1 |
Meng, X., & Taylor, J. W. (2020). Estimating value-at-risk and expected shortfall using the intraday low and range data. European Journal of Operational Research, 280(1), 191-202. https://doi.org/10.1016/j.ejor.2019.07.011
DOI
|
2 |
Mutia, E., Rahmawaty, R., & Afrianandra, C. (2018). Value at risk of Sukuk Ijarah and Mudharabah in Indonesia. Journal of Accounting Research, Organization and Economics, 1(1), 65-73. https://doi.org/10.24815/jaroe.v1i1.10751
DOI
|
3 |
Otoritas Jasa Keuangan. (2020). Indonesian banking statistics. https://www.ojk.go.id/id/kanal/perbankan/data-dan-statistik/statistik-perbankan-indonesia/Documents/Pages/Statistik-Perbankan-Indonesia---Desember-2020/Statistik%20Perbankan%20Indonesia%20Des%202020.pdf
|
4 |
Rahman, M. M., Chowdhury, A. A., & Moudud-Ul-Huq, S. (2020). How do the banks determine regulatory capital, risk, and cost inefficiency in Bangladesh? Journal of Asian Finance, Economics, and Business, 7(12), 211-222. https://doi.org/10.13106/jafeb.2020.vol7.no12.211
DOI
|
5 |
Sitorus, S. (2018). Investment decision-making based on the value at risk (VaR) analysis for stocks of state own bank in Indonesia. Journal of Economics and Business, 2(2), 128-141.
DOI
|
6 |
Sukono, F., Lesmana, E., Susanti, D., Napitupulu, H., & Hidayat, Y. (2019). Estimation of value-at-risk adjusted under the capital asset pricing model based on the ARMAX-GARCH approach. Jurnal Matematika Integratif, 15(1), 29. https://doi.org/10.24198/jmi.v15i1.20931
DOI
|
7 |
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), 1-25. https://doi.org/10.1186/s40854-020-00178-1
DOI
|
8 |
Vo, D. H., Pham, T. N., Pham, T. T. V., Truong, L. M., & Cong Nguyen, T. (2019). Risk, return, and portfolio optimization for various industries in the ASEAN region. Borsa Istanbul Review, 19(2), 132-138. https://doi.org/10.1016/j.bir.2018.09.003
DOI
|
9 |
Wong, H., & Li, W. K. (1995). Portmanteau test for conditional heteroscedasticity, using ranks of squared residuals. Journal of Applied Statistics, 22(1), 121-134. https://doi.org/10.1080/757584402
DOI
|
10 |
Akhmadi, Y., Mustofa, I., Rika, H. M., & Hanggraeni, D. (2019). Value at risk assessment uses the extreme value theory approach and generalized Pareto distribution case studies of state-owned banks in Indonesia in the period 2008-2018. Managament Insight: Jurnal Ilmiah Manajemen, 13(1), 63-72. https://doi.org/10.33369/insight.14.1.63-72
DOI
|
11 |
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987. https://doi.org/10.2307/1912773
DOI
|
12 |
Granger, C. W., & Joyeux, R. (1980). An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis, 1(1), 15-29. https://doi.org/10.1111/j.1467-9892.1980.tb00297.x
DOI
|
13 |
Hanoatubun, S. (2020). Effect of profitability, leverage, liquidity and company size on stock returns of pharmaceutical companies in Bei. Journal of Education, Psychology and Counseling, 2(1), 46-69. https://ojs.unud.ac.id/index.php/Manajemen/article/view/23852
|
14 |
Le, T. P. T. D., & Tran, H. L. M. (2021). The contagion effect from U.S. stock market to the Vietnamese and the Philippine stock markets: The evidence of DCC - GARCH model. Journal of Asian Finance, Economics. and Business, 8(2), 759-770. https://doi.org/10.13106/jafeb.2021.vol8.no2.0759
DOI
|
15 |
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
DOI
|
16 |
Tsay, R. S. (2010). Analysis of financial time series (3rd ed.). Hoboken, NJ: Wiley & Sons. https://doi.org/10.1002/9780470644560
|
17 |
Ambya, G. T., Hendrawaty, E., Kesumah, F. S. D., & Wisnu, F. K. (2020). Future natural gas price forecasting model and its policy implication. International Journal of Energy Economics and Policy, 10(5), 58-63. https://doi.org/10.32479/ijeep.9676
DOI
|
18 |
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307-327. https://doi.org/10.1109/TNN.2007.902962
DOI
|
19 |
Brockwell, P., & Davis, R. (2002). Introduction to time series and forecasting. Springer-Verlag.
|
20 |
Budiarti, R. (2019). Estimated portfolio value-at-risk for extreme data. E-Prosiding Nasional Seminar Nasional Statistika, 8(1), 111-129. http://prosiding.statistics.unpad.ac.id/index.php/prosiding/article/view/447
|
21 |
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427. https://doi.org/10.2307/2286348
DOI
|
22 |
Lee, J. H., & King, M. . (1993). A locally most mean powerful-based score test for ARCH and GARCH regression disturbances. Journal of Business and Economic Statistics, 11(1), 17-27. https://doi.org/10.1080/07350015.1993.10509930
DOI
|
23 |
Hendrawaty, E., Azhar, R., Kesumah, F. S. D., Sembiring, S. I. O., & Metalia, M. (2021). Modeling and forecasting crude oil prices during the Covid-19 pandemic. International Journal of Energy Economics and Policy, 11(2), 149-154. https://doi.org/10.32479/ijeep.10578
DOI
|
24 |
Hyndman, R., & Athanasopoulos, G. (2018). Forecasting: principles and practice (2nd ed.). Victoria, Australia: O'Texts Publishers.
|
25 |
Khan, K., Zhao, H., Zhang, H., Yang, H., Shah, M. H., & Jahanger, A. (2020). The impact of COVID-19 pandemic on stock markets: An empirical analysis of world major stock indices. Journal of Asian Finance, Economics, and Business, 7(7), 463-474. https://doi.org/10.13106/jafeb.2020.vol7.no7.463
DOI
|