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]).
PT. XYZ adalah sebuah korporasi penyedia layanan Internet Service Provider atau yang dikenal dengan istilah ISP dan jasa kebutuhan layanan TI lainnya yang sedang mengembangankan bisnis nya di Kota Balikpapan, Kalimantan Timur. Adapun proses bisnis utama dari PT. XYZ adalah membantu client dalam merancang bangun serta memberikan solusi terbaik bagi kebutuhan IT, komunikasi dan jaringan dalam mendukung proses bisnis. Belum diterapkannya pengelolaan risiko pada PT. XYZ dianggap dapat menurunkan performa korporasi apabila tidak cepat diselesaikan. Merujuk pada studi permasalahan sebelumnya, studi ini akan mengkaji terkait manajemen risiko IT dengan framework COBIT 5 for risk dan FMEA berasas ISO 31000. Dimana COBIT 5 for risk memiliki kelebihan dalam mengidentifikasi risiko, proses pengkajian risiko melalui FMEA serta berasas pada ISO 31000 yang diterapkan oleh sebagian besar korporasi. Studi ini menghasilkan sebuah padanan atau ilustrasi terkait alur pengelolaan risiko dimana terdiri dari penggabungan beberapa framework pengelolaan risiko. Selanjutnya studi ini dapat berkontribusi sebagai acuan korporasi dalam pengelolaan risiko.
Procurement process holds crucial role in companies' business process, particularly manufacturing company. PT Semen Indonesia as one of manufacturing Companies in Indonesia relies on procurement process in order to fulfill their business. However, existing e-procurement does not cover all the procurement process which creates process variants have made whole procurement process run into bottleneck. Deeper analysis required to dig insights on the process variants to derive the cause of problem. Hence, this research aims to explore Process Mining to gain more insights from the process using data log derived from the system. BPM lifecycle used as an approach to reach the objective of the research. However, not all phases in BPM lifecycle are imposed, yet Process Identification, Process Discovery and Process Analysis. The result showed that top three activities that take time most are Upload to E Procurement, Purchasing Configuration, and Technical Evaluation which require improvements.
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