Abstract
Value at Risk (VaR) is one of the standard methods that can be used in measuring risk in stock investments. VaR is defined as the maximum possible loss for a particular position or portfolio in the known confidence level of a specific time horizon. The main topic discussed in this thesis is to estimate VaR using the TARCH (Threshold Autoregressive Conditional Heteroscedasticity) model in a time series by considering the effect of long memory. The TARCH model is applied to the daily log return data of a company's stock in Indonesia to estimate the amount of quantile that will be used in calculating VaR. Based on the analysis, it was found that with a significance level of 95% and assuming an investment of 200,000,000 IDR, the VaR using the TARCH model approach was 5,110,200 IDR per day.
Highlights
In the capital market, almost all investments contain an element of uncertainty or risk
Based on the description above, the purpose of this paper is to calculate the amount of Value at Risk (VaR) of a stock, which is to calculate the maximum loss at a certain position with the level of confidence that has been known in a specific horizon time
The approach used is the Threshold Autoregressive Conditional Heteroscedasticity (TARCH) time series model to estimate the size of the quantile that will be used in the calculation of Value at Risk (VaR) (Sukono et al, 2019)
Summary
Almost all investments contain an element of uncertainty or risk. Because investors face risky investment opportunities, a measurement tool is needed to test these market risks, so that investors can safely know how far they can invest Based on the description above, the purpose of this paper is to calculate the amount of Value at Risk (VaR) of a stock, which is to calculate the maximum loss at a certain position with the level of confidence that has been known in a specific horizon time. The approach used is the TARCH time series model to estimate the size of the quantile that will be used in the calculation of Value at Risk (VaR) (Sukono et al, 2019). Nurfadhlina Abdul Halim et al./ Operations Research: International Conference Series Vol 1, No 2, pp. 33-42, 2020
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Operations Research: International Conference Series
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
