How to cite this paper
Suripto, S. (2023). Decision-making model to predict auto-rejection: An implementation of ARIMA for accurate forecasting of stock price volatility during the Covid-19.Decision Science Letters , 12(1), 107-116.
Refrences
Abdelzaher, M. A., & Elgiziry, K. (2017). The effect of daily stock price limits on the investment risk: evidence from the Egyptian stock market. Accounting and Finance Research, 6(4), 1. https://doi.org/10.5430/afr.v6n4p1
Abu-lila, Z. M. (2021). Determinants of Investment in the Jordanian Productive Sectors. The Journal of Asian Finance, Economics and Business, 8(4), 635-641.
Akhmetzhanov, B., Tazhibekova, K., Shametova, A., & Urazbekov, A. (2018). Expanded implementation of solar photovoltaics: forecasting and risk assessment. International Journal of Energy Economics and Policy, 8(5), 113–118.
Aktas, O. U., Kryzanowski, L., & Zhang, J. (2021). Volatility spillover around price limits in an emerging market. Finance research letters, 39, 101610. https://doi.org/10.1016/j.frl.2020.101610
Alsaedi, Y., Tularam, G. A., & Wong, V. (2019). Application of autoregressive integrated moving average modeling for the forecasting of solar, wind, spot and options electricity prices: The Australian national electricity market. International Journal of Energy Economics and Policy, 9(4), 263–272. https://doi.org/10.32479/ijeep.7785
Arif, M. R., & Hasan, D. (2021). Relationship between innovation activities and business performance: A case study in Indonesia. The Journal of Asian Finance, Economics and Business, 8(4), 307-315.
Asuamah, S. Y., & Ohene-Manu, J. (2015). An econometric investigation of forecasting premium fuel. International Journal of Energy Economics and Policy, 5(3), 716–724.
Azhar, R., Kesumah, F. S. D., Ambya, Wisnu, F. K., & Russel, E. (2020). Application of short-term forecasting models for energy entity stock price: evidence from Indika Energi Tbk, Jakarta Islamic index. International Journal of Energy Economics and Policy, 10(1), 294–301. https://doi.org/10.32479/ijeep.8715
Bakar, N. A., & Rosbi, S. (2017). Autoregressive integrated moving average (ARIMA) model for forecasting cryptocurrency exchange rate in high volatility environment: a new insight of bitcoin transaction. International Journal of Advanced Engineering Research and Science, 4(11), 130–137. https://doi.org/10.22161/ijaers.4.11.20
Brockwell, P.J., & Davis, R.A. (2002). Introduction to time series and forecasting. New York: Springer-Verlag.
Danışoğlu, S., & Güner, Z. N. (2018). Do price limits help control stock price volatility? Annals of Operations Research, 260(1–2), 129–157. https://doi.org/10.1007/s10479-016-2317-y
Denkowska, A., & Wanat, S. (2020). Dependencies and systemic risk in the European insurance sector: New evidence based on copula-DCC-garch model and selected clustering methods. Entrepreneurial Business and Economics Review, 8(4), 7–27. https://doi.org/10.15678/EBER.2020.080401
Dritsaki, C. (2018). The performance of hybrid ARIMA-GARCH modeling and forecasting oil price. International Journal of Energy Economics and Policy, 8(3), 14–21.
Dritsaki, M., & Dritsaki, C. (2020). Forecasting European Union CO2 emissions using autoregressive integrated moving average-autoregressive conditional heteroscedasticity models. International Journal of Energy Economics and Policy, 10(4), 411–423. https://doi.org/10.32479/ijeep.9186
Fama, E. F. (1980). Agency problems and the theory of the firm. Journal of political economy, 88(2), 288-307.
FARISKA, P., NUGRAHA, N., PUTERA, I., ROHANDI, M. M. A., & FARISKA, P. (2021). Microblogging sentiment investor, return and volatility in the COVID-19 era: Indonesian Stock Exchange. The Journal of Asian Finance, Economics and Business, 8(3), 61-67.
Goh, T. S., Henry, H., & Albert, A. (2021). Determinants and Prediction of the Stock Market during COVID-19: Evidence from Indonesia. Journal of Asian Finance, Economics and Business, 8(1), 001–006. https://doi.org/10.13106/jafeb.2021.vol8.no1.001
Gontijo, T. S., Rodrigues, A. D. C., De Muylder, C. F., la Falce, J. L., & Pereira, T. H. M. (2020). Analysis of olive oil market volatility using the arch and garch techniques. International Journal of Energy Economics and Policy, 10(3), 423–428. https://doi.org/10.32479/ijeep.9138
Gunarto, T., Azhar, R., Tresiana, N., Supriyanto, & Ahadiat, A. (2020). An accurate estimated model of volatility crude oil price. International Journal of Energy Economics and Policy, 10(5), 228–233. https://doi.org/10.32479/ijeep.9513
Gürtler, M., & Paulsen, T. (2018). Forecasting performance of time series models on electricity spot markets. International Journal of Energy Sector Management, 12(4), 617–640. https://doi.org/10.1108/IJESM-12-2017-0006
Handika, R., & Chalid, D. A. (2018). The predictive power of log-likelihood of GARCH volatility. Review of Accounting and Finance, 17(4), 482–497. https://doi.org/10.1108/RAF-01-2017-0006
Hatta, H. H. M., Daud, F. M., & Mohamad, N. (2018). An application of time series ARIMA forecasting model for predicting the ringgit Malaysia-dollar exchange rate. Journal of Data Analysis, 1(1), 42–48. https://doi.org/10.24815/jda.v1i1.11884
Herwany, A., Febrian, E., Anwar, M., & Gunardi, A. (2021). The influence of the COVID-19 pandemic on stock market returns in Indonesia stock exchange. The Journal of Asian Finance, Economics and Business, 8(3), 39-47.
Hong, H. (2016). Information cascade and share market volatility: a Chinese perspective. The Journal of Asian Finance, Economics and Business, 3(4), 17-24.
Imtiaz, S., & Azhar, M. (2020). Ex-post effects of circuit breakers in crisis and calm markets: Long horizon evidence from wide-band Malaysian price limits. Journal of Economic Studies, 47(2), 333–350. https://doi.org/10.1108/JES-06-2018-0226
Jamalmanesh, A., Mashhadi, M. K., Seifi, A., & Falahi, M. A. (2018). Prediction of hydropower energy price using the gómes-maravall seasonal model. International Journal of Energy Economics and Policy, 8(2), 81–88.
Jati, K., & Premaratne, G. (2017). Analysis of Staple Food Price Behaviour: Multivariate BEKK-GARCH Model. The Journal of Asian Finance, Economics and Business, 4(4), 27–37. https://doi.org/10.13106/jafeb.2017.vol4.no4.27
Ji, S. H., & Yoon, K. C. (2020). The effects of widening daily stock price limits on the relevance between audit quality and stock return. Journal of Asian Finance, Economics and Business, 7(4), 107–119. https://doi.org/10.13106/JAFEB.2020.VOL7.NO4.107
Jihadi, M., Vilantika, E., Hashemi, S. M., Arifin, Z., Bachtiar, Y., & Sholichah, F. (2021). The Effect of Liquidity, Leverage, and Profitability on Firm Value: Empirical Evidence from Indonesia. Journal of Asian Finance, Economics and Business, 8(3), 423–431. https://doi.org/10.13106/jafeb.2021.vol8.no3.0423
Juan, T., Wu, Y., & Zhang, J. (2017). The performance of China’s stock market price limits: noise mitigator or noise maker? China Finance Review International, 7(1), 85–97. https://doi.org/10.1108/CFRI-07-2016-0096
Junghoon, S. (2016). Widened Price-Limits and Efficiency of Price Discovery over a Trading Day. Journal of Derivatives and Quantitative Studies, 24(2), 245–267. https://doi.org/10.1108/JDQS-02-2016-B0003
Karbowski, A., & Prokop, J. (2019). The impact of vertical r&d cooperation on the market performance of firms. Entrepreneurial Business and Economics Review, 7(4), 73–89. https://doi.org/10.15678/EBER.2019.070405
Kennedy, K., & Nourzad, F. (2016). Exchange rate volatility and its effect on stock market volatility. International Journal of Human Capital in Urban Management, 1(1), 37-46. https://doi.org/10.7508/ijhcum.2016.01.005
Khodavandloo, M., & Zakaria, Z. (2013, October). The effects of price limits in an emerging market: Evidence from the Bursa Malaysia. In International Academic Workshop on Social Science (IAW-SC-13) (pp. 114-117). Atlantis Press.
Kristiana, Y., Pramono, R., & Brian, R. (2021). Adaptation strategy of tourism industry stakeholders during the COVID-19 pandemic: A case study in Indonesia. The Journal of Asian Finance, Economics and Business, 8(4), 213-223. https://doi.org/10.13106/jafeb.2021.vol8.no4.0213
Laipaporn, J., & Tongkumchum, P. (2018). The use of information criteria for selecting some knots in natural cubic spline volatility estimation. In Proceedings of International Conference of Applied Statistics 2018 (pp. 161-164). Bangkok: Faculty of Sciences and Technologies, Prince of Songkla University, Pattani campus.
Laipaporn, J., & Tongkumchum, P. (2020). The time-varying correlation estimator using the natural cubic spline volatility. Advances and Applications in Statistics, 62(1), 79-95. http://doi.org/10.17654/AS062010079
Lam, K. C., & Oshodi, O. S. (2016). Forecasting construction output: a comparison of artificial neural network and Box-Jenkins model. Engineering, Construction and Architectural Management, 23(3), 302-322 https://doi.org/10.1108/ECAM-05-2015-0080
Lee, C. L., Stevenson, S., & Lee, M. -L. (2018). Low-frequency volatility of real estate securities and macroeconomic risk. Accounting and Finance, 58(2018), 311-342. https://doi.org/10.1111/acfi.12288
Li, H., Zheng, D., & Chen, J. (2014). Effectiveness, cause, and impact of price limit-Evidence from China’s cross-listed stocks. Journal of International Financial Markets, Institutions, and Money, 29(1), 217–241. https://doi.org/10.1016/j.intfin.2013.12.007
Ma, C. K., Rao, R. P., & Sears, R. S. (1989). Volatility, price resolution, and the effectiveness of price limits. Journal of Financial Services Research, 3(2–3), 165–199. https://doi.org/10.1007/BF00122800
Marín, J. B., Orozco, E. T., & Velilla, E. (2018). Forecasting electricity price in Colombia: A comparison between a neural network, ARMA process, and hybrid models. International Journal of Energy Economics and Policy, 8(3), 97–106.
Mihi-Ramírez, A., Arteaga-Ortíz, J., & Ojeda-González, S. (2019). The international movements of capital and labor: A study of foreign direct investment and migration flows. Entrepreneurial Business and Economics Review, 7(3), 143–160. https://doi.org/10.15678/EBER.2019.070308
Mikhaylov, A. Y. (2018). Volatility spillover effect between stock and exchange rate in oil-exporting countries. International Journal of Energy Economics and Policy, 8(3), 321–326.
Nyangarika, A., Mikhaylov, A., & Richter, U. H. (2019). Oil price factors: Forecasting on the base of the modified auto-regressive integrated moving average model. International Journal of Energy Economics and Policy, 9(1), 149–159. https://doi.org/10.32479/ijeep.6812
Pointer, L. V., & Khoi, P. D. (2019). Predictors of return on assets and return on equity for banking and insurance companies on Vietnam stock exchange. Entrepreneurial Business and Economics Review, 7(4), 185–198. https://doi.org/10.15678/EBER.2019.070411
Purwono, R., Mucha, K., & Mubin, M. K. (2018). The dynamic of Indonesia's current account deficit: Analysis of the impact of exchange rate volatility. Journal of Asian Finance, Economics, and Business, 5(2), 25-33. https://doi.org/10.13106/jafeb.2018.vol5.no2.25
Rahmi, M., Azma, N., Muttaqin, A. A., Jazil, T., & Rahman, M. (2016). Risk Volatility Measurement: Evidence from Indonesian Stock Market. The Journal of Asian Finance, Economics and Business, 3(3), 57–65. https://doi.org/10.13106/jafeb.2016.vol3.no3.57.
Rathnayaka, R. K. T., & Seneviratna, D. M. K. N. (2018). Taylor series approximation and unbiased GM (1, 1) based hybrid statistical approach for forecasting daily gold price demands. Grey Systems: Theory and Application, 9(1), 5–18. https://doi.org/10.1108/gs-08-2018-0032
Rillo, A. D. (2018). ASEAN financial integration: Opportunities, risks, and challenges. Public Policy Review, 14(5), 901-923.
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Akhmetzhanov, B., Tazhibekova, K., Shametova, A., & Urazbekov, A. (2018). Expanded implementation of solar photovoltaics: forecasting and risk assessment. International Journal of Energy Economics and Policy, 8(5), 113–118.
Aktas, O. U., Kryzanowski, L., & Zhang, J. (2021). Volatility spillover around price limits in an emerging market. Finance research letters, 39, 101610. https://doi.org/10.1016/j.frl.2020.101610
Alsaedi, Y., Tularam, G. A., & Wong, V. (2019). Application of autoregressive integrated moving average modeling for the forecasting of solar, wind, spot and options electricity prices: The Australian national electricity market. International Journal of Energy Economics and Policy, 9(4), 263–272. https://doi.org/10.32479/ijeep.7785
Arif, M. R., & Hasan, D. (2021). Relationship between innovation activities and business performance: A case study in Indonesia. The Journal of Asian Finance, Economics and Business, 8(4), 307-315.
Asuamah, S. Y., & Ohene-Manu, J. (2015). An econometric investigation of forecasting premium fuel. International Journal of Energy Economics and Policy, 5(3), 716–724.
Azhar, R., Kesumah, F. S. D., Ambya, Wisnu, F. K., & Russel, E. (2020). Application of short-term forecasting models for energy entity stock price: evidence from Indika Energi Tbk, Jakarta Islamic index. International Journal of Energy Economics and Policy, 10(1), 294–301. https://doi.org/10.32479/ijeep.8715
Bakar, N. A., & Rosbi, S. (2017). Autoregressive integrated moving average (ARIMA) model for forecasting cryptocurrency exchange rate in high volatility environment: a new insight of bitcoin transaction. International Journal of Advanced Engineering Research and Science, 4(11), 130–137. https://doi.org/10.22161/ijaers.4.11.20
Brockwell, P.J., & Davis, R.A. (2002). Introduction to time series and forecasting. New York: Springer-Verlag.
Danışoğlu, S., & Güner, Z. N. (2018). Do price limits help control stock price volatility? Annals of Operations Research, 260(1–2), 129–157. https://doi.org/10.1007/s10479-016-2317-y
Denkowska, A., & Wanat, S. (2020). Dependencies and systemic risk in the European insurance sector: New evidence based on copula-DCC-garch model and selected clustering methods. Entrepreneurial Business and Economics Review, 8(4), 7–27. https://doi.org/10.15678/EBER.2020.080401
Dritsaki, C. (2018). The performance of hybrid ARIMA-GARCH modeling and forecasting oil price. International Journal of Energy Economics and Policy, 8(3), 14–21.
Dritsaki, M., & Dritsaki, C. (2020). Forecasting European Union CO2 emissions using autoregressive integrated moving average-autoregressive conditional heteroscedasticity models. International Journal of Energy Economics and Policy, 10(4), 411–423. https://doi.org/10.32479/ijeep.9186
Fama, E. F. (1980). Agency problems and the theory of the firm. Journal of political economy, 88(2), 288-307.
FARISKA, P., NUGRAHA, N., PUTERA, I., ROHANDI, M. M. A., & FARISKA, P. (2021). Microblogging sentiment investor, return and volatility in the COVID-19 era: Indonesian Stock Exchange. The Journal of Asian Finance, Economics and Business, 8(3), 61-67.
Goh, T. S., Henry, H., & Albert, A. (2021). Determinants and Prediction of the Stock Market during COVID-19: Evidence from Indonesia. Journal of Asian Finance, Economics and Business, 8(1), 001–006. https://doi.org/10.13106/jafeb.2021.vol8.no1.001
Gontijo, T. S., Rodrigues, A. D. C., De Muylder, C. F., la Falce, J. L., & Pereira, T. H. M. (2020). Analysis of olive oil market volatility using the arch and garch techniques. International Journal of Energy Economics and Policy, 10(3), 423–428. https://doi.org/10.32479/ijeep.9138
Gunarto, T., Azhar, R., Tresiana, N., Supriyanto, & Ahadiat, A. (2020). An accurate estimated model of volatility crude oil price. International Journal of Energy Economics and Policy, 10(5), 228–233. https://doi.org/10.32479/ijeep.9513
Gürtler, M., & Paulsen, T. (2018). Forecasting performance of time series models on electricity spot markets. International Journal of Energy Sector Management, 12(4), 617–640. https://doi.org/10.1108/IJESM-12-2017-0006
Handika, R., & Chalid, D. A. (2018). The predictive power of log-likelihood of GARCH volatility. Review of Accounting and Finance, 17(4), 482–497. https://doi.org/10.1108/RAF-01-2017-0006
Hatta, H. H. M., Daud, F. M., & Mohamad, N. (2018). An application of time series ARIMA forecasting model for predicting the ringgit Malaysia-dollar exchange rate. Journal of Data Analysis, 1(1), 42–48. https://doi.org/10.24815/jda.v1i1.11884
Herwany, A., Febrian, E., Anwar, M., & Gunardi, A. (2021). The influence of the COVID-19 pandemic on stock market returns in Indonesia stock exchange. The Journal of Asian Finance, Economics and Business, 8(3), 39-47.
Hong, H. (2016). Information cascade and share market volatility: a Chinese perspective. The Journal of Asian Finance, Economics and Business, 3(4), 17-24.
Imtiaz, S., & Azhar, M. (2020). Ex-post effects of circuit breakers in crisis and calm markets: Long horizon evidence from wide-band Malaysian price limits. Journal of Economic Studies, 47(2), 333–350. https://doi.org/10.1108/JES-06-2018-0226
Jamalmanesh, A., Mashhadi, M. K., Seifi, A., & Falahi, M. A. (2018). Prediction of hydropower energy price using the gómes-maravall seasonal model. International Journal of Energy Economics and Policy, 8(2), 81–88.
Jati, K., & Premaratne, G. (2017). Analysis of Staple Food Price Behaviour: Multivariate BEKK-GARCH Model. The Journal of Asian Finance, Economics and Business, 4(4), 27–37. https://doi.org/10.13106/jafeb.2017.vol4.no4.27
Ji, S. H., & Yoon, K. C. (2020). The effects of widening daily stock price limits on the relevance between audit quality and stock return. Journal of Asian Finance, Economics and Business, 7(4), 107–119. https://doi.org/10.13106/JAFEB.2020.VOL7.NO4.107
Jihadi, M., Vilantika, E., Hashemi, S. M., Arifin, Z., Bachtiar, Y., & Sholichah, F. (2021). The Effect of Liquidity, Leverage, and Profitability on Firm Value: Empirical Evidence from Indonesia. Journal of Asian Finance, Economics and Business, 8(3), 423–431. https://doi.org/10.13106/jafeb.2021.vol8.no3.0423
Juan, T., Wu, Y., & Zhang, J. (2017). The performance of China’s stock market price limits: noise mitigator or noise maker? China Finance Review International, 7(1), 85–97. https://doi.org/10.1108/CFRI-07-2016-0096
Junghoon, S. (2016). Widened Price-Limits and Efficiency of Price Discovery over a Trading Day. Journal of Derivatives and Quantitative Studies, 24(2), 245–267. https://doi.org/10.1108/JDQS-02-2016-B0003
Karbowski, A., & Prokop, J. (2019). The impact of vertical r&d cooperation on the market performance of firms. Entrepreneurial Business and Economics Review, 7(4), 73–89. https://doi.org/10.15678/EBER.2019.070405
Kennedy, K., & Nourzad, F. (2016). Exchange rate volatility and its effect on stock market volatility. International Journal of Human Capital in Urban Management, 1(1), 37-46. https://doi.org/10.7508/ijhcum.2016.01.005
Khodavandloo, M., & Zakaria, Z. (2013, October). The effects of price limits in an emerging market: Evidence from the Bursa Malaysia. In International Academic Workshop on Social Science (IAW-SC-13) (pp. 114-117). Atlantis Press.
Kristiana, Y., Pramono, R., & Brian, R. (2021). Adaptation strategy of tourism industry stakeholders during the COVID-19 pandemic: A case study in Indonesia. The Journal of Asian Finance, Economics and Business, 8(4), 213-223. https://doi.org/10.13106/jafeb.2021.vol8.no4.0213
Laipaporn, J., & Tongkumchum, P. (2018). The use of information criteria for selecting some knots in natural cubic spline volatility estimation. In Proceedings of International Conference of Applied Statistics 2018 (pp. 161-164). Bangkok: Faculty of Sciences and Technologies, Prince of Songkla University, Pattani campus.
Laipaporn, J., & Tongkumchum, P. (2020). The time-varying correlation estimator using the natural cubic spline volatility. Advances and Applications in Statistics, 62(1), 79-95. http://doi.org/10.17654/AS062010079
Lam, K. C., & Oshodi, O. S. (2016). Forecasting construction output: a comparison of artificial neural network and Box-Jenkins model. Engineering, Construction and Architectural Management, 23(3), 302-322 https://doi.org/10.1108/ECAM-05-2015-0080
Lee, C. L., Stevenson, S., & Lee, M. -L. (2018). Low-frequency volatility of real estate securities and macroeconomic risk. Accounting and Finance, 58(2018), 311-342. https://doi.org/10.1111/acfi.12288
Li, H., Zheng, D., & Chen, J. (2014). Effectiveness, cause, and impact of price limit-Evidence from China’s cross-listed stocks. Journal of International Financial Markets, Institutions, and Money, 29(1), 217–241. https://doi.org/10.1016/j.intfin.2013.12.007
Ma, C. K., Rao, R. P., & Sears, R. S. (1989). Volatility, price resolution, and the effectiveness of price limits. Journal of Financial Services Research, 3(2–3), 165–199. https://doi.org/10.1007/BF00122800
Marín, J. B., Orozco, E. T., & Velilla, E. (2018). Forecasting electricity price in Colombia: A comparison between a neural network, ARMA process, and hybrid models. International Journal of Energy Economics and Policy, 8(3), 97–106.
Mihi-Ramírez, A., Arteaga-Ortíz, J., & Ojeda-González, S. (2019). The international movements of capital and labor: A study of foreign direct investment and migration flows. Entrepreneurial Business and Economics Review, 7(3), 143–160. https://doi.org/10.15678/EBER.2019.070308
Mikhaylov, A. Y. (2018). Volatility spillover effect between stock and exchange rate in oil-exporting countries. International Journal of Energy Economics and Policy, 8(3), 321–326.
Nyangarika, A., Mikhaylov, A., & Richter, U. H. (2019). Oil price factors: Forecasting on the base of the modified auto-regressive integrated moving average model. International Journal of Energy Economics and Policy, 9(1), 149–159. https://doi.org/10.32479/ijeep.6812
Pointer, L. V., & Khoi, P. D. (2019). Predictors of return on assets and return on equity for banking and insurance companies on Vietnam stock exchange. Entrepreneurial Business and Economics Review, 7(4), 185–198. https://doi.org/10.15678/EBER.2019.070411
Purwono, R., Mucha, K., & Mubin, M. K. (2018). The dynamic of Indonesia's current account deficit: Analysis of the impact of exchange rate volatility. Journal of Asian Finance, Economics, and Business, 5(2), 25-33. https://doi.org/10.13106/jafeb.2018.vol5.no2.25
Rahmi, M., Azma, N., Muttaqin, A. A., Jazil, T., & Rahman, M. (2016). Risk Volatility Measurement: Evidence from Indonesian Stock Market. The Journal of Asian Finance, Economics and Business, 3(3), 57–65. https://doi.org/10.13106/jafeb.2016.vol3.no3.57.
Rathnayaka, R. K. T., & Seneviratna, D. M. K. N. (2018). Taylor series approximation and unbiased GM (1, 1) based hybrid statistical approach for forecasting daily gold price demands. Grey Systems: Theory and Application, 9(1), 5–18. https://doi.org/10.1108/gs-08-2018-0032
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