References
- Aït-Sahalia, Y., Kalnina, I., Xiu, D. (2020). High-frequency factor models and regressions. Journal of Econometrics 216(1):86–105. doi:https://doi.org/10.1016/j.jeconom.2020.01.007
- Andersen, T. G., Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review 39(4):885–905. doi:https://doi.org/10.2307/2527343
- Andersen, T. G., Bollerslev, T., Christoffersen, P. F., Diebold, F. X. (2006). Volatility and correlation forecasting. Handbook of Economic forecasting 1:777–878.
- Andreou, E., Ghysels, E. (2002). Rolling-sample volatility estimators: Some new theoretical, simulation, and empirical results. Journal of Business & Economic Statistics 20(3):363–376. doi:https://doi.org/10.1198/073500102288618504
- Bandi, F. M., Russell, J. R. (2005). Realized covariation, realized beta and microstructure noise. Unpublished paper., Graduate School of Business, University of Chicago,
- Bannouh, K., Martens, M., Oomen, R. C., van Dijk, D. J. (2012). Realized mixed-frequency factor models for vast dimensional covariance estimation. ERIM Report Series Reference No. ERS-2012-017-F&A, 2012.
- Barndorff-Nielsen, O. E., Shephard, N. (2004). Econometric analysis of realized covariation: High frequency based covariance, regression, and correlation in financial economics. Econometrica 72(3):885–925. doi:https://doi.org/10.1111/j.1468-0262.2004.00515.x
- Barndorff-Nielsen, O. E., Shephard, N. (2005). Econometrics of testing for jumps in financial economics using bipower variation. Journal of Financial Econometrics 4(1):1–30. doi:https://doi.org/10.1093/jjfinec/nbi022
- Bauwens, L., Laurent, S., Rombouts, J. V. (2006). Multivariate garch models: a survey. Journal of Applied Econometrics21(1):79–109. doi:https://doi.org/10.1002/jae.842
- Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31(3):307–327. doi:https://doi.org/10.1016/0304-4076(86)90063-1
- Bollerslev, T. (1990). Modelling the coherence in short-run nominal exchange rates: a multivariate generalized arch model. The Review of Economics and Statistics 72(3):498–505. pages doi:https://doi.org/10.2307/2109358
- Corsi, F. (2008). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics 7(2):174–196. doi:https://doi.org/10.1093/jjfinec/nbp001
- Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. EconometricaSociety50(4):987–1007. pages doi:https://doi.org/10.2307/1912773
- Engle, R. F., Ledoit, O., Wolf, M. (2019). Large dynamic covariance matrices. Journal of Business & Economic Statistics 37(2):313–363. doi:https://doi.org/10.1080/07350015.2017.1345683
- Fan, J., Fan, Y., Lv, J. (2008). High dimensional covariance matrix estimation using a factor model. Journal of Econometrics 147(1):186–197. doi:https://doi.org/10.1016/j.jeconom.2008.09.017
- Foster, D. P., Nelson, D. B. (1996). Continuous record asymptotics for rolling sample variance estimators. Econometrica 64(1):139–174. doi:https://doi.org/10.2307/2171927
- Halbleib, R., Voev, V. (2016). Forecasting covariance matrices: a mixed approach. Journal of Financial Econometrics 14(2):383–417. doi:https://doi.org/10.1093/jjfinec/nbu031
- Haugen, R. A., Baker, N. L. (1991). The efficient market inefficiency of capitalization–weighted stock portfolios. The Journal of Portfolio Management 17(3):35–40. doi:https://doi.org/10.3905/jpm.1991.409335
- Hayashi, T., Yoshida, N. (2005). On covariance estimation of non-synchronously observed diffusion processes. Bernoulli 11(2):359–379. doi:https://doi.org/10.3150/bj/1116340299
- Hu, Y.-P., Tsay, R. S. (2014). Principal volatility component analysis. Journal of Business & Economic Statistics 32(2):153–164. doi:https://doi.org/10.1080/07350015.2013.818006
- Jacod, J., Protter, P. (2011). Discretization of Processes, Vol. 67. New York: Springer-Verlag.
- Jagannathan, R., Ma, T. (2003). Risk reduction in large portfolios: Why imposing the wrong constraints helps. The Journal of Finance 58(4):1651–1683. doi:https://doi.org/10.1111/1540-6261.00580
- Jing, B. Y., Liu, Z., Kong, X. B. (2014). On the estimation of integrated volatility with jumps and microstructure noise. Journal of Business & Economic Statistics 32(3):457–467. doi:https://doi.org/10.1080/07350015.2014.906350
- Kelly, B. T., Pruitt, S., Su, Y. (2019). Characteristics are covariances: a unified model of risk and return. Journal of Financial Economics 134(3):501–524. URL https://EconPapers.repec.org/RePEc:eee:jfinec:v:134:y:2019:i:3:p:501-524. doi:https://doi.org/10.1016/j.jfineco.2019.05.001
- Lanne, M., Saikkonen, P. (2007). A multivariate generalized orthogonal factor GARCH model. Journal of Business & Economic Statistics 25(1):61–75. doi:https://doi.org/10.1198/073500106000000404
- Mcaleer, M., Medeiros, M. C. (2008). Realized volatility: a review. Econometric Reviews 27(1-3):10–45. doi:https://doi.org/10.1080/07474930701853509
- Nielsen, F., Aylursubramanian, R. (2008). Far from the madding crowd volatility efficient indices. Research Insights, MSCI Barra: 1–14.
- Pelger, M. (2019). Large-dimensional factor modeling based on high-frequency observations. Journal of Econometrics 208(1):23–42. doi:https://doi.org/10.1016/j.jeconom.2018.09.004
- Rasch, D. (1989). Magnus, jan r.: Linear structures. monograph no. 42. (series editor: A. stuart: Gri_n's statistical monographs and courses). charles gri_n & co. ltd., london 1988, 205 pp. Biometrical Journal 31(6):726–726.
- Tao, M., Wang, Y., Yao, Q., Zou, J. (2011). Large volatility matrix inference via combining low-frequency and high-frequency approaches. Journal of the American Statistical Association 106(495):1025–1040. doi:https://doi.org/10.1198/jasa.2011.tm10276
- Tse, Y. K., Tsui, A. K. C. (2002). A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business & Economic Statistics 20(3):351–362. doi:https://doi.org/10.1198/073500102288618496
- Wang, H., Pan, J. (2018). A scalar dynamic conditional correlation model: Structure and estimation. Science China Mathematics 61(10):1881–1906. doi:https://doi.org/10.1007/s11425-017-9273-x
- Zhang, L. (2011). Estimating covariation: Epps effect, microstructure noise. Journal of Econometrics 160(1):33–47. doi:https://doi.org/10.1016/j.jeconom.2010.03.012