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 ESTIMASI TINGKAT RISIKO INVESTASI EMAS MENGGUNAKAN PENDEKATAN GENERALIZED EXTREME VALUE DAN GENERALIZED PARETO DISTRIBUTION

2019 ◽  
Vol 2 (1) ◽  
pp. 35
Author(s):  
Noviana Pratiwi ◽  
Catur Iswahyudi
Keyword(s):  
Confidence Level ◽  
Value At Risk ◽  
Generalized Pareto Distribution ◽  
Secondary Data ◽  
Capital Allocation ◽  
Heavy Tail ◽  
Extreme Value ◽  
Generalized Extreme Value Distribution ◽  
Generalized Extreme Value ◽  
Level Of Risk

This study estimates the level of risk in investing in gold. Value at Risk (VaR) is a method which can be used for calculating the level of risk. There are two distribution approaches used, namely Generalized Extreme Value Distribution (GEV) and Generalized Distribution Pareto (GDP). These two distributions are used because gold data is alleged to have a heavy tail distribution. The study uses secondary data on gold prices with January 2015 to December 2017 period with a total of 876 data. The results obtained indicate that the data return for the gold price has a heavy tail. Estimation results obtained indicate that the VaR value at the 95% confidence level is less than VaR with a 99% confidence level so it can be concluded that the higher the level of risk to be taken, the greater the level of confidence and capital allocation to cover losses taken by investors. The GDP Estimation value gives a greater value than GEV. and the largest VaR value is shown at 4.049%, which means that the maximum loss that may occur in one period ahead is 4.049%.

scholarly journals STL Decomposition and SARIMA Model: The Case for Estimating Value-at-Risk of Covid-19 Increment Rate in DKI Jakarta

2021 ◽  
Vol 7 (2) ◽  
pp. 1-10
Author(s):  
Agnes Zahrani ◽  
Aniq A. Rohmawati ◽  
Siti Sa’adah
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Extreme Values ◽  
Moving Average ◽  
Extreme Value ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Increment Rate ◽  
The Difference ◽  
Parameter Estimators

In this research, we propose an extreme values measure, the Value-at-Risk (VaR) based Seasonal Trend Loess (STL) Decomposition and Seasonal Autoregressive Integrated Moving Average (SARIMA) models, which is more sensitive to the seasonality of extreme value than the conventional VaR. We consider the problem of the seasonality and extreme value for increment rate of Covid-19 forecasting. For stakeholder, government and regulator, VaR estimation can be implemented to face the extreme wave of new positive Covid-19 in the future and minimize the losses that possibly affected in term of financial and human resources. Specifically, the estimation of VaR is developed with the difference lies on parameter estimators of STL and SARIMA model. The VaR has coverage probability as well as close 1-α. Thus, we propose to set α as parameter to estimate VaR. Consequently, the performance of VaR will depend not only on parameter model but also α. Our aim estimates VaR with minimum α based on correct VaR value. Numerical analysis is carried out to illustrate the estimative VaR.

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