Climate change has been projected to increase the intensity and magnitude of extreme temperature in Indonesia. Solar radiation management (SRM) has been proposed as a strategy to temporarily combat global warming, buying time for negative emissions. Although the global impacts of SRM have been extensively studied in recent years, regional impacts, especially in the tropics, have received much less attention. This article investigates the potential stratospheric sulphate aerosol injection (SAI) to modify mean and extreme temperature, as well as the relative humidity and wet bulb temperature (WBT) change over Indonesian Maritime Continent (IMC) based on simulations from three different earth system models.We applied a simple downscaling method and corrected the bias of model output to reproduce historical temperatures and relative humidity over IMC. We evaluated changes in geoengineering model intercomparison project (GeoMIP) experiment G4, an SAI experiment in 5 Tg of SO 2 into the equatorial lower stratosphere between 2020 and 2069, concurrent with the RCP4.5 emissions scenario. G4 is able to significantly reduce the temperature means and extremes, and although differences in magnitude of response and spatial pattern occur, there is a generally consistent response. The spatial response of changes forced by RCP4.5 scenario and G4 are notably heterogeneous in the archipelago, highlighting uncertainties that would be critical in assessing socio-economic consequences of both doing, and not doing G4. In general, SAI has bigger impacts in reducing temperatures over land than oceans, and the southern monsoon region shows more variability.

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Background: Stock investment has been gaining momentum in the past years due to the development of technology. During the pandemic lockdown, people have invested more. One the one hand, stock investment has high potential profitability, but on the other, it is equally risky. Therefore, a value at risk (VaR) analysis is needed. One approach to calculate VaR is by using the Bayesian mixture model, which has been proven to be able to overcome heavy-tailed cases. Then, the VaR’s accuracy needs to be tested, and one of the ways is by using backtesting, such as the Kupiec test.Objective: This study aims to determine the VaR model of PT NFC Indonesia Tbk (NFCX) return data using Bayesian mixture modelling and backtesting. On a practical level, this study can provide information about the potential risks of investing that is grounded in empirical evidence.Methods: The data used was NFCX data retrieved from Yahoo Finance, which was then modelled with a mixture model based on the normal and Laplace distributions. After that, the VaR accuracy was calculated and then tested by using backtesting.Results: The test results showed that the VaR with the mixture Laplace autoregressive (MLAR) approach (2;[2],[4]) was accurate at 5% and 1% quantiles while mixture normal autoregressive MNAR (2;[2],[2,4]) was only accurate at 5% quantiles.Conclusion: The better performing NFCX VaR model for this study based on backtesting using Kupiec test is MLAR(2;[2],[4]).

Perkembangan UMKM ini juga harus bisa mengimbangi globalisasi pasar yang menuntut peningkatan daya saing dan strategi bisnis yang saat ini tengah beralih ke era digital. Dari survey yang dilakukan BPS (2016) 67% UMKM mengalami kendala. Kendala yang sering dihadapi salah satunya adalah pemasaran (31%). Berdasarkan hasil yang ada, tidak semua UMKM yang ada melakukan implementasi digital didalam membantu kegiatan marketing. Oleh karena itu penelitian kali ini akan dibahas dinamika perubahan permintaan khususnya UMKM makanan perkembangan era digital dengan menggunakan metode pendekatan sistem dinamik. Melalui penggambaran model simuasi akan digambarkan faktor eksternal pelanggan dan faktor internal penjual. Penggambaran model akan dapat diketahui perubahan permintaan sebelum dilakukan implementasi marketing didalam proses bisnis dan saat dilakukan implementasi.

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