Value-at-risk (Var) Application at Hypothetical Portfolios in Jakarta Islamic Index

The paper is an exploratory study to apply the method of historical simulation based on the concept of Value at Risk on hypothetical portfolios on Jakarta Islamic Index (JII). Value at Risk is a tool to measure a portfolio's exposure to market risk. We construct four portfolios based on the frequencies of the companies in Jakarta Islamic Index on the period of 1 January 2008 to 2 August 2010. The portfolio A has 12 companies, Portfolio B has 9 companies, portfolio C has 6 companies and portfolio D has 4 companies. We put the initial investment equivalent to USD 100 and use the rate of 1 USD=Rp 9500. The result of historical simulation applied in the four portfolios shows significant increasing risk on the year 2008 compared to 2009 and 2010. The bigger number of the member in one portfolio also affects the VaR compared to smaller member. The level of confidence 99% also shows bigger loss compared to 95%. The historical simulation shows the simplest method to estimate the event of increasing risk in Jakarta Islamic Index during the Global Crisis 2008

Perhitungan Value at Risk Pada Portofolio Saham Menggunakan Copula (Studi Kasus : Saham- Saham Perusahaan Di Indonesia Periode 13 Oktober 2011 - 12 Oktober 2016)

Investment is one of the way that is widely performed by people to achieve profitability in the future.Stock data is a data that is obtained from the observation that stock prices can be categorized into time series data, which usually have a tendency to fluctuate rapidly by the time so the variance of the residual will always change all the time or not constant, or often called heteroscedasticity case. Forecasting and data analysis is intended to minimize the risk and uncertainty factors. The risks can not be avoided but can be managed and estimated using Value at Risk (VaR) measurement tool. Copula theory is one of the tool that can be used to fit the joint distribution because it does not require the assumption of normality of the data so it is flexible enough for a variety of data, especially for financial data. This research is conducted using the method of Copula-GARCH to fit the three stocks of companies return data in Indonesia which have high volatility, those are PT Vale Indonesia Tbk (INCO), PT Bank Central Asia Tbk (BCA), and PT Indocement Tunggal Tbk (INTP) in period of October 13, 2011 to October 12, 2016 into ARIMA-GARCH model. The analysis is followed by copula on two stocks that have the highest ARIMA-GARCH residual correlation, those are BCA and INTP.Copula Gumbel is selected as the best copula with the amount of is 1,337. The risk derived from the calculation of Value at Risk (VaR) at the 99% confidence level is 3,922%, at the 95% confidence level is 2,397%, and at the 90% confidence level is 1,745%

Penghitungan Value at Risk Portofolio Optimum Saham Perusahaan Berbasis Syariah Dengan Pendekatan Ewma

The objective of this research is to examine maximum losses when investor invests on syariah based stock. Markowitz model is used for constructing the optimal portfolio. Value at Risk Model is also used for calculating the expected losses. The research indicates that volatility seems to cluster in a predictable fashion. Therefore the research forecasts variances used exponentially weighted moving average (EWMA) model. This research also aims to evaluate whether the EWMA model can predict variances reasonably well. The data used in this research are syariah based stock which had been included in Jakarta Islamic Index (JII) during the year 2005—2006. This research provides that VAR models using an EWMA forecast are good enough for predicting risk. The number of exception of 508 daily datas are only less than 5% or valid at confident level 95%. As benchmark we also use historical method and monte carlo simulation to compare performance of EWMA forecast

Pengukuran Value at Risk Menggunakan Prosedur Volatility Updating Hull and White Berdasarkan Exponentially Weighted Moving Average (Ewma) (Studi Kasus Pada Portofolio Dua Saham)

Investment is an effort to get profits for individual or institution. But the investment policy is always faced with market risk as the effect of financial instruments movement such as stock price movements. Market risk measurement tool commonly used is Value at Risk (VaR), which measures the amount of loss at a certain confidence level. VaR measurement by Hull and White volatility updating procedure is a modification of the historical simulation involving information of volatility change calculated by Exponentially Weighted Moving Average (EWMA). This procedure is fit to financial data such as stock returns that are generally not normally distributed and are heteroskedastic. VaR calculation applied to the portfolio between Kalbe Farma Tbk (KLBF) stock and Lippo Karawaci Tbk (LPKR) stock from 3 January 2011 to 19 April 2013 were selected based on the largest trading volume at the end of the observation for LQ45 stocks listed in the Indonesia Stock Exchange (IDX) . The data used is the return calculated from the closing price of stocks. The validity of VaR was tested through a back test by Kupiec test, and concluded that the 95% VaR and 99% VaR are valid

Value-at-Risk versus Non-Value-at-Risk Traders

In the paper, I simulate the games with a joint presence of 95% VaR-rule and return-rule groups of agents in the game. Simulations highlighted the level of omniscience, next being the rule, which agents follow at the decision-making, and the third the presence of liquidity agents in the game. Omniscient agents make different decisions than non-omniscient agents with non-omniscient return-rule agents performed a little better than the omniscient return-rule agents did, and omniscient VaR-rule agents performed slightly better than non-omniscient VaR-rule agents did. VaR-rule agents clearly outperform return-rule agents, with omniscient return-rule agents performing the worst. The role of liquidity agents has proved to be very significant with none of the two observed performed worst in the neither case.social networks, portfolio decision-making, stochastic finance, Value-at-Risk

Analisis Nilai Risiko (Value at Risk) Menggunakan Uji Kejadian Bernoulli (Bernoulli Coverage Test) (Studi Kasus Pada Indeks Harga Saham Gabungan)

Risk management is a systematic procedure to decrease the risk of an asset. Risk must be calculated in order to determine the best strategy in investing. Value at Risk (VaR) is a measure of risk that can be used. VaR measures the worst loss that can be happen in the future at a certain confidence level. There are many method to compute VaR. However, the methods are useful if it can predict future risks accurately. Therefore, the methods should be evaluate with a backtesting procedure. This research analyze the two methods of computing VaR, Historical Simulation and Johnson transformation approach, that estimate the risk of Jakarta Composite Index and backtest the methods use Bernoulli Coverage Test. The result, if using the relative VaR to forecast the risk of Jakarta Composite Index, the historical simulation approach can be used if the expected probability of violation is . Whereas the Johnson transformation approach can be used if the expected probability of violation is . If using the absolute VaR to forecast the risk of Jakarta Composite Index, the historical simulation approach can be used if the expected probability of violation is . Whereas the Johnson transformation approach can be used if the expected probability of violation is

Pengukuran Value at Risk Pada Aset Tunggal Dan Portofolio Dengan Simulasi Monte Carlo

Value at Risk (VaR) is the established standard for measuring market risk. VaR measures the worst expected loss under normal market conditions over a specific time interval at a given confidence level. A VaR statistic has three components: a time period, a confidence level and a loss amount (or loss percentage). The Monte Carlo simulation method calculates the change in the value of positions by using a random sample generated by price scenarios. Instead of using the past value of risk factors, Monte Carlo simulation generates models to estimate the risk factors from past portfolio returns by specifying the distributions and their parameters. Using these distributions and parameters, we can generate thousands of hypothetical scenarios for risk factors and, finally, we can determine future prices or rates based on hypothetical scenarios. VaRs can be derived from the cumulative distribution of future prices or rates for given confidence levels. In this paper, we calculate VaR at PT Astra International Tbk., PT Telekomunikasi Tbk., and the portfolio of the two assets. PT. Astra International Tbk has higher VaR than PT. Telekomunikasi Tbk. The VaR of a portfolio has lower result than VaR of each single asset