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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
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
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
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
Enhancing Sustainability through Weather Derivative Option Contracts: A Risk Management Tool in Greek Agriculture
This paper investigates the efficacy of weather derivatives as a risk management tool in the agricultural sector of Naousa, Greece, focusing on tree crops sensitive to temperature variations. The specific purpose is to assess how effectively weather derivative options can mitigate financial risks for farmers by providing strategic solutions. The study assesses the strategic application of Heating Degree Days (HDD) index options and their potential to alleviate economic vulnerabilities faced by farmers due to temperatures fluctuations. Employing different strike prices in Long Call and Straddle options strategies on the HDD index, the research offers tailored risk management solutions that cater to varying risk aversions among farmers. Moreover, the study applies the Value at Risk (VaR) methodology to quantify the financial security that weather derivatives can furnish, revealing a significantly reduced probability of severe financial losses in hedged scenarios compared to no-hedge conditions. Results show that all implemented strategies effectively enhance financial outcomes compared to scenarios without hedging, highlighting the exceptional utility of weather derivatives as risk management tools in the agricultural sector. Strategy 4, which exhibits the lowest VaR, emerges as the most effective, providing substantial protection against adverse weather conditions. This research supports the notion that weather derivatives can substantially contribute to the economic sustainability of rural economies, influencing policy decisions toward enhancing financial instruments for risk management in agriculture.
Journal Article
Customized yet Standardized Temperature Derivatives: A Non-Parametric Approach with Suitable Basis Selection for Ensuring Robustness
Previous studies have demonstrated that non-parametric hedging models using temperature derivatives are highly effective in hedging profit/loss fluctuation risks for electric utilities. Aiming for the practical applications of these methods, this study performs extensive empirical analyses and makes methodological customizations. First, we consider three types of electric utilities being exposed to risks of “demand”, “price”, and their “product (multiplication)”, and examine the design of an appropriate derivative for each utility. Our empirical results show that non-parametrically priced derivatives can maximize the hedge effect when a hedger bears a “price risk” with high nonlinearity to temperature. In contrast, standard derivatives are more useful for utilities with only “demand risk” in having a comparable hedge effect and in being liquidly traded. In addition, the squared prediction error derivative on temperature has a significant hedge effect on both price and product risks as well as a certain effect on demand risk, which illustrates its potential as a new standard derivative. Furthermore, spline basis selection, which may be overlooked by modeling practitioners, improves hedge effects significantly, especially when the model has strong nonlinearities. Surprisingly, the hedge effect of temperature derivatives in previous studies is improved by 13–53% by using an appropriate new basis.
Journal Article
Wind Put Barrier Options Pricing Based on the Nordix Index
Wind power generators face risks derived from fluctuations in market prices and variability in power production, generated by their high dependence on wind speed. These risks could be hedged using weather financial instruments. In this research, we design and price an up-and-in European wind put barrier option using Monte Carlo simulation. Under the existence of a structured weather market, wind producers may purchase an up-and-in European wind barrier put option to hedge wind fluctuations, allowing them to recover their investments and maximise their profits. We use a wind speed index as the underlying index of the barrier option, which captures risk from wind power generation and the Autoregressive Fractionally Integrated Moving Average (ARFIMA) to model the wind speed. This methodology is applied in the Colombian context, an electricity market affected by the El Niño phenomenon. We find that when the El Niño phenomenon occurs, there are incentives for wind generators to sell their energy to the system because their costs, including the put option price, are lower than the power prices. This research aims at encouraging policymakers and governments to promote renewable energy sources and a financial market to trade options to reduce uncertainty in the electrical system due to climate phenomena.
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