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"Weather derivatives."
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Improving the Efficiency of Hedge Trading Using Higher-Order Standardized Weather Derivatives for Wind Power
Since the future output of wind power generation is uncertain due to weather conditions, there is an increasing need to manage the risks associated with wind power businesses, which have been increasingly implemented in recent years. This study introduces multiple weather derivatives of wind speed and temperature and examines their effectiveness in reducing (hedging) the fluctuation risk of future cash flows attributed to wind power generation. Given the diversification of hedgers and hedging needs, we propose new standardized derivatives with higher-order monomial payoff functions, such as “wind speed cubic derivatives” and “wind speed and temperature cross-derivatives,” to minimize the cash flow variance and develop a market-trading scheme to practically use these derivatives in wind power businesses. In particular, while demonstrating the importance of standardizing weather derivatives regarding market liquidity and efficiency, we propose a strategy to narrow down the required number (or volume) of traded instruments and improve trading efficiency by utilizing the least absolute shrinkage and selection operator (LASSO) regression. Empirical analysis reveals that higher-order, multivariate standardized derivatives can not only enhance the out-of-sample hedge effect but also help reduce trading volume. The results suggest that diversification of hedging instruments increases transaction flexibility and helps wind power generators find more efficient portfolios, which can be generalized to risk management practices in other businesses.
Journal Article
HEDGING BY USING WEATHER DERIVATES IN WINTER SKI TOURISM
Tourism, as one of the main driving forces of economic development, is exposed to many risks. Besides frequent fluctuations in foreign currency exchange, prices of fuel and transportation, the tourism industry has become more sensitive to weather conditions lately. One of the new instruments which can be efficiently used for weather risk hedging is weather derivatives (forwards, futures, options and swaps on chosen weather variables - temperature, rain, snow, wind etc.). In this paper, we will present the possibility of weather derivatives application in winter tourism - snowfall forwards - in order to hedge the business of ski lift operator company. Our research is based on snowfall data of Kopaonik mountain ski resort and revenues of ski lift operator company. We will show that weather derivatives might be an effective tool for hedging weather risk and reducing the volatility of companies revenues in the winter ski tourism business in Serbia.
Journal Article
Minimizing the impact of geographical basis risk on weather derivatives
In the last decade, the index-based weather products (also called weather derivatives) have been gaining attention in the climate resilience discussion. Weather derivatives are designed to help companies hedging against climate variability. These products, that can be market-traded or over-the-counter, compensate individuals based on a pre-defined weather index. Thus, pay-offs of a weather derivative depend on a weather index and not, as with traditional types of insurance, on the actual amount of money lost due to adverse weather. One of the major drawbacks that may prevent weather derivatives to catch on is the impact of the Geographical Basis Risk (GBR), that is the deviation of weather conditions at different locations. In fact, when the reference weather station is not located in the immediate vicinity of the site of interest the hedging effectiveness may be reduced. In this paper, we contribute to the existing literature on GBR by proposing an optimization method that may help in offering a tailored solution, while at the same time keeping a standardized instrument as a reference. Using a historical record of Italian temperatures, strikes for temperatures are the choice variables of a penalty function containing pay-offs of a reference station and all other stations. Further, altitude and latitude of meteorological stations are shown to be relevant predictors to explain GBR. This can be an interesting starting point for the design of weather derivatives, since, from a unique station where the “reference” derivative is priced, all the other stations may be easily settled.
Journal Article
Weather Risk Management in Energy Sector: The Polish Case
The energy sector is perceived as one of the most exposed sectors to the consequences of weather risk both directly (damages of its infrastructure) and indirectly (frictions to the energy supply–demand balance). The main aim of this paper is to provide an insight into the impact of weather risk on economic activity of companies operating in the energy sector in Poland. The empirical objective is to examine whether energy companies: (i) identify their relevant weather risk exposures; (ii) evaluate the impact of weather risk in the cost-revenues dimension; and (iii) implement weather risk management tools, in this case—weather derivatives. In a methodical context, this study relies on a unique research approach and derives from works that examine companies’ risk disclosures in annual reports, by applying textual content analysis. The results indicate that Polish energy companies recognize the impact of weather risk on their performance, also in the cost-revenues dimension. However, although the reported weather risk management methods were diversified, the examined companies did not use weather derivatives to hedge their weather risk exposures. In the overall dimension, the companies leading with the perception and management of weather risk were diversified regarding performance and market size.
Journal Article
A spatially-continuous neural network temperature model for weather derivatives evaluation
This manuscript develops a spatially continuous temperature model for pricing weather derivatives. We formulate temperature at any specific point as a function of its longitude and latitude coordinates. Using satellite climate data, we learn this function using deep neural networks and use it to model and forecast temperature. More specifically, our model allows for describing temperature dynamics at any point within the zone of interest, even when there is no available temperature data at that specific location. This approach enhances our ability to evaluate climate risk with greater precision across different regions. Furthermore, we design a neural network architecture that maintains model explainability, which is crucial for promoting the use of these methods in real-life applications. Through numerical experiments with NASA-MERRA-2 satellite data, we illustrate the model’s application in pricing Heating Degree Day (HDD) derivatives. Additionally, we explore potential extensions of the model to improve forecasting accuracy further.
Journal Article
Exploring the financial risk of a temperature index: a fractional integrated approach
This paper introduces a new temperature index, which can suitably represent the underlying of a weather derivative. Such an index is defined as the weighted mean of daily average temperatures measured in different locations. It may be used to hedge volumetric risk, that is the effect of unexpected fluctuations in the demand/supply for some specific commodities—of agricultural or energy type, for example—due to unfavorable temperature conditions. We aim at exploring the long term memory property of the volatility of such an index, in order to assess whether there exist some long-run paths and regularities in its riskiness. The theoretical part of the paper proceeds in a stepwise form: first, the daily average temperatures are modeled through autoregressive dynamics with seasonality in mean and volatility; second, the assessment of the distributional hypotheses on the parameters of the model is carried out for analyzing the long term memory property of the volatility of the index. The theoretical results suggest that the single terms of the index drive the long memory of the overall aggregation; moreover, interestingly, the proper selection of the parameters of the model might lead both to cases of persistence and antipersistence. The applied part of the paper provides some insights on the behaviour of the volatility of the proposed index, which is built starting from single daily average temperature time series.
Journal Article
Temperature forecasting and derivatives pricing in the Yangtze River economic belt of China
In the Yangtze River Economic Belt, climate variability significantly affects sectors such as agriculture, energy, and related industries, making accurate temperature forecasting essential. This study develops and compares Seasonal Autoregressive Integrated Moving Average (SARIMA) and Ornstein-Uhlenbeck (O-U) models using temperature data from 11 provincial-level regions spanning 2004–2023. After model evaluation, the more accurate forecasting model was selected to support the design of temperature-indexed weather derivatives via option pricing theory. Our findings reveal: (1) temperature series become stationary after first-order differencing, validating their suitability for time series modeling; (2) both SARIMA and O-U models produce predictions closely aligned with observed data; (3) SARIMA exhibits lower forecasting errors for cumulative cooling degree days (CDDs), confirmed through Monte Carlo simulations; and (4) option pricing results show that increased climate volatility raises derivative premiums, reflecting heightened climate risk.This research demonstrates the potential of weather derivatives as a risk mitigation tool in the Yangtze River Economic Belt, contributing to climate-resilient economic development.
Journal Article
Stochastic modelling of temperature for pricing weather derivatives
We employ the modified Ornstein-Uhlenbeck model with a seasonal mean and stochastic volatility process to model the daily average temperature (DAT) of Bono region in Ghana. The study findings show that the daily average temperature in the Bono region reverts to a temperature of approximately 26° C at a rate of 18.72% with maximum and minimum temperatures of 32.67° C and 19.75° C, respectively. Although the Bono region is in the middle belt of Ghana, it experiences warm temperatures and experiences dry seasons relatively more than wet seasons in the number of years considered in our analysis. The findings from the study are relevant in the pricing of weather derivatives with temperature as the underlying variable in the financial and agricultural sector. Furthermore, it would assist in the development and design of tailored agriculture insurance models by incorporating the dynamics of temperature.
Journal Article
Do Weather Derivatives Mitigate the Revenue Risk of Farmers?—The Case of Tongliao, Inner Mongolia, China
This research probes the potential of weather derivatives as tools for mitigating the variability of crop yields due to climatic uncertainties in China. Centered on Tongliao City in Inner Mongolia, the study exploits a long short-term memory (LSTM) network to dissect and simulate 32 years of local precipitation data, thereby achieving a simulation of high reliability. Further exploration through a multiple linear regression model confirms a marked positive relationship between rainfall amounts and maize yields. By combining precipitation put options and the total revenue function for farmers, mathematical derivations yield specific expressions for optimal trading quantities and risk hedging efficiency. The research findings show that, using an assumption of a maize price that is 3 CNY/kg, when farmers purchase around 6.22 precipitation put options they can achieve 67.9% risk hedging efficiency. This highlights the significant role of precipitation put options under specific conditions in reducing the risk of decreased maize yields due to reduced precipitation. However, in practical markets, variations in maize prices and the price change unit (λ) are inevitable. Through further analysis, this study reveals that as these factors change, the optimal trading quantity and hedging efficiency also undergo varying degrees of adjustment. The investigation lays a theoretical groundwork for the practical application and empirical validation of weather derivatives within China’s agrarian sector. However, the study underscores the necessity of a holistic approach to market dynamics to refine hedging strategies. Future decision making must integrate market fluctuations, and adopting transparent pricing mechanisms is critical for enhanced risk management and the advancement of sustainable agricultural practices.
Journal Article