Penentuan Premi Asuransi Pertanian Padi Berbasis Indeks Risiko Iklim Multivariat Menggunakan Model Black-Scholes Dan Black-Scholes Fraksional

Arma, Nadhia Mulya (2026) Penentuan Premi Asuransi Pertanian Padi Berbasis Indeks Risiko Iklim Multivariat Menggunakan Model Black-Scholes Dan Black-Scholes Fraksional. Other thesis, Institut Teknologi Sepuluh Nopember.

Text 5006221092-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only Download (5MB) | Request a copy

Abstract

Perubahan iklim meningkatkan ketidakpastian produksi padi akibat fluktuasi berbagai unsur iklim, seperti curah hujan, suhu udara, kelembapan udara, dan radiasi matahari. Sebagian besar penelitian mengenai asuransi pertanian berbasis indeks masih menggunakan satu variabel iklim sebagai dasar penentuan premi, sehingga belum mampu merepresentasikan kompleksitas risiko iklim secara menyeluruh. Penelitian ini bertujuan membentuk indeks risiko iklim multivariat menggunakan Principal Component Analysis (PCA), menghitung premi asuransi pertanian berbasis indeks menggunakan model Black-Scholes dan Black-Scholes fraksional, serta membandingkan hasil premi dengan Asuransi Usaha Tani Padi (AUTP). Data yang digunakan berupa data agroklimatologi harian Kota Palu tahun 2016–2025 yang meliputi suhu udara, curah hujan, kelembapan udara, dan radiasi matahari, serta data produksi padi bulanan. PCA digunakan untuk mereduksi dimensi data dan menghasilkan satu komponen utama sebagai indeks risiko iklim. Selanjutnya, perhitungan premi dilakukan melalui pendekatan penentuan harga opsi dengan model Black-Scholes dan dikembangkan menggunakan model Black-Scholes fraksional untuk mengakomodasi karakteristik long memory pada data iklim. Hasil penelitian menunjukkan bahwa PCA berhasil membentuk indeks risiko iklim multivariat yang dapat digunakan sebagai underlying dalam penentuan premi asuransi berbasis indeks. Perhitungan premi menggunakan model Black-Scholes dan Black-Scholes fraksional menghasilkan nilai premi yang berbeda, baik pada indeks curah hujan maupun indeks risiko iklim berbasis PCA. Perbedaan tersebut menunjukkan bahwa karakteristik ketergantungan jangka panjang (long memory) dan pemilihan underlying memengaruhi estimasi premi. Selain itu, seluruh premi yang dihasilkan lebih tinggi dibandingkan premi AUTP. Hasil tersebut menunjukkan bahwa kedua model mampu merepresentasikan risiko iklim dalam penentuan premi, namun estimasi premi yang dihasilkan masih relatif tinggi sehingga diperlukan pengembangan model atau pendekatan penentuan premi yang lebih ekonomis agar dapat diterapkan secara praktis pada asuransi pertanian berbasis indeks.
=======================================================================================================================================
Climate change has increased the uncertainty of rice production due to fluctuations in multiple climatic variables, including rainfall, air temperature, humidity, and solar radiation. Most existing studies on index-based agricultural insurance determine premiums using a single climate variable, which is insufficient to represent the complExity of climate-related risks comprehensively. This study aims to construct a multivariate climate risk index using Principal Component Analysis (PCA), estimate agricultural insurance premiums using the Black-Scholes and fractional Black-Scholes models, and compare the estimated premiums with those of the Indonesian Rice Farming Insurance Program (Asuransi Usaha Tani Padi, AUTP). The study employs daily agroclimatological data from Palu City covering the period 2016–2025, consisting of air temperature, rainfall, humidity, and solar radiation, together with monthly rice production data. PCA was applied to reduce data dimensionality and generate a principal component representing the multivariate climate risk index. The resulting index was then used as the underlying variable in an option-pricing framework for premium calculation. The classical Black-Scholes model was further extended to the fractional Black-Scholes model to capture the long-memory characteristics commonly observed in climate data. The results indicate that PCA successfully generated a multivariate climate risk index suitable as the underlying variable for index-based agricultural insurance. Premium estimates obtained from the Black-Scholes and fractional Black-Scholes models differed for both the rainfall index and the PCA-based climate risk index, indicating that long-range dependence and the choice of underlying variable significantly influence premium estimation. Furthermore, all estimated premiums exceeded those of the existing AUTP scheme. These findings suggest that although both models are capable of incorporating climate risk into premium determination, the resulting premiums remain relatively high. Therefore, further methodological development is required to produce more economically feasible premium estimates for practical implementation in index-based agricultural insurance

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Asuransi pertanian, indeks risiko iklim, Principal Component Analysis (PCA), Black-Scholes, Black-Scholes fraksional, Agricultural insurance, climate risk index, Black–Scholes model, fractional Black–Scholes model
Subjects:	Q Science > QA Mathematics > QA278.5 Principal components analysis. Factor analysis. Correspondence analysis (Statistics)
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Actuaria > 94203-(S1) Undergraduate Thesis
Depositing User:	Nadhia Mulya Arma
Date Deposited:	20 Jul 2026 02:06
Last Modified:	20 Jul 2026 02:06
URI:	http://repository.its.ac.id/id/eprint/135437

Actions (login required)

View Item