A HYBRID APPROACH IN CONSTRUCTING AN INTERNAL SOLVENCY MODEL

Date

2023-9-04

Author

Hasgül, Etkin

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This thesis presents a innovative hybrid approach combining Time Series, Artificial Neural Network (ANN), and Copula models for calculating the Solvency Capital Requirement (SCR) concerning Non-Life Premium Risk across multiple lines of businesses (LoB). The loss ratio is formulated as $Z_{i}=\hat{X}_{i}+\hat{Y}_{i}+\varepsilon_{i}$, where $\hat{X}_{i}$ represents the Time Series component, and $\hat{Y}_{i}$ denotes the ANN component. Initially, the loss ratios are subjected to modeling through suitable time series models to capture the linear component of the model. Subsequently, the appropriate autoregressive neural network (NNAR) model is applied to the residuals resulting from the time series modeling for each LoB, representing the non-linear component. Lastly, the residuals of the combined model are modeled using an R-vine structure. By utilizing these models and the copula structure, simulated loss ratios are generated for the selected LoBs, enabling the analysis of Non-Life Premium Risk. The comparison with the Standard SCR model and the proposed model incorporating VaR and TVaR is performed to assess the efficacy of the proposed approach.

Subject Keywords

SARIMA, NNAR, ANN, COPULA, R-vine, VaR, TVaR, SCR, Premium Risk

URI

https://hdl.handle.net/11511/105416

Collections

Graduate School of Applied Mathematics, Thesis