Portfolio Value-at-Risk estimating on markov regime switching copula-autoregressive conditional jump intensity-threshold generalized autoregressive conditional heteroscedasticity model

2012 ◽  
Author(s):  
Chi Xie ◽  
Lin Yao
Keyword(s):  
At Risk ◽  
Regime Switching ◽  
Value At Risk ◽  
Conditional Heteroscedasticity ◽  
Markov Regime Switching ◽  
Intensity Threshold ◽  
Jump Intensity ◽  
Autoregressive Conditional Heteroscedasticity

scholarly journals ESTIMASI VALUE AT RISK PORTOFOLIO SAHAM MENGGUNAKAN METODE GARCH-COPULA (Studi Kasus : Harga Penutupan Saham Harian Unilever Indonesia dan Kimia Farma Periode 1 Januari 2013- 31 Desember 2016)

2018 ◽  
Vol 7 (4) ◽  
pp. 397-407
Author(s):  
Lingga Bayu Prasetya ◽  
Dwi Ispriyanti ◽  
Alan Prahutama
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Tail Dependence ◽  
Conditional Heteroscedasticity ◽  
Mutual Correlation ◽  
Copula Method ◽  
Stock Portfolio ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Return Value ◽  
Return Data

Any investment in the stock market will earn returns accompanied by risks. Return and risk has a mutual correlation that equilibrium. The formation of a portfolio is intended to provide a lower risk or with the same risk but provide a higher return. Value at Risk (VaR) is a instrument to analyze risk management. Time series model used in stock return data that it has not normal distribution and heteroscedastisicity is Generalized Autoregressive Conditional Heteroscedasticity (GARCH). GARCH-Copula is a combined method of GARCH and Copula. The Copula method is used in joint distribution modeling because it does not require the assumption of normality of the data and can capture tail dependence between each variable. This research uses return data from stock closing prices of Unilever Indonesia and Kimia Farma period January 1, 2013 until December 31, 2016. Copula model is selected based on the highest likelihood log value is Copula Clayton. Value at Risk estimates of Unilever Indonesia and Kimia Farma's stock portfolio on the same weight were performed using Monte Carlo simulation with backtesting of 30 days period data at 95% confidence level. Keywords : Stock, Risk, Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Copula, Value at Risk

scholarly journals PERBANDINGAN RESIKO INVESTASI BANK CENTRAL ASIA DAN BANK MANDIRI MENGGUNAKAN MODEL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (GARCH)

2016 ◽  
Vol 5 (4) ◽  
pp. 80
Author(s):  
Nurul Saadah ◽  
Maiyastri . ◽  
Hazmira Yozza
Keyword(s):  
At Risk ◽  
Central Asia ◽  
Value At Risk ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Conditional Heteroscedasticity

Abstrak. Data return saham adalah salah satu data deret waktu. Jika ingin melakukanpemodelan return, maka dapat dilakukan pemodelan deret waktu. Model rataan returnmenggunakan model Autoregressive Moving Average (ARMA). Sedangkan untuk memodelkanragam digunakan model Generalized Autoregressive Conditional Heteroscedasticity(GARCH). Setelah melakukan beberapa tahapan diperoleh model ARMA(1,0) danGARCH(1,1) sebagai model terbaik untuk data return saham Bank Central Asia. Sedangkanmodel terbaik untuk data return saham Bank Mandiri adalah model ARMA(0,1)dan GARCH(1,1). Model yang diperoleh digunakan untuk melakukan peramalan returndan volatilitas dalam pengukuran resiko. Salah satu alat ukur yang digunakan untukmengukur resiko adalah Value at Risk. Dari perhitungan resiko untuk kedua bank diperolehbahwa resiko maksimum Bank Mandiri lebih besar dari resiko maksimum BankCentral Asia.

scholarly journals Analisis Risiko Investasi Saham Syariah Dengan Model Value AT Risk-Asymmetric Power Autoregressive Conditional Heterocedasticity (VaR-APARCH)

2017 ◽  
Vol 6 (1) ◽  
pp. 37
Author(s):  
Syarif Hidayatullah ◽  
Mohammad Farhan Qudratullah
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Conditional Heteroscedasticity ◽  
Asymmetric Power ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Schwarz Criterion

Penelitian ini membahas analisis risiko data runtun waktu dengan model Value at Risk- Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCH)dalam pasar modal syariah. Metode yang digunakan dalam penelitian ini adalah penerapan kasus.Data yang digunakan adalah harga penutupan harian saham dalam Jakarta Islamic Index (JII)periode 4 Maret 2013 sampai 8 April 2015.Model APARCH yang dipilih berdasarkan nilai Schwarz Criterion (SC).Langkah-langkah dalam penelitian ini adalah menguji kestasioneran data, mengidentifikasi model ARIMA,mengestimasi parameter model ARIMA, menguji diagnostik model ARIMA, mendeteksi ada tidaknya unsur ARCH atau unsur heteroskedastisitas, uji asimetris data saham, mengestimasi model APARCH, menguji diagnostik model APARCH, dan menghitung risiko dengan VaR-APARCH.Model terbaik yang dipilih adalah ARIMA ((3),0,0) dan APARCH (1,1). Model ini valid untuk menganalisis besar risiko investasi dalam jangka waktu 10 hari ke depan.

scholarly journals Measuring parametric and semiparametric downside risks of selected agricultural commodities

2021 ◽  
Vol 67 (No. 8) ◽  
pp. 305-315
Author(s):  
Dejan Živkov ◽  
Marijana Joksimović ◽  
Suzana Balaban
Keyword(s):  
At Risk ◽  
Soybean Oil ◽  
Soybean Meal ◽  
Value At Risk ◽  
Downside Risk ◽  
Conditional Value At Risk ◽  
Agricultural Commodities ◽  
Second Best ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Very High

In this paper, we evaluate the downside risk of six major agricultural commodities – corn, wheat, soybeans, soybean meal, soybean oil and oats. For research purposes, we first use an optimal generalised autoregressive conditional heteroscedasticity (GARCH) model to create residuals, which we later use for measuring downside risks via parametric and semiparametric approaches. Modified value-at-risk (mVaR) and modified conditional value-at-risk (mCVaR) provide more accurate downside risk results than do ordinary value-at-risk (VaR) and conditional value-at-risk (CVaR). We report that soybean oil has the lowest mVaR and mCVaR because it has two very favourable features – skewness around zero and low kurtosis. The second-best commodity is soybeans. The worst-performing downside risk results are in wheat and oats, primarily because of their very high kurtosis values. On the basis of the results, we propose to investors and various agents involved with these agricultural assets that they reduce the risk of loss by combining these assets with other financial or commodity assets that have low risk.

scholarly journals Pemodelan Deteksi Krisis Keuangan di Indonesia Berdasarkan Indikator M2/Cadangan Devisa

2019 ◽  
Vol 16 (2) ◽  
pp. 98-103
Author(s):  
Aisyah Zahrotul Hidayah ◽  
Sugiyanto Sugiyanto ◽  
Isnandar Slamet
Keyword(s):  
Financial Crisis ◽  
Regime Switching ◽  
Structural Changes ◽  
Detection System ◽  
International Reserves ◽  
Markov Regime Switching ◽  
Volatility Models ◽  
Macroeconomic Indicators ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Very High

The banking crisis reflects the liquidity crisis and bankruptcy of banks in the financial system. The financial crisis that occurred in mid-1997 resulted in a financial crisis that had a severe impact on the Indonesian economy. This made it aware of the importance of building a financial crisis early detection system to prepare for a crisis. The crisis occurs due to several macroeconomic indicators undergoing structural changes (regimes) and contain very high fluctuations. Combined volatility models and Markov regime switching are very suitable for explaining crises. The M2/international reserves indicator from 1990 to 2018 was used to build a crisis model. The results showed that the Markov regime switching autoregressive conditional heteroscedasticity model MRS-ARCH(2,1) could explain the crisis that occurred in mid-1997. Based on this model, in the future the crisis might occur if the M2/international reserves indicator decreased minimum of 13%

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