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Submitted
Nov 16, 2022
Accepted
May 23, 2023
Published
Jun 30, 2023
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Abstract

Economics is one of the most important fields for a country. One of the activities that illustrate the importance of the economy in a country is an investment. Investment activities, especially stock investment, are included in the capital market activities that various age groups currently carry out. Stocks are generally known to have high-risk, high-return characteristics. Therefore we need a way to minimize losses in investing. This study uses time series analysis theory to analyze LQ45 stock data.The data used is the closing price of PT. Bank Central Asia, Tbk. obtained from finance.Yahoo.com. The results of this study indicate that the return of daily closing price data of PT. Bank Central Asia, Tbk. during the period 2017-2021, there are heteroscedasticity and asymmetric shocks, so variations of the ARCH/GARCH model are needed to obtain accurate forecasting results. One suitable model is Threshold GARCH (TGARCH). The results of this study indicate that the suitable forecasting model for the data is the MA(3)-TGARCH(1,1) model. The model produces forecasts with an accuracy rate based on MAPE of 0.895% for the next seven days

Keywords

Time Series, Stock Return, Heteroscedasticity, TGARCH, Forecasting

Article Details

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
1.
Nazarudin J, Gusriani N, Parmikanti K, Susanti S. Application of Threshold Generalized Autoregressive Conditional Heteroscedastic (TGARCH) Model in Forecasting the LQ45 Stock Price Return. EKSAKTA [Internet]. 2023Jun.30 [cited 2024Nov.5];24(02):271-84. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/369

References

  1. Anjum, S. (2020). Impact of market anomalies on stock exchange: a comparative study of KSE and PSX. Future Business Journal, 6(1), 1-11.
  2. Khan, S., & Alghulaiakh, H. (2020). ARIMA model for accurate time series stocks forecasting. International Journal of Advanced Computer Science and Applications, 11(7).
  3. Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S., & Ciccozzi, M. (2020). Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in brief, 29, 105340.
  4. Challa, M. L., Malepati, V., & Kolusu, S. N. R. (2020). S&p bse sensex and s&p bse it return forecasting using arima. Financial Innovation, 6(1), 47.
  5. Monika, P., Ruchjana, B., & Abdulla, A. (2022). The implementation of the ARIMA-ARCH model using data mining for forecasting rainfall in Bandung city. International Journal of Data and Network Science, 6(4), 1309-1318.
  6. Rita, M. R., Wahyudi, S., & Muharam, H. (2018). Bad Friday, Monday effect and political issue: application of ARCH-GARCH model to analyze seasonal pattern of stock return. International Journal of Engineering & Technology, 7(3.30), 38-47.
  7. Endri, E., Abidin, Z., Simanjuntak, T. P., & Nurhayati, I. (2020). Indonesian stock market volatility: GARCH model. Montenegrin Journal of Economics, 16(2), 7-17.
  8. Emenogu, N. G., Adenomon, M. O., & Nweze, N. O. (2020). On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting. Financial Innovation, 6(1), 18.
  9. Olayemi, M. S., Olubiyi, A. O., Olajide, O. O., & Ajayi, O. F. (2021). Modelling the Efficiency of TGARCH Model in Nigeria Inflation Rate. Journal of Statistical Modeling & Analytics (JOSMA), 3(2).
  10. Lorenzo-Valdés, A., & Ruíz-Porras, A. (2019). Conditional dependence between oil and exchange rate returns in a developing oil-exporting economy: An investigation with copula-based TGARCH models. International Journal of Global Energy Issues, 42(1-2), 21-44.
  11. Beckers, B., Herwartz, H., & Seidel, M. (2017). Risk forecasting in (T) GARCH models with uncorrelated dependent innovations. Quantitative Finance, 17(1), 121-137.
  12. Cai, Y., & Li, G. (2021). A quantile function approach to the distribution of financial returns following TGARCH models. Statistical Modelling, 21(3), 189-219.
  13. Chen, J., Li, H., & Lv, Y. (2022). Using TGARCH-M to Model the Impact of Good News and Bad News on Covid-19 Related Stocks’ Volatilities. Journal of Financial Risk Management, 11(2), 441-480.
  14. Hongwiengjan, W., & Thongtha, D. (2021). An analytical approximation of option prices via TGARCH model. Economic Research-Ekonomska Istraživanja, 34(1), 948-969.
  15. Munir, Q., & Sook Ching, K. (2019). Revisiting calendar effects in Malaysian finance stocks market: Evidence from threshold GARCH (TGARCH) model. Communications in Statistics-Theory and Methods, 48(6), 1377-1400.
  16. Robiyanto, R., Ernayani, R., & Ismail, R. S. (2019). Formulation of a dynamic portfolio with stocks and fixed-income instruments in the Indonesian capital market. Organizations and Markets in Emerging Economies, 10(1), 132-146.
  17. Shrestha, M. B., & Bhatta, G. R. (2018). Selecting appropriate methodological framework for time series data analysis. The Journal of Finance and Data Science, 4(2), 71-89.
  18. Yang, F., Ren, H., & Hu, Z. (2019). Maximum likelihood estimation for three-parameter Weibull distribution using evolutionary strategy. Mathematical Problems in Engineering, 2019.
  19. Hassani, H., & Yeganegi, M. R. (2019). Sum of squared ACF and the Ljung–Box statistics. Physica A: Statistical Mechanics and its Applications, 520, 81-86.
  20. Hauenstein, S., Wood, S. N., & Dormann, C. F. (2018). Computing AIC for black-box models using generalized degrees of freedom: A comparison with cross-validation. Communications in Statistics-Simulation and Computation, 47(5), 1382-1396.
  21. Rice, G., Wirjanto, T., & Zhao, Y. (2020). Tests for conditional heteroscedasticity of functional data. Journal of Time Series Analysis, 41(6), 733-758.
  22. Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics letters, 158, 3-6.
  23. Ausloos, M., Zhang, Y., & Dhesi, G. (2020). Stock index futures trading impact on spot price volatility. The CSI 300 studied with a TGARCH model. Expert Systems with Applications, 160, 113688.
  24. Abdullah, S. N. (2020). Using TGARCH, TGARCH-M, EGARCH, EGARCH-M, PGARCH and PGARCH-M models Gaussian and non-Gaussian for modeling (EUR/USD) and (GBP/USD) Exchange Rate. Periodicals of Engineering and Natural Sciences, 8(3), 1535-1547.
  25. Demertzis, K., Tsiotas, D., & Magafas, L. (2020). Modeling and forecasting the COVID-19 temporal spread in Greece: An exploratory approach based on complex network defined splines. International Journal of Environmental Research and Public Health, 17(13), 4693.
  26. Mallikarjuna, M., & Rao, R. P. (2019). Evaluation of forecasting methods from selected stock market returns. Financial Innovation, 5(1), 1-16.
  27. Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623.
  28. Lawrence, K. D., Klimberg, R. K., & Lawrence, S. M. (2009). Fundamentals of forecasting using excel. Industrial Press Inc..