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The purpose of this paper is to propose a feasible model for the daily average temperatures of Beijing, Shanghai and Shenzhen, in order to price temperature‐based weather derivatives; also to derive analytical approximation formulas for the sensitivities of these contracts.

This study proposes a seasonal volatility model that estimates daily average temperatures of Beijing, Shanghai and Shenzhen using the mean‐reverting Ornstein‐Uhlenbeck process. It then uses the analytical approximation and Monte Carlo methods to price heating degree days and cooling degree days options for these cities. In addition, it derives and calculates the option sensitivities on the basis of an analytical approximation formula.

There exists a strong seasonality in the volatility of daily average temperatures of Beijing, Shanghai and Shenzhen. To model the seasonality Fourier approximation is applied to the squared volatility of daily temperatures. The analytical approximation formulas and Monte Carlo simulation produce very similar prices for heating/cooling degree days options in Beijing and Shanghai, a result that also verifies the convergence of the Monte Carlo and approximation estimators. However, the two methods do not produce converging option prices in the case of HDD options for Shenzhen.

The article provides important insight to investors and hedgers by proposing a feasible model for pricing temperature‐based weather contracts in China and derives analytical approximations for the sensitivities of heating/cooling degree days options.

The goal of the paper is to analyse the various issues attached to the valuation of weather derivatives. We focus our study on temperature‐related contracts since they are the most widely traded at this point and try to address the following questions: (i) should the quantity underlying the swaps or options contracts be defined as the temperature, degree‐days or cumulative degree‐days? This discussion is conducted both in terms of the robustness of the statistical modelling of the state variable and the mathematical valuation of the option (European versus Asian). (ii) What pricing approaches can tackle the market incompleteness generated by a non‐tradable underlying when furthermore the market price of risk is hard to identify in other traded instruments and unlikely to be zero? We illustrate our study on a database of temperatures registered at Paris Le Bourget and compare the calls and puts prices obtained using the different methods most widely used in weather markets.

The purpose of this paper is to analyze the efficiency of temperature-based weather derivatives (WD) in reducing risk exposure for Chinese agriculture industry. Therefore, a put option with cumulated growing degree days as its underlying index is assumed to be bought by farmers as a risk management instrument to prevent income fluctuations from adverse temperature conditions.

The objective of this paper is to analyze the efficiency of temperature-based WD in reducing risk exposure for Chinese agriculture industry. Therefore, a put option with cumulated growing degree days as its underlying index is assumed to be bought by farmers as a risk management instrument to prevent income fluctuations from adverse temperature conditions.

The results of the efficiency tests show that temperature-based put options are efficient in offsetting yield shortfalls for rice and wheat in China. The weather-yield models have a high prediction power in explaining yield variation by temperature.

The de-trending procedure for the weather-yield model should be improved to distinguish better between technology progress, human activities and influence of weather. Further, more advanced models could be used for the pricing.

The findings of the paper support the launch of WD as an efficient risk management tool for agriculture in China. Compared with traditional damage-based insurance, WD are more flexible, have lower transactions costs and avoid moral hazard or adverse selection.

The efficiency problem of WD has not been analyzed sufficiently worldwide and especially not for developing countries like China where a large proportion of the population works as farmers. This paper supports to fill this gap.

This paper aims to quantify the potentials for the development of combined heat and power (CHP) in Europe.

To this end, it uses the TIMES-EU energy-economic model and assesses the impact of key policy options and targets in the area of CO₂ emissions reduction, renewable energies and energy efficiency improvements. The results are also compared with the cogeneration potentials as reported by the Member States in their national reports.

The paper shows that CHP output could be more than doubled and that important CHP penetration potential exists in expanding the European district heating systems. This result is even more pronounced with the far-reaching CO₂ emissions reduction necessary in order to meet a long-term 2 degree target. Nevertheless, the paper also shows that strong CO₂ emission reductions in the energy sector might limit the CHP potential due to increased competition for biomass with the transport sector.

Given the proven socio-economic benefits of using CHP, the paper identifies the areas for future research in order to better exploit the potential of this technology such as the combination of CHP and district cooling or country- and industry-specific options to generate process heat.

The Equal Pay Act 1970 (which came into operation on 29 December 1975) provides for an “equality clause” to be written into all contracts of employment. S.1(2) (a) of the 1970 Act (which has been amended by the Sex Discrimination Act 1975) provides:

The purpose of this paper is to compare the ability of popular temperature models, namely, the models given by Alaton et al., by Benth and Benth, by Campbell and Diebold and by Brody et al., to forecast the prices of heating/cooling degree days (HDD/CDD) futures for New York, Atlanta, and Chicago.

To verify the forecasting power of various temperature models, a statistical backtesting approach is utilised. The backtesting sample consists of the market data of daily settlement futures prices for New York, Atlanta, and Chicago. Settlement prices are separated into two groups, namely, “in‐period” and “out‐of‐period”.

The findings show that the models of Alaton et al. and Benth and Benth forecast the futures prices more accurately. The difference in the forecasting performance of models between “in‐period” and “out‐of‐period” valuation can be attributed to the meteorological temperature forecasts during the contract measurement periods.

In future studies, it may be useful to utilize the historical data for meteorological forecasts to assess the forecasting power of the new hybrid model considered.

Out‐of‐period backtesting helps reduce the effect of any meteorological forecast on the formation of futures prices. It is observed that the performance of models for out‐of‐period improves consistently. This indicates that the effects of available weather forecasts should be incorporated into the considered models.

To the best of the author's knowledge this is the first study to compare some of the popular temperature models in forecasting HDD/CDD futures. Furthermore, a new temperature modelling approach is proposed for incorporating available temperature forecasts into the considered dynamic models.

This paper aims to develop a forecasting tool for the automatic assessment of both environmental and economic benefits resulting from low-carbon investments in the real estate sector, especially when applied in large building stocks. A set of four artificial neural networks (NNs) is created to provide a fast and reliable estimate of the energy consumption in buildings due to heating, hot water, cooling and electricity, depending on some specific buildings’ characteristics, such as geometry, orientation, climate or technologies.

The assessment of the building’s energy demand is performed comparing the as-is status (pre-retrofit) against the design option (post-retrofit). The authors associate with the retrofit investment the energy saved per year, and the net monetary saving obtained over the whole cost after a predetermined timeframe. The authors used a NN approach, which is able to forecast the buildings’ energy demand due to heating, hot water, cooling and electricity, both in the as-is and in the design stages. The design stage is the result of a multiple attribute optimization process.

The approach here developed offers the opportunity to manage energy retrofit interventions on wide property portfolios, where it is necessary to handle simultaneously a large number of buildings without it being technically feasible to achieve a very detailed level of analysis for every property of a large portfolio.

Among the major accomplishments of this research, there is the creation of a methodology that is not excessively data demanding: the collection of data for building energy simulations is, in fact, extremely time-consuming and expensive, and this NN model may help in overcoming this problem. Another important result achieved in this study is the flexibility of the model developed. The case study the authors analysed was referred to one specific stock, but the results obtained have a more widespread importance because it ends up being only a matter of input-data entering, while the model is perfectly exportable in other contexts.

Major segments of the U.S. economy are affected by weather. With the emergence of weather derivatives, exposure to weather‐related risk has evolved from being merely accepted. As a result, weather risk management strategies are increasingly being adopted in strategic decision‐making by senior management. Weather derivatives enable managers to focus on core operating risks by trading away those business exposures related to temperature, precipitation, snow level, etc. These contracts offer a unique opportunity to discretely trade a new category of risk, which was previously considered to be an inevitable cost of doing business. This article describes the weather derivatives market and its contracts and outlines the principles of pricing and risk analysis in weather markets. In closing, the article discusses the application of these products for portfolio and business risk management using illustrative examples from the energy markets.

The purpose of this paper is to model and to value a temperature derivative to hedge late frost risk in viticulture.

Starting from 11 years of historical temperature data collected in Mendoza, Argentina, the authors reconstruct the missing data using principal component analysis. The frequency content of time series is examined by the periodogram method; ordinary least squares are used to estimate the trends of minimum, maximum and average temperatures, and hypothesis tests of univariate and bivariate normality are performed on deseasonalized and filtered temperature returns. The authors express the temperature dynamics by correlated Ornstein‐Uhlenbeck processes and historical data were fitted into the model to obtain parameters estimates. An Asian‐type option on a temperature index is constructed and its price and sensitivities are computed by Monte Carlo method.

The authors define an index in terms of minimum and average temperatures that, under some simplifying hypotheses, quantifies the damage produced by a late frost. To hedge the late frost risk, an Asian‐type option on the index is constructed. Together with the results concerning the design and pricing of the option, the analysis of historical data reveals non‐negligible linear trends, negative in minimum temperature and positive in maximum and average temperatures. These findings may be consistent with the hypothesis of global warming or with the presence of out‐of‐phase very low frequency components.

The authors have not found in the literature a similar option to hedge the risk of spring frosts faced by fruit producers.

This article discusses the basics of computer‐room air conditioning, an important component of the special environment required by mainframe computers and many mini‐computers as well. Computer room air conditioners differ in some significant ways from “comfort” air‐conditioners, which are designed for the comfort of people rather than machines. These differences make it less than ideal to use air conditioning systems designed for human comfort for computer cooling. The author describes several different types of air‐conditioners, considerations related to the construction of a computer room, and factors that determine air‐conditioning requirements.