268
Views
0
CrossRef citations to date
0
Altmetric
Research Papers

High-dimensional realized covariance estimation: a parametric approach

&
Pages 2093-2107 | Received 12 Jun 2020, Accepted 26 Jul 2022, Published online: 31 Aug 2022

References

  • Ait-Sahalia, Y. and Xiu, D., Using principal component analysis to estimate a high dimensional factor model with high-frequency data. J. Econom., 2017, 201(2), 384–399.
  • Ait-Sahalia, Y. and Xiu, D., Principal component analysis of high-frequency data. J. Am. Stat. Assoc., 2019, 114(525), 287–303.
  • Aït-Sahalia, Y., Fan, J. and Xiu, D., High-frequency covariance estimates with noisy and asynchronous financial data. J. Am. Stat. Assoc., 2010, 105(492), 1504–1517.
  • Allez, R. and Bouchaud, J.-P., Individual and collective stock dynamics: Intra-day seasonalities. New J. Phys., 2011, 13(2), 025010.
  • Bańbura, M. and Modugno, M., Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. J. Appl. Econom., 2014, 29(1), 133–160.
  • Barndorff-Nielsen, O.E. and Shephard, N., Econometric analysis of realized covariation: High frequency based covariance, regression, and correlation in financial economics. Econometrica, 2004, 72(3), 885–925.
  • Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N., Realized kernels in practice: Trades and quotes. Econom. J., 2009, 12(3), C1–C32.
  • Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N., Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading. J. Econom., 2011, 162(2), 149–169.
  • Bibinger, M., Hautsch, N., Malec, P. and Reiss, M., Estimating the spot covariation of asset prices-statistical theory and empirical evidence. J. Bus. Econ. Stat., 2019, 37(3), 419–435.
  • Bollerslev, T., Patton, A.J. and Quaedvlieg, R., Exploiting the errors: A simple approach for improved volatility forecasting. J. Econom., 2016, 192(1), 1–18.
  • Buccheri, G., Bormetti, G., Corsi, F. and Lillo, F., A score-driven conditional correlation model for noisy and asynchronous data: An application to high-frequency covariance dynamics. J. Bus. Econ. Stat., 2021, 39(4), 920–936.
  • Buccheri, G., Livieri, G., Pirino, D. and Pollastri, A., A closed-form formula characterization of the epps effect. Quant. Finance, 2020, 20(2), 243–254.
  • Christensen, K., Kinnebrock, S. and Podolskij, M., Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data. J. Econom., 2010, 159(1), 116–133.
  • Corsi, F., Peluso, S. and Audrino, F., Missing in asynchronicity: A kalman-em approach for multivariate realized covariance estimation. J. Appl. Econom., 2015, 30(3), 377–397.
  • Dai, C., Lu, K. and Xiu, D., Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data. J. Econom., 2019, 208(1), 43–79. Special issue on financial engineering and risk management.
  • Dempster, A.P., Laird, N.M. and Rubin, D.B., Maximum likelihood from incomplete data via the em algorithm. J. R. Stat. Soc. Ser. B (Methodol.), 1977, 39(1), 1–38.
  • Durbin, J. and Koopman, S., Time Series Analysis by State Space Methods: Second Edition, Oxford Statistical Science Series, 2012 (OUP: Oxford).
  • Engle, R. and Colacito, R., Testing and valuing dynamic correlations for asset allocation. J. Bus. Econ. Stat., 2006, 24, 238–253.
  • Fleming, J., Kirby, C. and Ostdiek, B., The economic value of volatility timing. J. Finance, 2001, 56(1), 329–352.
  • Fleming, J., Kirby, C. and Ostdiek, B., The economic value of volatility timing using “realized” volatility. J. Financ. Econ., 2003, 67(3), 473–509.
  • Hansen, P.R. and Lunde, A., Realized variance and market microstructure noise. J. Bus. Econ. Stat., 2006, 24(2), 127–161.
  • Hansen, P.R., Lunde, A. and Nason, J.M., The model confidence set. Econometrica, 2011, 79(2), 453–497.
  • Harvey, A., Forecasting, Structural Time Series Models and the Kalman Filter, 1990 (Cambridge University Press: Cambridge).
  • Hautsch, N., Kyj, L.M. and Oomen, R.C.A., A blocking and regularization approach to high-dimensional realized covariance estimation. J. Appl. Econom., 2012, 27(4), 625–645.
  • Hayashi, T. and Yoshida, N., On covariance estimation of non-synchronously observed diffusion processes. Bernoulli, April, 2005, 11(2), 359–379.
  • Jagannathan, R. and Ma, T., Risk reduction in large portfolios: Why imposing the wrong constraints helps. J. Finance, 2003, 58(4), 1651–1683.
  • Jungbacker, B. and Koopman, S.J., Likelihood-based dynamic factor analysis for measurement and forecasting. Econom. J., 2015, 18(2), C1–C21.
  • Koopman, S.J., Lit, R., Lucas, A. and Opschoor, A., Dynamic discrete copula models for high-frequency stock price changes. J. Appl. Econom., 2018, 33(7), 966–985.
  • Kupiec, P., Techniques for verifying the accuracy of risk measurement models. J. Deriv., 1995, 3(2), 73–84.
  • Munnix, M.C., Schafer, R. and Guhr, T., Statistical causes for the epps effect in microstructure noise. Int. J. Theor. Appl. Finance, 2011, 14(8), 1231–1246.
  • Oh, D.H. and Patton, A.J., Time-varying systemic risk: Evidence from a dynamic copula model of CDS spreads. J. Bus. Econ. Stat., 2018, 36(2), 181–195.
  • Patton, A.J. and Sheppard, K., Evaluating Volatility and Correlation Forecasts, pp. 801–838, 2009 (Springer Berlin Heidelberg: Berlin, Heidelberg).
  • Pelger, M., Large-dimensional factor modeling based on high-frequency observations. J. Econom., 2019, 208(1), 23–42.
  • Peluso, S., Corsi, F. and Mira, A., A Bayesian high-frequency estimator of the multivariate covariance of noisy and asynchronous returns. J. Financ. Econom., July, 2014, 13(3), 665–697.
  • Proietti, T., Estimation of common factors under cross-sectional and temporal aggregation constraints: Nowcasting monthly gdp and its main components. In COMPSTAT 2008, edited by P. Brito, pp. 547–558, 2008 (Physica-Verlag HD: Heidelberg).
  • Renò, R., A closer look at the epps effect. Int. J. Theor. Appl. Finance, 2003, 06(1), 87–102.
  • Shephard, N. and Xiu, D., Econometric analysis of multivariate realised QML: Estimation of the covariation of equity prices under asynchronous trading. J. Econom., 2017, 201(1), 19–42.
  • Simon, D., Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches, 2006 (Wiley-Interscience: USA).
  • Tola, V., Lillo, F., Gallegati, M. and Mantegna, R.N., Cluster analysis for portfolio optimization. J. Econ. Dyn. Control, 2008, 32(1), 235–258.
  • White, H., Maximum likelihood estimation of misspecified models. Econometrica, 1982, 50(1), 1–25.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.