Value at Risk (VaR) and Expected Tail Loss (ETL) are two risk measures that are used frequently to measure the investment risk. Even though VaR can estimate maximum loss when the investor holds a single asset in a particular period and interval confidence, the investor frequently develops a portfolio of assets. This condition can create shared risk among assets in the portfolio so that there will be a chance of an asset for getting loss caused by the other assets developing the portfolio. On the other hand, there is a fact that VaR cannot provide loss information at the tail loss part so that we also need ETL that can overcome this problem. Because of that reason, this paper uses Credible Value at Risk (CredVaR) and Credible Expected Tail Loss (CredETL), which are formulated based on the Buhlman credibility concept. Both methods can estimate an investment risk that can overcome the shortcoming of VaR and ETL that do not consider the risk among assets inside the portfolio. The application of both methods was utilized to evaluate the individual risk of each asset in a portfolio comprised of five stocks in the LQ-45 Index (period of February 2019 until July 2019). The data divided into ten periods of risk analysis comprises of ten-year daily data of each stock from June 2009 to May 2019. According to the result of the analysis, it can be concluded that both methods are powerful in measuring the risk.
ABSTRACT. This paper elaborates a research of the cancer patients after receiving a treatment in cencored data using Bayesian estimation under Linex Loss function for Survival Model which is assumed as an exponential distribution. By giving Gamma distribution as prior and likelihood function produces a gamma distribution as posterior distribution. The posterior distribution is used to find estimatior ̂ by using Linex approximation. After getting ̂, the estimators of hazard function ĥ and survival function ̂ can be found. Finally, we compare the result of Maximum Likelihood Estimation (MLE) and Linex approximation to find the best method for this observation by finding smaller MSE. The result shows that MSE of hazard and survival under MLE are 2.91728E-07 and 0.000309004 and by using Bayesian Linex worths 2.8727E-07 and 0.000304131, respectively. It concludes that the Bayesian Linex is better than MLE.
This paper analyses a Skewed t Distribution approach to estimate Value at Risk (VaR) as a tool that can measure a risk investment. The method can estimate an investment risk that can overcome the shortcoming of classical VaR, which cannot capture the existence of fat tail and skewness. The application of the method was utilized to evaluate the individual risk of four stocks taken from the NYSE Index, namely Advance Micro Devices Inc (AMD), The Coca-Cola Company (KO), Pfizer Inc. (PFE), and Walmart Inc (WMT). It can be summarized from the result of the analysis that VaR (in several confidence levels) based on the distribution approach is powerful in risk measurement and can give an alternative to the investor for estimating the risk.
Pengendalian kualitas dengan diagram kontrol Multivariate Exponentially Weighted Moving Average (MEWMA), merupakan usaha untuk meminimalkan produk cacat atau rusak dari produk yang diproduksi oleh perusahaan. Hal tersebut dilakukan untuk mempertahankan kualitas produk yang dihasilkan agar sesuai dengan kriteria produk yang telah ditetapkan dan dapat diterima oleh konsumen. Dalam produksi pengolahan air bersih pada PDAM Tirta Khatulistiwa dilakukan pengontrolan kualitas dengan lima karakteristik kualitas yaitu warna, kekeruhan, suhu, Daya Hantar Listrik (DHL) dan pH dengan menggunakan Diagram Kontrol MEWMA. Penerapan diagram kontrol MEWMA pada penelitian ini menggunakan senilai 0,05 dengan Batas Pengendali Atas (BPA) yaitu senilai . Proses produksi pengolahan air bersih berada di bawah batas BPA hingga pengontrolan keempat, sehingga dapat dikatakan bahwa proses telah terkontrol atau dengan kata lain proses telah stabil. Kata Kunci: Diagram Kontrol, Multivariate Expoentially Weighted Moving Average (MEWMA), BPA
Model koreksi kesalahan (ECM) berfungsi untuk membentuk hubungan jangka panjang, mengoreksi ketidakseimbangan jangka pendek, mengatasi masalah data runtun waktu yang tidak stasioner dan mengatasi masalah regresi lancung (spurious regression). Diperlukan lima langkah dalam model koreksi kesalahan yaitu melakukan uji akar unit (ADF), melakukan uji kointegrasi Engle-Granger, estimasi model koreksi kesalahan Engle Granger dan Domowitz-Elbadawi, melakukan signifikan parameter, dan pemilihan model yang terbaik dengan membandingkan kriteria nilai AIC. Penelitian ini bertujuan untuk menerapkan model koreksi kesalahan pada kasus data runtun waktu indeks harga konsumen (IHK) di Jawa Tengah dan membandingkan model koreksi kesalahan Engle Granger dan Domowitz-Elbadawi dengan menggunakan kriteria pembanding nilai AIC. Data yang digunakan dalam penelitian ini merupakan data sekunder yang diperoleh dari BPS berupa data bulanan IHK berdasarkan empat kelompok pengeluaran dari tahun 2014 sampai dengan tahun 2017. Hasil penelitian menunjukkan bahwa penerapan model koreksi kesalahan yang digunakan adalah valid (sesuai) dan perbandingan menggunakan nilai AIC dari kedua model koreksi kesalahan diperoleh model koreksi kesalahan Engle Granger mempunyai kemampuan yang baik. Perolehan nilai AIC pada model koreksi kesalahan untuk masing-masing data IHK berdasarkan empat kelompok pengeluaran sebesar 0,4399 dan 1,1601 yang menunjukkan model koreksi kesalahan Engle Granger merupakan model yang lebih baik digunakan dari model koreksi kesalahan Domowitz-Elbadawi.Kata Kunci : Uji Akar Unit, Kointegrasi, Model Koreksi Kesalahan (ECM)
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