Non-linear time-varying stochastic models for agroclimate risk assessment

This work develops a model for minimum temperature in order to assess the weather related risk in agriculture industry. Non-linear autoregressive models with time-varying coefficients and volatility with various seasonal components and lags are compared in an appropriate model-selection algorithm using AIC. The optimal model is a time-varying autoregressive model which includes non-linear and seasonally-varying autoregressive terms as well as time-varying volatility. These models are then used to simulate future weather from which the probabilities of appropriate complex hazard events are estimated.