Estimating the Payoffs of Temperature-based Weather Derivatives

Temperature-based weather derivatives are written on an index which is normally defined to be a nonlinear function of average daily temperatures. Recent empirical work has demonstrated the usefulness of simple time-series models of temperature for estimating the payoffs to these instruments. This paper argues that a more direct and parsimonious approach is to model the time-series behaviour of the index itself, provided a sufficiently rich supply of historical data is available. A data set comprising average daily temperature spanning over a hundred years for four Australian cities is assembled. The data is then used to compare the actual payoffs of temperature-based European call options with the expected payoffs computed from historical temperature records and two time-series approaches. It is concluded that expected payoffs computed directly from historical records perform poorly by comparison with the expected payoffs generated by means of competing time-series models. It is also found that modeling the relevant temperature index directly is superior to modeling average daily temperatures.Temperature, Weather Derivatives, Cooling Degree Days, Time-series Models.

Identification of stochastic processes for an estimated icewine temperature hedging variable

Weather derivatives are a relatively new form of financial security that can provide firms with the ability to hedge against the impact of weather related risks to their activities. Participants in the energy industry have employed standardized weather contracts trading on organized exchanges since 1999 and the interest in non-standardized contracts for specialized weather related risks is growing at an increasing rate. The purpose of this paper is to examine the potential use of weather derivatives to hedge against temperature related risks in Canadian ice wine production. Specifically we examine historical data for the Niagara region of the province of Ontario, Canada, the largest icewine producing region of the world, to determine an appropriate underlying variable for the design of an option contact that could be employed by icewine producers. Employing monte carlo simulation we derive a range of benchmark option values based upon varying assumptions regarding the stochastic process for an underlying temperature variable. The results show that such option contracts can provide valuable hedging opportunities for producers, given the historical seasonal temperature variations in the region.wine market, weather derivatives, weather hedging, Agribusiness, Agricultural Finance, Crop Production/Industries, Environmental Economics and Policy, G13, G32, Q14, Q51, Q54,

Modeling and Forecasting CAT and HDD Indices For Weather Derivative Pricing

In this paper, we use wavelet neural networks in order to model a mean-reverting Ornstein–Uhlenbeck temperature process, with seasonality in the level and volatility and time-varying speed of mean reversion. We forecast up to 2 months ahead out of sample daily temperatures, and we simulate the corresponding Cumulative Average Temperature and Heating Degree Day indices. The proposed model is validated in 8 European and 5 USA cities all traded in the Chicago Mercantile Exchange. Our results suggest that the proposed method outperforms alternative pricing methods, proposed in prior studies, in most cases. We find that wavelet networks can model the temperature process very well and consequently they constitute an accurate and efficient tool for weather derivatives pricing. Finally, we provide the pricing equations for temperature futures on Cooling and Heating Degree Day indices

Developing analytical distributions for temperature indices for the purposes of pricing temperature-based weather derivatives

Temperature-based weather derivatives are written on an index which is normally defined to be a nonlinear function of average daily temperatures. Recent empirical work has demonstrated the usefulness of simple time-series models of temperature for estimating the payoffs to these instruments. This paper develops analytical distributions of temperature indices on which temperature derivatives are written. If deviations of daily temperature from its expected value is modelled as an Ornstein-Uhlenbeck process with time-varying variance, then the distributions of the temperature index on which the derivative is written is the sum of truncated, correlated Gaussian deviates. The key result of this paper is to provide an analytical approximation to the distribution of this sum, thus allowing the accurate computation of payoffs without the need for any simulation. A data set comprising average daily temperature spanning over a hundred years for four Australian cities is used to demonstrate the efficacy of this approach for estimating the payoffs to temperature derivatives. It is demonstrated that expected payoffs computed directly from historical records is a particulary poor approach to the problem when there are trends in underlying average daily temperature. It is shown that the proposed analytical approach is superior to historical pricing.Weather Derivatives, Temperature Models, Cooling Degree Days, Maximum Likelihood Estimation, Distribution for Correlated Variables

WEATHER DERIVATIVES: MANAGING RISK WITH MARKET-BASED INSTRUMENTS

Accurate pricing of weather derivatives is critically dependent upon correct specification of the underlying weather process. We test among six likely alternative processes using maximum likelihood methods and data from the Fresno, CA weather station. Using these data, we find that the best process is a mean-reverting geometric Brownian process with discrete jumps and ARCH errors. We describe a pricing model for weather derivatives based on such a process.Risk and Uncertainty,

Meteorological forecasts and the pricing of weather derivatives

In usual pricing approaches for weather derivatives, forward-looking information such as meteorological weather forecasts is not considered. Thus, important knowledge used by market participants is ignored in theory. By extending a standard model for the daily temperature, this paper allows the incorporation of meteorological forecasts in the framework of weather derivative pricing and is able to estimate the information gain compared to a benchmark model without meteorological forecasts. This approach is applied for temperature futures referring to New York, Minneapolis and Cincinnati with forecast data 13 days in advance. Despite this relatively short forecast horizon, the models using meteorological forecasts outperform the classical approach and more accurately forecast the market prices of the temperature futures traded at the Chicago Mercantile Exchange (CME). Moreover, a concentration on the last two months or on days with actual trading improves the results.Weather forecasting, weather risk, price forecasting, nancial markets, temperature futures, CME

New Weather Indices for China: Tool of Risk Control of International Supply Chain

China is at the core of the world’s supply chain because of its focus on production and consumption. However, as weather can significantly affect supply chain operations, China plans to introduce weather derivatives to secure the multinational supply chain. Using historical records over the decade, weather derivatives could be an important tool for hedging risk and meeting the needs of Chinese market. In this paper, new weather indices for China financial markets are experimentally created through simulated machine learning to assess the ability of the weather indices to reduce risk. Through a simulation test from 2008 to 2017, the indices were found to successfully match 98% of the risk with the situation across two dimensions: i). changing Chinese weather data; and ii). a connection with US weather indices

Cross-city hedging with weather derivatives using bivariate DCC GARCH models

As monopolies gave their way to competitive wholesale electricity markets, volumetric risk came into play. Electricity supplier can buy weather derivatives to protect from volumetric risk due to unexpected weather conditions. However, contracts can only be negotiated for weather variables measured at few selected locations. To hedge their specific risk, electricity supplier have to correlate their risk with the risk at tradeable locations. In this paper, we concentrate on temperature derivatives. More precisely, we examine if and how bivariate GARCH models with dynamic conditional correlations can help in modelling correlation between two distinct temperature time series. The knowledge of correlation dynamics between the temperature time series enables an electricity supplier to correlate his risk with the risk of a traded city and to construct a sensible hedge. It turns out that the application of bivariate DCC GARCH models to three German temperature time series provides encouraging results. --