Open Access
Issue |
E3S Web Conf.
Volume 253, 2021
2021 International Conference on Environmental and Engineering Management (EEM 2021)
|
|
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Article Number | 02023 | |
Number of page(s) | 9 | |
Section | Big Data Environment Management Application and Industry Research | |
DOI | https://doi.org/10.1051/e3sconf/202125302023 | |
Published online | 06 May 2021 |
- Christoffersen, P., 2014. The economic value of realized volatility: using high-frequency returns for option valuation. J. Financ. Quant. Anal. 49. [Google Scholar]
- Clements, M. P., Galvao, A. B., Kim, J. H., 2008. Quantile forecasts of daily exchange rate returns from forecasts of realized volatility. J. Empir. Financ. 15, 729–750. [Google Scholar]
- Corsi, F., Fusari, N., Vecchia, D. L., 2013. Realizing smiles: options pricing with realized volatility. J. Financ. Econ. 107, 284–304. [Google Scholar]
- Maheu, J. M., Mccurdy, T. H., 2011. Do high-frequency measures of volatility improve forecasts of return distributions? Ssrn Electron. J. 160, 69–76. [Google Scholar]
- Seo, S. W., Kim, J. S., 2015. The information content of option-implied information for volatility forecasting with investor sentiment. J. Bank. Financ. 50, 106–120. [Google Scholar]
- Wen, F., Xiao, J., Huang, C., Xia, X., 2018. Interaction between oil and US dollar exchange rate: nonlinear causality, time-varying influence and structural breaks in volatility. Appl. Energy 50, 319–334. [Google Scholar]
- Noh, J. and Kim, T. H., 2006. Forecasting volatility of futures market: the S&P 500 and FTSE 100 futures using high frequency returns and implied volatility. Applied Economics, 38(4), pp. 395–413. [Google Scholar]
- Huang, J., Tan, N., & Zhong, M. (2014). Incorporating Overconfidence into Real Option Decision-Making Model of Metal Mineral Resources Mining Project 2014. DOI: 10.1155/2014/232516. [Google Scholar]
- Li, W., Cheng, Y. and Fang, Q., 2020. Forecast on silver futures linked with structural breaks and day-of-the-week effect. The North American Journal of Economics and Finance, p. 101–192. [Google Scholar]
- Wei, Y., Liang, C., Li, Y., Zhang, X. and Wei, G., 2020. Can CBOE gold and silver implied volatility help to forecast gold futures volatility in China? Evidence based on HAR and Ridge regression models. Finance Research Letters, 35, p. 101–287. [Google Scholar]
- Luo, X. and Ye, Z., 2015. Predicting volatility of the Shanghai silver futures market: What is the role of the US options market?. Finance Research Letters, 15, pp. 68–77. [Google Scholar]
- Yang, C., Gong, X. and Zhang, H., 2019. Volatility forecasting of crude oil futures: The role of investor sentiment and leverage effect. Resources Policy, 61, pp. 548–563. [Google Scholar]
- Corsi, F., 2009. A simple approximate long-memory model of realized volatility. J. Financ. Econometr. 7 (2), 174–196. [Google Scholar]
- Andersen, T. G., Bollerslev, T., Diebold, F. X., Labys, P., 2002. Modeling and forecasting realized volatility. Econometrica 71, 579–625. [Google Scholar]
- Beer, F. and Zouaoui, M., 2013. Measuring stock market investor sentiment. Journal of Applied Business Research (JABR), 29(1), pp. 51–68. [Google Scholar]
- Al Shalabi, L. and Shaaban, Z., 2006, May. Normalization as a preprocessing engine for data mining and the approach of preference matrix. In 2006 International conference on dependability of computer systems (pp. 207–214). IEEE. [Google Scholar]
- Zhu, Y., Tian, D. and Yan, F., 2020. Effectiveness of Entropy Weight Method in Decision-Making. Mathematical Problems in Engineering, 2020. [Google Scholar]
- Andersen, T. G., Bollerslev, T., 1998. Answering the skeptics: yes, standard volatility models do provide accurate forecasts. Int. Econ. Rev. 39 (4), 885–905. [Google Scholar]
- Qiao, G., Teng, Y., Li, W. and Liu, W., 2019. Improving volatility forecasting based on Chinese volatility index information: Evidence from CSI 300 index and futures markets. The North American Journal of Economics and Finance, 49, pp. 133–151. [Google Scholar]
- Hawkins, D. M., 2004. The problem of overfitting. Journal of chemical information and computer sciences, 44(1), pp. 1–12. [CrossRef] [PubMed] [Google Scholar]
- Exterkate, P., Groenen, P. J., Heij, C., van Dijk, D., 2016. Nonlinear forecasting with many predictors using kernel ridge regression. Int. J. Forecast. 32 (3), 736–753. [Google Scholar]
- [22]. Hoerl, A. E., Kennard, R. W., 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12 (1), 55–67. [CrossRef] [Google Scholar]
- [23]. Campbell, J. Y., Thompson, S. B., 2008. Predicting excess stock returns out of sample: can anything beat the historical average? Rev. Financial Stud. 21 (4), 1509–1531. [Google Scholar]
- [24]. Clark, T. E., West, K. D., 2007. Approximately normal tests for equal predictive accuracy in nested models. J. Econometr. 138 (1), 291–311. [Google Scholar]
- [25]. Rossi, B. and Inoue, A., 2012. Out-of-sample forecast tests robust to the choice of window size. Journal of Business & Economic Statistics, 30(3), pp. 432–453. [Google Scholar]
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