PERHITUNGAN VALUE AT RISK DENGAN PENDEKATAN THRESHOLD AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY-GENERALIZED EXTREME VALUE

2019 ◽  
Vol 12 (1) ◽  
pp. 73
Author(s):  
Mutik Dian Prabaning Tyas ◽  
Di Asih I Maruddani ◽  
Rita Rahmawati
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Extreme Value ◽  
Generalized Extreme Value ◽  
Conditional Heteroscedasticity ◽  
Threshold Autoregressive ◽  
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 PEMBENTUKAN PORTOFOLIO SAHAM DENGAN METODE MARKOWITZ DAN PENGUKURAN VALUE AT RISK BERDASARKAN GENERALIZED EXTREME VALUE (Studi Kasus: Saham Perusahaan The IDX Top Ten Blue 2017)

2018 ◽  
Vol 7 (2) ◽  
pp. 212-223
Author(s):  
Ria Epelina Situmorang ◽  
Di Asih I Maruddani ◽  
Rukun Santoso
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Extreme Values ◽  
Heavy Tail ◽  
Extreme Value ◽  
Return Rate ◽  
Optimal Portfolio ◽  
Average Risk ◽  
Generalized Extreme Value ◽  
Financial Investment

In financial investment, investors will try to minimize risk and increase returns for portfolio formation. One method of forming an optimal portfolio is the Markowitz method. This method can reduce the risk and increase returns. The performance portfolio is measured using the Sharpe index. Value at Risk (VaR) is an estimate of the maximum loss that will be experienced in a certain time period and level of trust. The characteristics of financial data are the extreme values that are alleged to have heavy tail and cause financial risk to be very large. The existence of extreme values can be modeled with Generalized Extreme Value (GEV). This study uses company stock data of The IDX Top Ten Blue 2017 which forms an optimal portfolio consisting of two stocks, namely a combination of TLKM and BMRI stocks for the best weight of 20%: 80% with the expected return rate of 0.00111 and standard deviation of 0.01057. Portfolio performance as measured by the Sharpe index is 1,06190 indicating the return obtained from investing in the portfolio above the average risk-free investment return rate of -0,01010. Risk calculation is obtained based on Generalized Extreme Value (GEV) if you invest both of these stocks with a 95% confidence level is 0,0206 or 2,06% of the current assets. Keywords: Portfolio, Risk, Heavy Tail, Value at Risk (VaR), Markowitz, Sharpe Index, Generalized Extreme Value (GEV).

scholarly journals ANALISIS RISIKO PORTOFOLIO LQ45 MENGGUNAKAN PENDEKATAN VALUE AT RISK BLOCK MAXIMA-GENERALIZED EXTREME VALUE

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Nanda Ayuni, Setyo Wira Rizki, Hendra Perdana
Keyword(s):  
At Risk ◽  
Extreme Value Theory ◽  
Value At Risk ◽  
Heavy Tail ◽  
Value Theory ◽  
Extreme Value ◽  
Generalized Extreme Value ◽  
Block Maxima

Setiap bentuk investasi memiliki risiko yang dapat menyebabkan kerugian bagi investor. Semakin tinggi hasil yang diharapkan dari investasi tersebut, maka semakin tinggi juga tingkat risikonya. Dengan demikian, investor perlu mengetahui besar risiko yang akan dihadapinya, sehingga dapat melakukan tindakan pencegahan agar bisa mengantisipasi risiko tersebut. Metode yang dapat digunakan untuk mengukur risiko adalah value at risk (VaR). Extreme value theory (EVT) merupakan metode yang digunakan untuk mengukur risiko pada data runtun waktu yang memiliki distribusi ekor gemuk. Distribusi ekor gemuk memiliki kecenderungan lebih besar terjadinya kejadian ekstrem dibandingkan dengan distribusi normal. Umumnya, hal ini ditandai oleh nilai kurtosis yang positif. Salah satu metode EVT adalah block maxima yang mengikuti distribusi GEV (generalized extreme value). Perhitungan VaR yang akurat pada data runtun waktu finansial dapat menggunakan VaR dengan metode block maxima-GEV. Penelitian ini menggunakan data harga saham penutupan harian pada indeks LQ45 periode 1 Januari sampai 31 Desember 2018. Saham yang digunakan untuk pembentukan portofolio ada lima yaitu PTBA, ANTM, PGAS, BBCA, dan ICBP, yang mana saham-saham tersebut dipilih berdasarkan nilai mean return tertinggi. Berdasarkan hasil analisis, diperoleh nilai VaR dengan metode block maxima-GEV dengan tingkat kesalahan 5% adalah sebesar 2,555% dari total nilai investasinya. Misalnya, jika investor berinvestasi sebesar Rp100.000.000,00,- maka investor tersebut mempunyai risiko sebesar Rp2.555.000,00. Kata Kunci : investasi, VaR, extreme value theory, heavy tail

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 Estimation of the Value-at-Risk (VaR) Using the TARCH Model by Considering the Effects of Long Memory in Stock Investments

2020 ◽  
Vol 1 (1) ◽  
pp. 34-43
Author(s):  
Nurfadhlina Abdul Halim ◽  
Agus Supriatna ◽  
Adhy Prasetyo
Keyword(s):  
At Risk ◽  
Long Memory ◽  
Value At Risk ◽  
Time Horizon ◽  
Significance Level ◽  
Standard Methods ◽  
Threshold Autoregressive ◽  
Stock Investments ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Model Approach

Value at Risk (VaR) is one of the standard methods that can be used in measuring risk in stock investments. VaR is defined as the maximum possible loss for a particular position or portfolio in the known confidence level of a specific time horizon. The main topic discussed in this thesis is to estimate VaR using the TARCH (Threshold Autoregressive Conditional Heteroscedasticity) model in a time series by considering the effect of long memory. The TARCH model is applied to the daily log return data of a company's stock in Indonesia to estimate the amount of quantile that will be used in calculating VaR. Based on the analysis, it was found that with a significance level of 95% and assuming an investment of 200,000,000 IDR, the VaR using the TARCH model approach was 5,110,200 IDR per day.

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