Regime-Switching Temperature Dynamics Model for Weather Derivatives

Abstract

Weather is a key production factor in agricultural crop production and at the same time the most significant and least controllable source of peril in agriculture. These effects of weather on agricultural crop production have triggered a widespread support for weather derivatives as a means of mitigating the risk associated with climate change on agriculture. However, these products are faced with basis risk as a result of poor design and modelling of the underlying weather variable (temperature). In order to circumvent these problems, a novel time-varying mean-reversion Lévy regime-switching model is used to model the dynamics of the deseasonalized temperature dynamics. Using plots and test statistics, it is observed that the residuals of the deseasonalized temperature data are not normally distributed. To model the nonnormality in the residuals, we propose using the hyperbolic distribution to capture the semiheavy tails and skewness in the empirical distributions of the residuals for the shifted regime. The proposed regime-switching model has a mean-reverting heteroskedastic process in the base regime and a Lévy process in the shifted regime. By using the Expectation-Maximization algorithm, the parameters of the proposed model are estimated. The proposed model is flexible as it modelled the deseasonalized temperature data accurately. However, most farmers in Africa have rarely heard about this effective hedging tool. WD, if introduced in Africa, will be more viable, reliable, and efficient to the agricultural industry and can hedge against the increasing weather changes that affect agriculture since it is devoid of factors like loss adjustments, moral hazards, adverse selections, high premiums, and complex information requirements. To avoid

Pricing Gamma Based Temperature Derivatives

“…The conventional method typically involves assuming that temperature adheres to the probability distribution described by the Ornstein-Uhlenbeck stochastic process which posits that the residuals, when the trend reverts to the mean, follow a Gaussian probability distribution. However, the research done by Gyamerah et al (2018) suggests that there is a non-negligible probability that temperature residuals may not adhere to a normal distribution.…”

“…This discrepancy has prompted authors to utilize the normal inverse Gaussian distribution in order to depict data that displays skewness and possesses heavy-tailed characteristics. (Gyamerah et al, 2018).…”

Probabilistic forecasting of crop yields via quantile random forest and Epanechnikov Kernel function

“…For this reason, an effective and reliable insurance is needed to hedge farmers and stakeholders from the peril of weather uncertainties. Traditional insurance for agricultural risk management is not patronized in most developing countries because of high premiums, loss adjustments, moral hazards, adverse selections, and complex information requirements [8]. However, weather derivatives and indexbased insurance such as area-yield and weather index insurance are seen as effective risk management tools in the agricultural sector for both small-and large-scale farmers in developing/under-developed countries.…”

Weather Derivatives and the Market Price of Risk

“…The model (1.5) resurfaces in [4] and then in [12] with the volatility modeled as a stochastic process. Gyamerah et al in [9], proposed the so-called novel time-varying mean-reversion Levy regimeswitching model for the dynamics of the deseasonalized temperature. The authors, however, did not use the proposed model for pricing weather derivative contracts.…”