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

scholarly journals Identifikasi Model I-Garch (Integrated Generalized Autoregressive Conditionally Heterocedastic) Untuk Peramalan Value At Risk

2020 ◽  
Vol 3 (1) ◽  
pp. 25-38
Author(s):  
Nendra Mursetya Somasih Dwipa
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Confidence Level ◽  
Value At Risk ◽  
Time Series Data ◽  
Composite Index ◽  
Information Criteria ◽  
Stock Index ◽  
Series Data ◽  
Residual Variance

A stock returns data are one of type time series data who has a high volatility and different variance in every point of time. Such data are volatile, seting up a pattern of asymmetrical, having a nonstationary model, and that does not have a constant residual variance (heteroscedasticity). A time series ARCH and GARCH model can explain the heterocedasticity of data, but they are not always able to fully capture the asymmetric property of high frequency. Integrated Generalized Autoregresive Heteroskedascticity (IGARCH) model overcome GARCH weaknesses in capturing unit root. Furthermore IGARCH models were used to estimate the value of VaR as the maximum loss that will be obtained during a certain period at a certain confidence level. The aim of this study was to determine the best forecasting model of Jakarta Composite Index (JSI). The model had used in this study are ARCH, GARCH, and IGARCH. From the case studies were carried out, the result of forecasting volatility of stock index by using IGARCH(1,1) obtained log likelihood values that 3857,979 to the information criteria AIC = -6,3180; BIC = -6,3013; SIC = -6,3180; dan HQIC = -6,3117. Value of VaR movement of the JCI if it becomes greater the investment is Rp.500,000,000.00 with a confidence level of 95% on the date of July 2, 2015 using a model IGARCH (1,1) is Rp7.166.315,00.

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 Forecasting Value at Risk: Evidence from Emerging Economies in Asia

2018 ◽  
Vol 2 (1) ◽  
pp. 77-90
Author(s):  
Le Trung Thanh ◽  
Nguyen Thi Ngan ◽  
Hoang Trung Nghia
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Stock Returns ◽  
Emerging Economies ◽  
Value At Risk ◽  
Stock Index ◽  
Historical Simulation ◽  
Out Of Sample ◽  
Student’S T ◽  
T Distribution

In this paper, various Value-at-Risk techniques are applied to stock indices of 9 Asian emerging financial markets. The results from our selected models are then backtested by Unconditional Coverage, Independence, Joint Tests of Unconditional Coverage and Independence and Basel tests to ensure the quality of Value-at-Risk (VaR) estimates. The main conclusions are: (1) Timevarying volatility is the most important characteristic of stock returns when modelling VaR; (2) Financial data is not normally distributed, indicating that the normality assumption of VaR is not relevant; (3) Among VAR forecasting approaches, the backtesting based on in- and out-of-sample evaluations confirms its superiority in the class of GARCH models; Historical Simulation (HS), Filtered Historical Simulation (FHS), RiskMetrics and Monte Carlo were rejected because of its underestimation (for HS and RiskMetrics) or overestimation (for the FHS and Monte Carlo); (4) Models under student’s t and skew student’s t distribution are better in taking into account financial data’s characters; and (5) Forecasting VaR for futures index is harder than for stock index. Moreover, results show that there is no evidence to recommend the use of GARCH (1,1) to estimate VaR for all markets. In practice, the HS and RiskMetrics are popularly used by banks for large portfolios, despite of its serious underestimations of actual losses. These findings would be helpful for financial managers, investors and regulators dealing with stock markets in Asian emerging economies.  

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 On the Volatility of Daily Stock Returns of Total Petroleum Company of Nigeria: Evidence from GARCH Models, Value-at-Risk and Backtesting

2018 ◽  
Author(s):  
Ngozi G. Emenogu ◽  
Monday Osagie Adenomon ◽  
Nweze Nwaze Obinna
Keyword(s):  
At Risk ◽  
Stock Returns ◽  
Confidence Level ◽  
Value At Risk ◽  
Stock Price ◽  
Garch Models ◽  
Correct Number ◽  
Energy Company ◽  
Petroleum Company ◽  
Tests Of Independence

Total Nigeria Plc is a Marketing and Services subsidiary of Total; a multinational energy company operating in more than 130 countries and committed to providing sustainable products and services for its customers. For over 50 years, Total Nigeria  Plc  has  remained  the  leader  in  the  downstream  sector  of  the  Nigerian  oil  and  gas  industry. This study investigated the volatility of the stock price of Total Petroleum Nigeria plc using nine (9) GARCH models namely sGARCH, gjrGARCH, eGARCH, iGARCH, aPARCH, TGARCH, NGARCH, NAGARCH and AVGARCH. We also investigated the Value-at-Risk (VaR) and Backtesting of the Models. The aim actually of this study is to boost the confidence of the shareholders and investors of the Total Nigeria plc. To achieve this, daily stock price for Total petroleum Nigeria plc from secondary was collected from January 2nd 2001 to May 8th 2017. . The study used both normal and student t innovations, using Akaike Information Criterion (AIC) to select the best model, for normal innovations for log returns and cleansed log returns of Total plc, the eGARCH and sGARCH models performed best respectively, while NGARCH model performed best for student t innovation for both log returns and cleansed returns of Total plc. The persistence of the models are stable except in few cases where iGARCH, eGARCH where not stable. Also for student t innovation, the sGARCH and gjrGARCH fails to converge. The mean-reverting number of day for the returns of Total Nigeria plc differs from model to model. Evidence from the VaR Analysis revealed from the selected models revealed that the Risk of VaR losses is high at 99% confidence level, slightly high at 95% confidence level and better at 90% confidence level. Although The Duration-Based Tests of independence conducted revealed that the models are correctly specified since in all cases the null hypotheses were accepted. This means that the probability of an exception on any day did not depends on the outcome of the previous day. Finally, both the unconditional (Kupiec) and conditional (Christoffersen) coverage tests for the correct number of exceedances for both Total stock returns and cleansed returns. The tests revealed rejection of the models at 1% level of significance. This confirms that unconditional (Kupiec) and conditional (Christoffersen) coverage tests for the correct number of exceedances are reliable compared to the Duration-Based Tests of independence. Therefore we recommend that shareholders and investors in Total Nigeria plc are to remain and continue to investment in Total Nigeria plc because if there is any form of losses, the price of the stock has the potentials to improve in the future. Again, though the risk is high at 99% confidence level, this in line with the financial theory that states that an asset with high expected risk would pay higher return on the average.

scholarly journals Optimisation of Time-Varying Asset Pricing Models with Penetration of Value at Risk and Expected Shortfall

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 394
Author(s):  
Adeel Nasir ◽  
Kanwal Iqbal Khan ◽  
Mário Nuno Mata ◽  
Pedro Neves Mata ◽  
Jéssica Nunes Martins
Keyword(s):  
At Risk ◽  
Asset Pricing ◽  
Stock Returns ◽  
Value At Risk ◽  
Expected Shortfall ◽  
Time Varying ◽  
Pricing Models ◽  
Asset Pricing Model ◽  
Asset Pricing Models ◽  
Market Beta

This study aims to apply value at risk (VaR) and expected shortfall (ES) as time-varying systematic and idiosyncratic risk factors to address the downside risk anomaly of various asset pricing models currently existing in the Pakistan stock exchange. The study analyses the significance of high minus low VaR and ES portfolios as a systematic risk factor in one factor, three-factor, and five-factor asset pricing model. Furthermore, the study introduced the six-factor model, deploying VaR and ES as the idiosyncratic risk factor. The theoretical and empirical alteration of traditional asset pricing models is the study’s contributions. This study reported a strong positive relationship of traditional market beta, value at risk, and expected shortfall. Market beta pertains its superiority in estimating the time-varying stock returns. Furthermore, value at risk and expected shortfall strengthen the effects of traditional beta impact on stock returns, signifying the proposed six-factor asset pricing model. Investment and profitability factors are redundant in conventional asset pricing models.

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 How Risky Are the Options? A Comparison with the Underlying Stock Using MaxVaR as a Risk Measure

Risks ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 76
Author(s):  
Saswat Patra ◽  
Malay Bhattacharyya
Keyword(s):  
At Risk ◽  
Stock Returns ◽  
Value At Risk ◽  
Risk Measure ◽  
Stock Index ◽  
Risk Exposure ◽  
Index Options ◽  
Significance Level ◽  
Significance Levels ◽  
Better Than

This paper investigates the risk exposure for options and proposes MaxVaR as an alternative risk measure which captures the risk better than Value-at-Risk especially. While VaR is a measure of end-of-horizon risk, MaxVaR captures the interim risk exposure of a position or a portfolio. MaxVaR is a more stringent risk measure as it assesses the risk during the risk horizon. For a 30-day maturity option, we find that MaxVaR can be 40% higher than VaR at a 5% significance level. It highlights the importance of MaxVaR as a risk measure and shows that the risk is vastly underestimated when VaR is used as the measure for risk. The sensitivity of MaxVaR with respect to option characteristics like moneyness, time to maturity and risk horizons at different significance levels are observed. Further, interestingly enough we find that the MaxVar to VaR ratio is higher for stocks than the options and we can surmise that stock returns are more volatile than options. For robustness, the study is carried out under different distributional assumptions on residuals and for different stock index options.

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