<![CDATA[Abstract. This study integrates Extreme Value Theory (EVT) and a C-Vine Copula model to design the trigger mechanism and premium structure of a flood parametric insurance scheme in Jakarta (DKI Jakarta). Using historical flood data from 2014–2020 (84 monthly observations) with an event-based analytical unit, the model incorporates maximum rainfall (mm), flood duration (days), and the number of affected neighborhood units (RW). The Generalized Extreme Value (GEV) distribution is employed to model rainfall extremes, while the Generalized Pareto Distribution (GPD) is applied to flood duration. A C-Vine Gumbel Copula is used to capture interdependence across variables. Estimation results show Kendall’s τ of 0.58 between rainfall and affected RWs, and 0.54 between flood duration and RWs, with goodness-of-fit validation (p > 0.05). The optimal trigger — rainfall > 175 mm, flood duration > 2.5 days, and > 120 affected RWs — yields a claim probability of 3.5% ± 1.2% and a basis risk of 18%. With an insured value of IDR 10 billion and a 20% loading factor, the annual premium is determined at IDR 428.4 million. The EVT–Copula integration enhances flood risk estimation accuracy and premium efficiency, providing a replicable probabilistic framework for the development of parametric insurance products in high-risk regions.]]>
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