should theoretically be included in the prices. However most existing models focus only on historical observations of temperature, actually excluding some relevant information. The few models that have instead considered weather forecasts are analysed, and in particular the model introduced by Ritter, Musshoff, and Odening to price temperature monthly futures including weather forecasts is described in details. I’ve performed an analysis applying a simplified version of the model described, based on temperature data from Tampa, Florida, in 2007. The results show that models with meteorological forecasts indeed outperform models that ignore them.

This paper proposes and implements an equilibrium framework for valuing weather derivatives. We generalize the Lucas model of 1978 to include the daily temperature as a fundamental variable in the economy. Temperature behavior in the past twenty years is closely studied for five major cities in the U.S., and a model is then proposed for the daily temperature variable which incorporates all the key properties of temperature behavior including seasonal cycles and uneven variations throughout the year. The model system is then estimated using the 20-year data and numerical analysis is subsequently performed for forward and option contracts on HDD's and CDD's. Key advantages of our model include the use of weather forecasts as inputs and the ability to handle contracts of any maturity, for any season. Numerical analysis shows that within our framework, the market price of risk associated with the temperature variable does not appear to impact the weather derivatives' value in a significant way, which implies it is indeed justifiable to use the riskfree rate to derive weather derivatives' values as many practitioners do in the industry. Finally, we show that the so-called historical simulation method can lead to significant pricing errors due to its erroneous implicit assumptions.

Forecasting based pricing of Weather Derivatives (WDs) is a new approach in valuation of contingent claims on nontradable underlyings. Standard techniques are based on historical weather data. Forward-looking information such as meteorological forecasts or the implied market price of risk (MPR) are often not incorporated. We adopt a risk neutral approach (for each location) that allows the incorporation of meteorological forecasts in the framework of WD pricing. We study weather Risk Premiums (RPs) implied from either the information MPR gain or the meteorological forecasts. The size of RPs is interesting for investors and issuers of weather contracts to take advantages of geographic diversification, hedging effects and price determinations. By conducting an empirical analysis to London and Rome WD data traded at the Chicago Mercantile Exchange (CME), we find out that either incorporating the MPR or the forecast outperforms the standard pricing techniques.