| Summary: | In the presented thesis, we build the theoretical framework for the development of temperature-based weather derivatives market in China. Our research is divided into two separate studies due to their different scopes. In the first study, we focus on the determination of the most precise model for temperature-based weather derivative modeling and pricing in China. To achieve this objective, a heuristic comparison of the new stochastic seasonal variation (SSV) model with three established empirical temperature and pricing models, i.e. the Alaton model [1], the CAR model [2] and the Spline model [3] is conducted. Comparison criteria include residual normality, residual auto-correlation function (ACF), Akaike information criterion (AIC), relative errors, and stability of price behaviors. The results show that the SSV model dominates the other three models by providing both a more precise fitting of the temperature process and more stable price behaviors. In the second study, novel forms of temperature indices are proposed and analyzed both on the city level and the climatic zone level, with the aim to provide a contract-selecting scheme that increases the risk management efficiency in the agricultural sector of China. Performances of the newly-introduced indices are investigated via an efficiency test which considers the root mean square loss (RMSL), the value at risk (VaR) and the certainty-equivalent revenues (CERs). According to the results, agricultural risk management on the city scale can be optimized by using the absolute-deviation growth degree-day (GDD) index. On the other hand, it is suggested that climatic zone-based contracts can be more efficient compared with city-based contracts. The recommended contract-selection scheme is to purchase climatic zone-based average GDD contracts in climatic zone II, and to purchase climatic zone-based optimal-weighted GDD contracts in climatic zone I or III.
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