RISK ASSESSMENT OF STOCKS PORTFOLIO THROUGH ENSEMBLE ARMA-GARCH AND VALUE AT RISK (CASE STUDY: INDF.JK AND ICBP.JK STOCK PRICE)

2021 ◽  
Vol 14 (2) ◽  
pp. 125-136
Author(s):  
Tarno Tarno ◽  
Trimono Trimono ◽  
Di Asih I Maruddani ◽  
Yuciana Wilandari ◽  
Rianti Siwi Utami
Keyword(s):  
Risk Assessment ◽  
At Risk ◽  
Value At Risk ◽  
Stock Price ◽  
Holding Period ◽  
Return Variance ◽  
Stock Portfolio ◽  
Portfolio Return ◽  
Model Approach

Stocks portfolio is a form of investment that can be used to minimize the risk of loss. In a stock portfolio, the Value at Risk (VaR) can be predicted through the portfolio return. If portfolio return variance is heteroskedastic risk prediction can be done by using VaR with ARIMA-GARCH or Ensemble ARIMA-GARCH model approach. Furthermore, the accuracy of VaR is tested through Backtesting test. In this study, the portfolio is formed from PT Indofood CBP Sukses Makmur (ICBP.JK) and PT Indofood Sukses Makmur Tbk (INDF.JK) stocks from 01/01/2018 to 07/30/2021. The results showed that the best model is  Ensemble ARMA-GARCH with MSE 1.3231×10-6. At confidence level of 95% and 1 day holding period, the VaR of the Ensemble ARMA-GARCH was -0.0213. Based on the Backtesting test, it is proven to be very accurate to predict the value of loss risk because the value of the Violation Ratio (VR) is equal to 0.

scholarly journals ARIMA-GARCH Model and ARIMA-GARCH Ensemble for Value-at-Risk Prediction on Stocks Portfolio

2020 ◽  
Author(s):  
Tarno Tarno ◽  
Di Asih I Maruddani ◽  
Rita Rahmawati ◽  
Abdul Hoyyi ◽  
Trimono Trimono ◽  
...  
Keyword(s):  
At Risk ◽  
Risk Prediction ◽  
Confidence Level ◽  
Value At Risk ◽  
Garch Model ◽  
Holding Period ◽  
Return Variance ◽  
Stock Portfolio ◽  
Portfolio Return ◽  
Model Approach

Stocks portfolio is a form of investment that can be used to minimize the risk of loss. In a stock portfolio, the value at risk (VaR) can be predicted through the portfolio return. If portfolio return variance is heteroscedastic risk prediction can be done by using VaR with ARIMA-GARCH or Ensemble ARIMA-GARCH model approach. Furthermore, the accuracy of VaR is tested through backtesting test. In this study, the portfolio formed from Astra Agro Lestari Ltd (AALI) and Indofood Ltd (INDF) stocks from 10/02/2012 to 10/01/2019. The results showed that the best model is ARIMA(0,0,[3])-GARCH(1,2) with AIC of -5.604 and MSE 1.874e-07.At confidence level of 95% and 1 day holding period, the VaR of the ARIMA(0,0,[3])-GARCH(1,2) was -0.3464. Based on the backtesting test, it is proven to be very accurate to predict the value of loss risk because the value of the violation ratio (VR) is equal to 0.

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 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 Mean-value at risk portfolio selection problem using clustering technique : A case study

2019 ◽  
Author(s):  
Sheshma Kiran Kumari ◽  
P. Kumar ◽  
J. Priya ◽  
S. Surya ◽  
A. K. Bhurjee
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Mean Value ◽  
Selection Problem ◽  
Portfolio Selection Problem ◽  
Clustering Technique

scholarly journals Robust Optimization-Based Commodity Portfolio Performance

2020 ◽  
Vol 8 (3) ◽  
pp. 54
Author(s):  
Ramesh Adhikari ◽  
Kyle J. Putnam ◽  
Humnath Panta
Keyword(s):  
At Risk ◽  
Robust Optimization ◽  
Value At Risk ◽  
Optimization Technique ◽  
Practical Importance ◽  
Optimization Techniques ◽  
Commodity Futures ◽  
Conditional Value At Risk ◽  
Holding Period ◽  
Mean Variance

This paper examines the performance of a naïve equally weighted buy-and-hold portfolio and optimization-based commodity futures portfolios for various lookback and holding periods using data from January 1986 to December 2018. The application of Monte Carlo simulation-based mean-variance and conditional value-at-risk optimization techniques are used to construct the robust commodity futures portfolios. This paper documents the benefits of applying a sophisticated, robust optimization technique to construct commodity futures portfolios. We find that a 12-month lookback period contains the most useful information in constructing optimization-based portfolios, and a 1-month holding period yields the highest returns among all the holding periods examined in the paper. We also find that an optimized conditional value-at-risk portfolio using a 12-month lookback period outperforms an optimized mean-variance portfolio using the same lookback period. Our findings highlight the advantages of using robust optimization for portfolio formation in the presence of return uncertainty in the commodity futures markets. The results also highlight the practical importance of choosing the appropriate lookback and holding period when using robust optimization in the commodity portfolio formation process.

EFFECTS OF MULTIPLE CRITERIA ON PORTFOLIO OPTIMIZATION

2014 ◽  
Vol 13 (01) ◽  
pp. 77-99 ◽  
Author(s):  
TUNCER ŞAKAR CEREN ◽  
MURAT KÖKSALAN
Keyword(s):  
At Risk ◽  
Portfolio Optimization ◽  
Decision Maker ◽  
Value At Risk ◽  
Efficient Frontier ◽  
Conditional Value At Risk ◽  
Computational Studies ◽  
Expected Return ◽  
Risk Criteria ◽  
Return Variance

We study the effects of considering different criteria simultaneously on portfolio optimization. Using a single-period optimization setting, we use various combinations of expected return, variance, liquidity and Conditional Value at Risk criteria. With stocks from Borsa Istanbul, we make computational studies to show the effects of these criteria on objective and decision spaces. We also consider cardinality and weight constraints and study their effects on the results. In general, we observe that considering alternative criteria results in enlarged regions in the efficient frontier that may be of interest to the decision maker. We discuss the results of our experiments and provide insights.

Sign in / Sign up

Export Citation Format

Share Document