Diversification of Agricultural Areas in Indonesia using Dynamic Copula Modeling and K-Means Clustering

Agriculture is one of the main pillars of economic growth in Indonesia. Failure in this sector can result in faltering economic stability of the country. Thus, to minimize these failures, mapping of areas with particular commodity potential is needed. One of the main factors affecting the growth of crops is rainfall. Therefore, this paper aims to model the potential distribution of commodity growth based on rainfall precipitation using dynamic copula. The modeling results are then used as a basis for grouping the potential of food crop commodities in Indonesia. The determination of the group was carried out using the k-means clustering method. We expect that the result of the modeling can provide an overview for farmers or the government to make policies related to the optimization of Indonesia's agricultural sector. This result will enable the government to offer facilities that can minimize agricultural losses, such as superior seeds that are resistant to weather changes and the provision of training for enhancing farming skills. In addition, it is also suggested to diversify farm areas to reduce the failures due to dependence on a single agricultural product.

Co-Movement between Tourist Arrivals of Inbound Tourism Markets in South Korea: Applying the Dynamic Copula Method Using Secondary Time Series Data

Tourism demand is severely affected by unpredicted events, which has prompted scholars to examine ways of predicting the effects of positive and negative shocks on tourism, to ensure a sustainable tourism industry. The purpose of this study was to investigate if non-linear dependence structures exist between tourist flows into South Korea from five major source countries, as South Korea has undergone fluctuations in tourist arrivals due to diverse circumstances and has complex relations with tourism source countries. Additionally, the study examines the structures of extreme tail dependence, which is indicated in the case of unexpected events, and identifies how co-movements vary over time through dynamic copula–GARCH (generalized autoregressive conditional heteroskedasticity) tests. The secondary time series data for the 2005–2019 period of tourist arrivals to Korea were derived from the Korea Tourism Knowledge and Information System for testing the copula models. The copula estimations indicate significant dependencies among all market pairs as well as the strongest dependence between China and Taiwan. Moreover, extreme tail dependence structures show co-movements for four pairs of tourism markets in only negative shocks, for five pairs in both positive and negative conditions, but no co-movement in the China–Taiwan pair. Finally, the dynamic dependence structures reveal that the China–Taiwan dependence is higher than the other time-varying dependence structures, implying that the two markets complement each other.

Assessing the Current Situation of the World Wheat Market Leadership: Using the Semi-Parametric Approach

This paper examines the world wheat market leadership using price discovery occurring in wheat futures markets of the United States (U.S.) and Europe. An error correction model (ECM) generalized autoregressive conditional heteroskedasticity (GARCH), and semi-parametric dynamic copula methods are used for this purpose. The results indicate a positive link between U.S. and Europe price discovery which is stronger, fluctuating less after August 2010 because of a drought occurring in the Black Sea region, and then lessens, fluctuating more after 2015 with the changing wheat trade map. Furthermore, after 2015, wheat market leadership moved from the U.S. to the European market, meaning price discovery is primarily located by the Marché à Terme International de France (MATIF) futures market.