scholarly journals VALUE AT RISK PADA PORTOFOLIO SAHAM DENGAN COPULA ALI-MIKHAIL-HAQ

2019 ◽  
Vol 8 (4) ◽  
pp. 543-556
Author(s):  
Delsy Nurutsaniyah ◽  
Tatik Widiharih ◽  
Di Asih I Maruddani
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Confidence Level ◽  
Value At Risk ◽  
Joint Distribution ◽  
Conditional Heteroskedasticity ◽  
Model Data ◽  
Time Period ◽  
High Volatility ◽  
Measuring Tool

Investment is one alternative to increase assets in the future. Investors can invest in a portfolio to reduce the level of risk. Value at Risk (VaR) is a measuring tool that can calculate the worst loss over a given time period at a given confidence level. GARCH (Generalized Autoregressive Conditional Heteroskedasticity) is used to model data with high volatility. The teory of copula is a powerful tool for modeling joint distribution for any marginal distributions. Ali-Mikhail-Haq copula from Archimedean copula family can be applied to data with dependencies τ between -0.1817 to 0.3333. This research uses Ali-Mikhail-Haq copula with a Monte Carlo simulation to calculate a bivariate portfolio VaR from a combination stocks of PT Pembangunan Perumahan Tbk. (PTPP), PT Bank Tabungan Negara Tbk. (BBTN), and PT Jasa Marga Tbk. (JSMR) in the period of March 3, 2014 - March 1, 2019. The results of VaR calculation on bivariate portfolio for next 1 day period obtained the lowest VaR is owned by bivariate portfolio between PTPP and JSMR with a weight of 30% and 70% at confidence level of 99%, 95%, and 90% respectively are 4.014%, 2.545%, and 1.876%.Keywords: Value at Risk, GARCH, Ali-Mikhail-Haq Copula, Monte Carlo

scholarly journals COPULA FRANK UNTUK PERHITUNGAN VALUE AT RISK PORTOFOLIO BIVARIAT PADA MODEL EXPONENTIAL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY

2022 ◽  
Vol 10 (4) ◽  
pp. 562-572
Author(s):  
Eka Anisha ◽  
Di Asih I Maruddani ◽  
Suparti Suparti
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Price ◽  
Var Model ◽  
Asymmetric Effect ◽  
Time Period ◽  
High Volatility ◽  
Measuring Tool ◽  
Stock Portfolio ◽  
Autoregressive Conditional Heteroscedasticity

Stocks are one type of investment that promises return for investors but often carries a high risk. Value at Risk (VaR) is a measuring tool that can calculate the amount of the worst loss that occurs in a stock portfolio with a certain level of confidence and within a certain time period. In general, financial data have a high volatility value, which causes the residuals are not normally distributed. ARCH/GARCH modoel is used to solve the heteroscedasticity problem. If the data also have an asymmetric effect, it is modelled with Exponential GARCH model. Copula-Frank is part of the Archimedian copula which is used to solve empirical cases. The data on this study were BBCA and KLBF stock price return data in the observation period 30 December 2011 – 6 December 2019. Furthermore, to test the validity of the VaR model, a backtesting test will be carried out using the Kupiec Test. The results showed that the best model used for BBCA stocks was ARIMA (1,0,1) EGARCH (1,1) and for KLBF stocks was ARIMA (1,0,1) EGARCH (1,2). The amount of risk with a 95% confidence level used a combination of the EGARCH and Copula-Frank models was 2.233% of today's investment. Based on the backtesting test used the Kupiec Test, the VaR model of the portfolio obtained was declared valid.

scholarly journals COPULA FRANK PADA VALUE at RISK (VaR) PEMBENTUKAN PORTOFOLIO BIVARIAT (Studi Kasus : Saham-Saham Perusahaan yang Meraih Predikat The IDX Top Ten Blue Tahun 2017 dengan Periode Saham 20 Oktober 2014 – 28 Februari 2018)

2018 ◽  
Vol 7 (3) ◽  
pp. 293-302
Author(s):  
Juria Ayu Handini ◽  
Di Asih I Maruddani ◽  
Diah Safitri
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Stock Returns ◽  
Confidence Level ◽  
Value At Risk ◽  
Residual Variance ◽  
Copula Theory ◽  
Joint Distributions ◽  
Copula Method ◽  
Frank Copula

The capital market has an important role in society to invest in financial instruments. Investors can invest in the form of a portfolio that is by combining several shares to reduce the risk that will occur. Value at Risk (VaR) is a method for estimating the worst risk of an investment. GARCH (Generalized Autoregressive Conditional Heteroscedasticity) is used to model high-volatile stock data that causes residual variance is not constant. Copula theory is a powerful tool for modeling joint distributions because it does not require normality assumptions that are difficult to fulfill in financial data. Copula Frank has a feature that can identify positive and negative dependencies. This study aims to measure the value of VaR using the Frank-GARCH copula method using stock returns data of PT Bank Rakyat Indonesia, Tbk (BBRI), PT Telekomunikasi Indonesia, Tbk (TLKM), and PT. Unilever Indonesia, Tbk (UNVR) for the period 20 October 2014 - 28 February. Bivariate portfolio pairs obtained namely TLKM and UNVR shares because they have the highest Rho Spearman residual correlation value of ρ = 0.3204. Based on the generation of data using Monte Carlo simulations, the results of the calculation of Value at Risk (VaR) of 1.40% at the 90% confidence level, 1.89% at the 95% confidence level, and 2.79% at the 99% confidence level. Keywords: Value at Risk, Frank copula, GARCH, Monte Carlo

Price Limits, the VaR calculation, and a Monte Carlo Simulation

2002 ◽  
Vol 10 (1) ◽  
pp. 81-111
Author(s):  
Jin Yoo
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Confidence Level ◽  
Value At Risk ◽  
Stock Price ◽  
Price Limits ◽  
Holding Period ◽  
A Value ◽  
Practical Implications

This paper raises an issue of calculating a value at risk (VaR) of a stock price in the presence of daily price limits, suggests an appropriate methodology for it, and discusses its practical implications. One finding is that the VaR with price limits is never bigger than without. It turns out that the discrepancy between the two VaRs increases as the confidence level rises, the holding period lengthens, the volatility goes up, or the price limits get tighter.

scholarly journals PENERAPAN METODEEXPECTED SHORTFALLPADA PENGUKURAN RISIKO INVESTASI SAHAM DENGAN VOLATILITAS MODEL GARCH

2019 ◽  
Vol 8 (1) ◽  
pp. 184-193
Author(s):  
Nurul Fitria Fitria Rizani ◽  
Mustafid Mustafid ◽  
Suparti Suparti
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Price ◽  
Garch Model ◽  
Expected Shortfall ◽  
Conditional Heteroskedasticity ◽  
Investment Risk ◽  
Stock Investment ◽  
Autoregressive Conditional Heteroskedasticity ◽  
High Volatility

One of the methods that can be used to measure stock investment risk is Expected Shortfall (ES). ES is an expectation of risk size which value is greater than Value at Risk (VaR), ES has characteristics of sub-additive and convex. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to model stock data that has high volatility. Calculating ES is done with data that shows deviations from normality using Cornish-Fisher's expansion. This researchapplies the ES at the closing stock price of PT Astra International Tbk. (ASII), PT Bank Negara Indonesia (Persero) Tbk. (BBNI), and PT Indocement Tunggal Prakarsa Tbk. (INTP) for the period of 11 February 2013 - 31 March 2019. Based on the volatility of GARCH (1,1) analysis, we find ES calculation for each stock by 95% level  confidence. The ES for ASII shares is 4.1%, greater than the VaR value which isonly 2.64%.The ES for BBNI shares is 4.38%, greater than it’s VaR value which is only 2,86%. The ES for INTP shares is 6.22%, which is also greater than it’s VaR value which is only3,99%. The greather of VaR then Thegreather of ES obtained.Keywords: Expected Shortfall, Value at Risk, GARCH

scholarly journals VALUE at RISK (VaR) DAN CONDITIONAL VALUE at RISK (CVaR) DALAM PEMBENTUKAN PORTOFOLIO BIVARIAT MENGGUNAKAN COPULA GUMBEL

2020 ◽  
Vol 9 (3) ◽  
pp. 326-335
Author(s):  
Dina Rahma Prihatiningsih ◽  
Di Asih I Maruddani ◽  
Rita Rahmawati
Keyword(s):  
At Risk ◽  
High Risk ◽  
Confidence Level ◽  
Value At Risk ◽  
Joint Distribution ◽  
Conditional Value At Risk ◽  
Arima Models ◽  
R Software ◽  
Selection Of

One way to minimize risk in investing is to form of portfolio by combining several stocks.Value at Risk (VaR) is a method for estimating risk but has a weakness that is VaR is incoherent because it does not have the subadditivity. To overcome the weakness of VaR, Conditional Value at Risk (CVaR) can use. Stock data is generally volatile, so ARIMA-GARCH is used to model it. The selection of ARIMA models on R software can be automatically using the auto.arima() function. Then Copula Gumbel is a method for modeling joint distribution and flexible because it does not require the assumption of normality and has the best sensitivity to high risk so that it is suitable for use in stock data.The first step in this research is to modeling Copula Gumbel-GARCH with the aim to calculate VaR and CVaR on the portfolio of PT Bank Mandiri Tbk (BMRI) and PT Indo Tambangraya Megah Tbk (ITMG). At the confidence level 99%, 95%, and 90% obtained the VaR results sequentially amounted to 3.977073%; 2.546167%; and 1.837288% and the CVaR results sequentially amounted to 4.761437%; 3.457014%; and 2.779182%. The worst condition is a loss with VaR and it is still possible if a worse condition occurs is a loss with CVaR so that investors can be more aware of the biggest loss that will be suffered.Keywords: Value at Risk, Conditional Value at Risk, Auto ARIMA, Copula Gumbel.

scholarly journals PENDEKATAN METODE MARKOWITZ UNTUK OPTIMALISASI PORTOFOLIO DENGAN RISIKO EXPECTED SHORTFALL (ES) PADA SAHAM SYARIAH DILENGKAPI GUI MATLAB

2022 ◽  
Vol 10 (4) ◽  
pp. 508-517
Author(s):  
Umiyatun Muthohiroh ◽  
Rita Rahmawati ◽  
Dwi Ispriyanti
Keyword(s):  
At Risk ◽  
Risk Analysis ◽  
Confidence Level ◽  
Value At Risk ◽  
Expected Shortfall ◽  
Optimal Portfolio ◽  
Optimal Portfolios

A portfolio is a combination of two or more securities as investment targets for a certain period of time with certain conditions. The Markowitz method is a method that emphasizes efforts to maximize return expectations and can minimize stock risk. One method that can be used to measure risk is Expected Shortfall (ES). ES is an expected measure of risk whose value is above Value-at-Risk (VaR). To make it easier to calculate optimal portfolios with the Markowitz method and risk analysis with ES, an application was made using the Matlab GUI. The data used in this study consisted of three JII stocks including CPIN, CTRA, and BSDE stocks. The results of the portfolio formation with the Markowitz method obtained an optimal portfolio, namely the combination of CPIN = 34.7% and BSDE = 65.3% stocks. At the 95% confidence level, the ES value of 0.206727 is greater than the VaR value (0.15512).  

scholarly journals On the existence of an optimal estimation window for risk measures

2015 ◽  
Vol 4 (1and2) ◽  
pp. 28
Author(s):  
Marcelo Brutti Righi ◽  
Paulo Sergio Ceretta
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measures ◽  
Optimal Estimation ◽  
Expected Shortfall ◽  
Estimation Bias ◽  
Optimal Length

We investigate whether there can exist an optimal estimation window for financial risk measures. Accordingly, we propose a procedure that achieves optimal estimation window by minimizing estimation bias. Using results from a Monte Carlo simulation for Value at Risk and Expected Shortfall in distinct scenarios, we conclude that the optimal length for the estimation window is not random but has very clear patterns. Our findings can contribute to the literature, as studies have typically neglected the estimation window choice or relied on arbitrary choices.

scholarly journals ESTIMASI NILAI CONDITIONAL VALUE AT RISK MENGGUNAKAN FUNGSI GAUSSIAN COPULA

2015 ◽  
Vol 4 (4) ◽  
pp. 188
Author(s):  
HERLINA HIDAYATI ◽  
KOMANG DHARMAWAN ◽  
I WAYAN SUMARJAYA
Keyword(s):  
At Risk ◽  
Confidence Level ◽  
Value At Risk ◽  
Risk Measure ◽  
Copula Function ◽  
Gaussian Copula ◽  
Nonlinear Dependence ◽  
Conditional Value At Risk ◽  
Dependence Structure ◽  
Measurement Tools

Copula is already widely used in financial assets, especially in risk management. It is due to the ability of copula, to capture the nonlinear dependence structure on multivariate assets. In addition, using copula function doesn’t require the assumption of normal distribution. There fore it is suitable to be applied to financial data. To manage a risk the necessary measurement tools can help mitigate the risks. One measure that can be used to measure risk is Value at Risk (VaR). Although VaR is very popular, it has several weaknesses. To overcome the weakness in VaR, an alternative risk measure called CVaR can be used. The porpose of this study is to estimate CVaR using Gaussian copula. The data we used are the closing price of Facebook and Twitter stocks. The results from the calculation using 90%  confidence level showed that the risk that may be experienced is at 4,7%, for 95% confidence level it is at 6,1%, and for 99% confidence level it is at 10,6%.

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