Value at Risk on Composite Price Share Index Stock Data

Transcription

1 Journal of Physics: Conference Series PAPER OPEN ACCESS Value at Risk on Composite Price Share Index Stock Data To cite this article: A Oktaviarina 2018 J. Phys.: Conf. Ser View the article online for updates and enhancements. This content was downloaded from IP address on 22/09/2018 at 17:56

2 Value at Risk on Composite Price Share Index Stock Data A Oktaviarina Matematics Department, Universitas Negeri Surabaya affiatioktaviarina@unesa.ac.id Abstract : The financial servicest authority was declared Let s Save Campaign on n commemoration of the World Savings Day that falls on this day, October 31, The activity was greeted enthusiastically by Indonesia Stock Exchange by taking out the slogan Let s Save The Stocks. Stock is a form of investment that is expected to benefit in the future despite has risks. Value at Risk (VaR) is a method that can measure how much the risk of a financial investment. Composite Stock Price Indeks is the stock price index used by Indonesia Stock Exchange as stock volatility benchmarks in Indonesia. This study aimed to estimate Value at Risk (VaR) on closing price Composite Price Share Index Stock data on the period 20 September 2016 until 20 September Box-Pierce test results p value= which is greater than, that shows homoskedasticity. Value at Risk (VaR) with Variance Covariance Method is Rp ,07 which means with 99% confindence interval someone who invests Rp ,00 will get Rp ,07 as a maximum loss. Keywords: Stock, Value at Risk, Variance Covariance Method, Homoskedasticity 1. Introduction The financial servicest authority was declared Let s Save Campaign on n commemoration of the World Savings Day that falls on this day, October 31, The activity was greeted enthusiastically by Indonesia Stock Exchange by taking out the slogan Let s Save The Stocks. Stock is a form of investment that is expected to benefit in the future despite has risks. risk is defined as a condition in which it contains an exposure that may be detrimental (Gallati, 2003). Ferdiansyah (2006) said Value at Risk (VaR) is a very popular method used for risk measurement. There are three methods in VaR namely Monte Carlo method, Historical method and Variance-Covariance method. Variance-Covariance method is one of the famous method to estimate Value at Risk, the method will be used in this paper. 2. Return Return a stock is a profit that will be earned by someone who buys stocks. The return value can be calculated by formula Continously Compounded Return as follows (Rosadi D, 2014): (1) with =stock return =stock price in period to t =stock price in period to t-1 3. Value at Risk (VaR) Value at Risk (VaR) is a method to estimate the maximum loss to be earned over a given period of time (Jorion, P, 2001). VaR with variance covariance method can be written as follows: (2) with E = asset value Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by Ltd 1

3 T = time horizon = simple standard deviation = confidence interval 4. Descriptive Statistics The used data is secondary data about closing price on Composite Price Share Index Stock period 20 September 2016 until 20 September Table 1. Descriptive Statiscs of Closing Price Data Table 1 displays descriptive statistics of Cosing Price data, for instance minimum data is and is the maximum. 5. Stationarity Test Closing Price Time Series Plot of Closing Price Period Figure 1. Time Series Plot of Closing Price Figure 1 reveals that time series plot of closing price is not stationary yet in mean and variance. In order to reach stationarity, data have to transform form Closing Price data to Return data by using equation (1). Figure 2. Time Series Plot of Return Data Figure 2 depicts that return data gained the stationarity in mean and variance since both value are equal to zero. 6. Model Identification 2

4 Figure 3. ACF for Return Data Figure 4. PACF for Return Data Figure 3 and 4 indicate cut off after third lag, that is to say AR, MA or ARMA is model for return data. It means that homoscedasticity model is fullfiled. So that the best model for return data is ARIMA (0,0,3) or MA (3) with zero mean. 7. Fit Model MA(3) Box-Pierce test done to ensure there MA (3) is the best model for return data. The result of Box-Pierce test is X-squared=0, , df=1, p-value=0,9528. P value greater than =5%, it means that MA(3) is the best model for return data. 8. Value at Risk (VaR) Value at Risk (VaR) with Variance Covariance Method by using equation (2) is ,07 which means with 99% confindence interval someone who invests Rp ,00 will get Rp ,07 as a maximum loss. 9. Conclusion Best model for log return of Composite Price Share Index Stock data is ARIMA (0,0,3). Value at Risk (VaR) with Variance Covariance Method is Rp ,07 which means with 99% confindence interval someone who invests Rp ,00 will get Rp ,07 as a maximum loss References [1] Gallati, R. (2003). Risk Management & Capital Adequacy. New York: McGraw-Hill Inc [2] Ferdiansyah, T. (2006). Refleksi dan Strategi Penerapan Manajemen Risiko Perbankan Indonesia. Jakarta: PT. Elex Media Komputindo. 3

5 [3] Rosadi, D. (2014).Analisis Runtun Waktu dan Aplikasinya Dengan R. Gadjah Mada University, Press [4] Jorion, P. (2001). Value at Risk. New York. McGraw-Hill Inc 4