Aı̈t-Sahalia, Y., Fan, J., Xiu, D., 2010. High-frequency covariance estimates with noisy and asynchronous financial data. J. Amer. Statist. Assoc. 105 (492), 1504–1517.
Aı̈t-Sahalia, Y., Mykland, P. A., Zhang, L., 2011. Ultra high frequency volatility estimation with dependent microstructure noise. Journal of Econometrics 160 (1), 160 – 175.
- Abadir, K., Magnus, J., 2005. Matrix Algebra. Econometric Exercises. Cambridge University Press.
Paper not yet in RePEc: Add citation now
Allez, R., Bouchaud, J.-P., 2011. Individual and collective stock dynamics: intra-day seasonalities. New Journal of Physics 13 (2), 025010.
Barndorff-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.
Barndorff-Nielsen, O. E., Hansen, P. R., Lunde, A., Shephard, N., 2011. Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading. Journal of Econometrics 162 (2), 149 – 169.
Bibinger, M., 2012. An estimator for the quadratic covariation of asynchronously observed itô processes with noise: Asymptotic distribution theory. Stochastic Processes and their Applications 122 (6), 2411 – 2453.
Bibinger, M., Hautsch, N., Malec, P., Reiss, M., 2014. Estimating the spot covariation of asset prices: Statistical theory and empirical evidence. CFS Working Paper Series 477, Center for Financial Studies (CFS).
Blasques, F., Koopman, S. J., Lucas, A., 2015. Information-theoretic optimality of observation-driven time series models for continuous responses. Biometrika 102 (2), 325.
Bollerslev, T., April 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31 (3), 307–327.
Corsi, F., Peluso, S., Audrino, F., 2015. Missing in asynchronicity: A kalman-em approach for multivariate realized covariance estimation. Journal of Applied Econometrics 30 (3), 377–397.
Corsi, F., Pirino, D., Renò, R., 2010. Threshold bipower variation and the impact of jumps on volatility forecasting. Journal of Econometrics 159 (2), 276 – 288.
- Cox, D., 1981. Statistical analysis of time series: Some recent developments [with discussion and reply]. Scandinavian Journal of Statistics 8 (2), 93–115.
Paper not yet in RePEc: Add citation now
Creal, D., Koopman, S. J., Lucas, A., 2008. A general framework for observation driven time-varying parameter models. SSRN Electronic Journal.
Creal, D., Koopman, S. J., Lucas, A., 2011. A dynamic multivariate heavy-tailed model for time-varying volatilities and correlations. Journal of Business & Economic Statistics 29 (4), 552–563.
Creal, D., Koopman, S. J., Lucas, A., 2013. Generalized autoregressive score models with applications. Journal of Applied Econometrics 28 (5), 777–795.
Delle Monache, D., Petrella, I., Venditti, F., 2016. Adaptive state space models with applications to the business cycle and financial stress. CEPR Discussion Paper (DP11599).
Delle Monache, D., Petrella, I., Venditti, F., Jul. 2015. Common faith or parting ways? A time varying parameters factor analysis of euro-area inflation. Birkbeck Working Papers in Economics and Finance 1515, Birkbeck, Department of Economics, Mathematics & Statistics.
Durbin, J., Koopman, S., 2012. Time Series Analysis by State Space Methods: Second Edition. Oxford Statistical Science Series. OUP Oxford.
Engle, R., 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation. Econometrica 50 (4), 987–1007.
Engle, R., 2002. Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics 20 (3), 339–50.
Engle, R., Colacito, R., 2006. Testing and valuing dynamic correlations for asset allocation. Journal of Business & Economic Statistics 24, 238–253.
- Epps, T. W., 1979. Comovements in stock prices in the very short run. Journal of the American Statistical Association 74 (366), 291–298.
Paper not yet in RePEc: Add citation now
Hansen, P. R., Lunde, A., 2006. Realized variance and market microstructure noise. Journal of Business & Economic Statistics 24 (2), 127–161.
Harvey, A. C., 2013. Dynamic Models for Volatility and Heavy Tails. No. 9781107034723 in Cambridge Books. Cambridge University Press.
Harvey, A., 1991. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
- Hayashi, T., Yoshida, N., 04 2005. On covariance estimation of non-synchronously observed diffusion processes. Bernoulli 11 (2), 359–379.
Paper not yet in RePEc: Add citation now
Jacod, J., Li, Y., Mykland, P. A., Podolskij, M., Vetter, M., 2009. Microstructure noise in the continuous case: The pre-averaging approach. Stochastic Processes and their Applications 119 (7), 2249 – 2276.
- Jaeckel, P., Rebonato, R., 1999. The most general methodology for creating a valid correlation matrix for risk management and option pricing purposes. Journal of risk 2 (2).
Paper not yet in RePEc: Add citation now
Koopman, S. J., Lit, R., Lucas, A., Mar. 2015. Intraday Stock Price Dependence using Dynamic Discrete Copula Distributions. Tinbergen Institute Discussion Papers 15037 /III/DSF90, Tinbergen Institute.
Koopman, S. J., Lucas, A., Scharth, M., March 2016. Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models. The Review of Economics and Statistics 98 (1), 97–110.
- Patton, A. J., Sheppard, K., 2009. Evaluating Volatility and Correlation Forecasts. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 801–838.
Paper not yet in RePEc: Add citation now
- Rapisarda, F., Brigo, D., Mercurio, F., 2007. Parameterizing correlations: a geometric interpretation. IMA Journal of Management Mathematics 18 (1), 55–73.
Paper not yet in RePEc: Add citation now
Shephard, N., Xiu, D., 2017. Econometric analysis of multivariate realised qml: Estimation of the covariation of equity prices under asynchronous trading. Journal of Econometrics 201 (1), 19 – 42.
Shumway, R. H., Stoffer, D. S., 1982. An approach to time series smoothing and forecasting using the em algorithm. Journal of Time Series Analysis 3 (4), 253–264.
- Simon, D., 2006. Kalman filter generalizations. John Wiley & Sons, Inc., pp. 183–227.
Paper not yet in RePEc: Add citation now
Zu, Y., Boswijk, H. P., 2014. Estimating spot volatility with high-frequency financial data. Journal of Econometrics 181 (2), 117 – 135.