Aielli, G., 2013. Dynamic conditional correlations: on properties and estimation. Journal of Business & Economic Statistics 31 (3), 282–299.
Andersen, T., Bollerslev, T., Diebold, F., Labys, P., 2003. Modeling and forecasting realized volatility. Econometrica 71, 579–625.
Andersen, T., Bollerslev, T., Frederiksen, P., Nielsen, M., 2010. Continuous-time models, realized volatilities and testable distributional implications for daily stock returns. Journal of Applied Econometrics 25, 233–261.
Barndor↵-Nielsen, O. E., Hansen, P. R., Lunde, A., Shephard, N., 2009. Realized kernels in practice: Trades and quotes. The Econometrics Journal 12 (3), C1–C32.
Barndor↵-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.
Barndor↵-Nielsen, O., Shephard, N., 2001. Normal modified stable processes. Theory of Probability and Mathematics Statistics 65, 1–19.
Bauer, G., Vorkink, K., 2011. Forecasting multivariate realized stock market volatility. Journal of Econometrics 160, 93–101.
- Bauwens, L., Grigoryeva, L., Ortega, J.-P., 2015b. Non-scalar garch models: Composite likelihood estimation and empirical comparisons.
Paper not yet in RePEc: Add citation now
Bauwens, L., Hafner, C. M., Pierret, D., 2013. Multivariate volatility modeling of electricity futures. Journal of Applied Econometrics 28 (5), 743–761.
Bauwens, L., Storti, G., 2013. Computationally efficient inference procedures for vast dimensional realized covariance models, . in Complex Models and Computational Methods in Statistics. Springer, pp. 37–49, m. Grigoletto, F. Lisi and S. Petrone Eds.
Bollerslev, T., Patton, A. J., Quaedvlieg, R., 2016a. Exploiting the errors: A simple approach for improved volatility forecasting.
Bonato, M., Caporin, M., Ranaldo, A., 2009. Forecasting realized (co)variances with a block structure Wishart autoregressive model, working Papers 2009-03, Swiss National Bank.
Bonato, M., Caporin, M., Ranaldo, A., 2012. A forecast-based comparison of restricted Wishart autoregressive models for realized covariance matrices. The European Journal of Finance 18 (9), 761–774.
Boudt, K., Danielsson, J., Laurent, S., 2013. Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting 29 (2), 244–257.
Boudt, K., Laurent, S., Lunde, A., Quaedvlieg, R., 2014. Positive semidefinite integrated covariance estimation, factorizations and asynchronicity. Tech. rep.
Chiriac, R., Voev, V., 2011. Modelling and forecasting multivariate realized volatility. Journal of Applied Econometrics 26, 922–947.
Colacito, R., Engle, R. F., Ghysels, E., 2011. A component model for dynamic correlations. Journal of Econometrics 164 (1), 45–59.
Corsi, F., 2009. A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics 7, 174–196.
- Diebold, F. X., Mariano, R. S., 2012. Comparing predictive accuracy. Journal of Business & economic statistics.
Paper not yet in RePEc: Add citation now
Econometrica 55 (3), 703–708. Noureldin, D., Shephard, N., Sheppard, K., 2012. Multivariate high-frequency-based volatility (HEAVY) models. Journal of Applied Econometrics 27 (6), 907–933.
Engle, R. F., 2002b. New frontiers for ARCH models. Journal of Applied Econometrics 17, 425–446.
Engle, R. F., Shephard, N., Sheppard, K., 2008a. Fitting vast dimensional time-varying covariance models.
Engle, R. F., Shephard, N., Sheppard, K., 2008b. Fitting vast dimensional time-varying covariance models.
- Engle, R., 2002a. Dynamic conditional correlation - a simple class of multivariate GARCH models. Journal of Business and Economic Statistics 20, 339–350.
Paper not yet in RePEc: Add citation now
Engle, R., Gallo, G., 2006. A multiple indicators model for volatility using intra-daily data. Journal of Econometrics 131, 3–27.
Engle, R., Kelly, B., 2012. Dynamic equicorrelation. Journal of Business and Economic Statistics 30 (2), 212–228.
Engle, R., Lee, G., 1999. A Permanent and Transitory Component Model of Stock Return Volatility, . Cointegration, Causality, and Forecasting: A Festschrift in Honor of Clive W.J. Granger. Oxford University Press - R. Engle and H. White eds., pp. 475–497.
Engle, R., Rangel, J., 2008. The spline-GARCH model for low-frequency volatility and its global macroeconomic causes. Review of Financial Studies 21, 1187–1222.
Engle, R., Russell, J., 1998. Autoregressive conditional duration: A new model for irregularly spaced transaction data. Econometrica 66, 1127–1162.
Engle, R., Sheppard, K., 2001. Theorical and empirical properties of dynamic conditional correlation multivariate garch, mimeo, UCSD.
Fan, J., Li, Y., Yu, K., 2012. Vast volatility matrix estimation using high-frequency data for portfolio selection. Journal of the American Statistical Association 107 (497), 412–428.
Francq, C., HorvaÌth, L., Zakoı̈an, J.-M., 2014. Variance targeting estimation of multivariate GARCH models. Journal of Financial Econometrics 14 (2), 353–382.
Francq, C., Horvath, L., Zakoı̈an, J.-M., 2011. Merits and drawbacks of variance targeting in GARCH models. Journal of Financial Econometrics 4, 619–656.
Golosnoy, V., Gribisch, B., Liesenfeld, R., 2012. The conditional autoregressive Wishart model for multivariate stock market volatility.
Hafner, C., Linton, O., 2010. Efficient estimation of a multivariate volatility model. Journal of Econometrics 159 (1), 55–73.
Hansen, P. R., Huang, Z., Shek, H. H., 2012. Realized garch: a joint model for returns and realized measures of volatility. Journal of Applied Econometrics 27 (6), 877–906.
Jin, X., Maheu, J., 2013. Modelling realized covariances and returns. Journal of Financial Econometrics 11 (2), 335–369, wP 11-08, The Rimini Center for Economic Analysis.
Journal of Econometrics 192 (1), 1–18. Bollerslev, T., Patton, A. J., Quaedvlieg, R., 2016b. Modeling and forecasting (un) reliable realized covariances for more reliable financial decisions. Available at SSRN 2759388.
Journal of Eonometrics 167, 211–223. GourieÌroux, C., Jasiak, J., Sufana, R., 2009. The Wishart autoregressive process of multivariate stochastic volatility. Journal of Econometrics 150, 167–181.
Ledoit, O., Santa-Clara, P., Wolf, M., 2003. Flexible multivariate garch modeling with an application to international stock markets.
- Lutkepohl, H., 1996. Handbook of matrices. Wiley.
Paper not yet in RePEc: Add citation now
Newey, W. K., West, K. D., 1987. A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix.
Noureldin, D., Shephard, N., Sheppard, K., 2014. Multivariate rotated ARCH models. Journal of Econometrics 179 (1), 16–30.
Pakel, C., Shephard, N., Sheppard, K., Engle, R. F., 2014. Fitting vast dimensional time-varying covariance models. Tech. rep., Working Paper.
Patton, A. J., 2011. Volatility forecast comparison using imperfect volatility proxies. Journal of Econometrics 160 (1), 246–256.
- Patton, A. J., Sheppard, K., 2009. Evaluating volatility and correlation forecasts. In: Handbook of financial time series. Springer, pp.
Paper not yet in RePEc: Add citation now
Pedersen, R. S., Rahbek, A., 2014. Multivariate variance targeting in the BEKK–GARCH model. The Econometrics Journal 17 (1), 24–55.
Shephard, N., Sheppard, K. K., 2010. Realising the future: forecasting with high-frequency-based volatility (HEAVY) models. Journal of Applied Econometrics 25, 197–231.
Sheppard, K., Xu, W., 2014. Factor high-frequency based volatility (heavy) models, available at SSRN: http://ssrn.com/abstract=2442230 or http://dx.doi.org/10.2139/ssrn.2442230.
- The Review of Economics and Statistics 85, 735–747. Lindsay, B. G., 1988. Composite likelihood methods. Contemporary mathematics 80 (1), 221–39.
Paper not yet in RePEc: Add citation now
- Tse, Y., Tsui, A., 2002. A multivariate GARCH model with time-varying correlations. Journal of Business and Economic Statistics 20, 351–362.
Paper not yet in RePEc: Add citation now
URL http://dx.doi.org/10.1002/jae.1234 Hansen, P. R., Lunde, A., Nason, J. M., 2011. The model confidence set. Econometrica 79 (2), 453–497.