Time-Varying Copula Models for Longitudinal Data

PurposeFarmer exchange rate (FER) is the ratio between a farmer's income and expenditure and is also an indicator of farmers’ welfare. There is little research regarding its use in risk modeling in crop insurance. This study seeks to propose a design for a household margin insurance scheme of the agricultural sector based on FER.Design/methodology/approachThis research employs various risk modeling concepts, i.e. value at risk, loss models and premium calculation, to construct the proposed model. The standard linear, static and time-varying copula models are used to identify the dependency between variables involved in calculating FER.FindingsFirst, FER can be considered as the primary variable for risk modeling in agricultural household margin insurance because it demonstrates farmers’ financial ability. Second, temporal dependence estimated using the time-varying copula can minimize errors, reduce the premium rate and result in a tighter guarantee's level of security.Originality/valueThis research extends the previous similar studies related to the use of index ratio in margin insurance loss modeling. Its authenticity is in the use of FER, which represents the farmers' trading capability. FER determines farmers’ losses by considering two aspects: the farmers’ income rate and their ability to fulfill their life and farming needs. Also, originality exists in the use of the time-varying copulas in identifying the dependence of the indices involved in calculating FER.

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Modeling indemnity of revenue-based crop insurance in Indonesia using time-varying copula models

10.1063/1.5139121 ◽

2019 ◽

Author(s):

Atina Ahdika ◽

Dedi Rosadi ◽

Adhitya Ronnie Effendie ◽

Gunardi

Keyword(s):

Crop Insurance ◽

Time Varying ◽

Copula Models

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On the Relationship of Cryptocurrency Price with US Stock and Gold Price Using Copula Models

Mathematics ◽

10.3390/math8111859 ◽

2020 ◽

Vol 8 (11) ◽

pp. 1859

Author(s):

Jong-Min Kim ◽

Seong-Tae Kim ◽

Sangjin Kim

Keyword(s):

Gold Price ◽

Gaussian Copula ◽

Time Varying ◽

Conditional Correlation ◽

Copula Models ◽

Marginal Regression ◽

High Volatility ◽

Asymmetric Garch ◽

Relationship Of ◽

The Relationship

This paper examines the relationship of the leading financial assets, Bitcoin, Gold, and S&P 500 with GARCH-Dynamic Conditional Correlation (DCC), Nonlinear Asymmetric GARCH DCC (NA-DCC), Gaussian copula-based GARCH-DCC (GC-DCC), and Gaussian copula-based Nonlinear Asymmetric-DCC (GCNA-DCC). Under the high volatility financial situation such as the COVID-19 pandemic occurrence, there exist a computation difficulty to use the traditional DCC method to the selected cryptocurrencies. To solve this limitation, GC-DCC and GCNA-DCC are applied to investigate the time-varying relationship among Bitcoin, Gold, and S&P 500. In terms of log-likelihood, we show that GC-DCC and GCNA-DCC are better models than DCC and NA-DCC to show relationship of Bitcoin with Gold and S&P 500. We also consider the relationships among time-varying conditional correlation with Bitcoin volatility, and S&P 500 volatility by a Gaussian Copula Marginal Regression (GCMR) model. The empirical findings show that S&P 500 and Gold price are statistically significant to Bitcoin in terms of log-return and volatility.

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