Aı̈t-Sahalia, Y., Fan, J., Xiu, D., 2010. High-frequency covariance estimates with noisy and asynchronous financial data. Journal of the American Statistical Association 105 (492).
Andersen, T. G., Bollerslev, T., Christoffersen, P. F., Diebold, F. X., 2013. Financial risk measurement for financial risk management. In: Handbook of the Economics of Finance (eds. G.
Andersen, T. G., Bollerslev, T., Diebold, F. X., Labys, P., 2003. Modeling and forecasting realized volatility. Econometrica 71 (2), 579–625.
- Andersen, T. G., Bollerslev, T., Diebold, F. X., Wu, G., 2006. Realized beta: Persistence and predictability. Advances in Econometrics 20, 1–39.
Paper not yet in RePEc: Add citation now
Andersen, T. G., Bollerslev, T., Huang, X., 2011. A reduced form framework for modeling volatility of speculative prices based realized variation measures. Journal of Econometrics 160 (2), 176–189.
Andersen, T. G., Bollerslev, T., Meddahi, N., 2004. Analytical evaluation of volatility forecasts.
Andersen, T. G., Dobrev, D., Schaumburg, E., 2012. Jump-robust volatility estimation using nearest neighbor truncation. Journal of Econometrics 169 (1), 75–93.
- Anderson, E. W., Cheng, A.-R. M., 2016. Robust Bayesian portfolio choice. Review of Financial Studies (forthcoming).
Paper not yet in RePEc: Add citation now
- Baker, M., Bradley, B., Wurgler, J., 2011. Benchmarks as limits to arbitrage: Understanding the low-volatility anomaly. Financial Analysts Journal 67 (1), 40–54.
Paper not yet in RePEc: Add citation now
Bandi, F. M., Russell, J. R., Zhu, Y., 2008. Using high-frequency data in dynamic portfolio choice. Econometric Reviews 27 (1-3), 163–198.
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.
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.
Bollerslev, T., 1990. Modelling the coherence in short-run nominal exchange rates: A multivariate Generalized ARCH model. Review of Economics and Statistics 72 (3), 498–505.
Bollerslev, T., Patton, A. J., Quaedvlieg, R., 2016. Exploiting the errors: A simple approach for improved volatility forecasting. Journal of Econometrics 192 (1), 1–18.
- Brodie, J., Daubechies, I., De Mol, C., Giannone, D., Loris, I., 2009. Sparse and stable markowitz portfolios. Proceedings of the National Academy of Sciences 106 (30), 12267–12272.
Paper not yet in RePEc: Add citation now
Brown, D. B., Smith, J. E., 2011. Dynamic portfolio optimization with transaction costs: Heuristics and dual bounds. Management Science 57 (10), 1752–1770.
Chan, L. K., Karceski, J., Lakonishok, J., 1999. On portfolio optimization: Forecasting covariances and choosing the risk model. Review of Financial Studies 12 (5), 937–974.
- Chiriac, R., Voev, V., 2010. Modelling and forecasting multivariate realized volatility. Journal of Applied Econometrics 26 (6), 922–947.
Paper not yet in RePEc: Add citation now
Christensen, K., Kinnebrock, S., Podolskij, M., 2010. Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data. Journal of Econometrics 159 (1), 116–133.
Corsi, F., 2009. A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics 7 (2), 174–196.
De Lira Salvatierra, I., Patton, A. J., 2015. Dynamic copula models and high frequency data. Journal of Empirical Finance 30, 120–135.
DeMiguel, V., Garlappi, L., Nogales, F. J., Uppal, R., 2009a. A generalized approach to portfolio optimization: Improving performance by constraining portfolio norms. Management Science 55 (5), 798–812.
DeMiguel, V., Garlappi, L., Uppal, R., 2009b. Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? Review of Financial Studies 22 (5), 1915–1953.
DeMiguel, V., Nogales, F. J., Uppal, R., 2014. Stock return serial dependence and out-of-sample portfolio performance. Review of Financial Studies 27 (4), 1031–1073.
Diebold, F. X., Mariano, R. S., 2002. Comparing predictive accuracy. Journal of Business & Economic Statistics 20 (1).
Engle, R., Kelly, B., 2012. Dynamic equicorrelation. Journal of Business & Economic Statistics 30 (2), 212–228.
- Epps, T. W., 1979. Comovements in stock prices in the very short run. Journal of the American Statistical Association 74 (366a), 291–298.
Paper not yet in RePEc: Add citation now
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.
Fleming, J., Kirby, C., Ostdiek, B., 2001. The economic value of volatility timing. Journal of Finance 56 (1), 329–352.
Fleming, J., Kirby, C., Ostdiek, B., 2003. The economic value of volatility timing using realized volatility. Journal of Financial Economics 67 (3), 473–509.
Gonçalves, S., Meddahi, N., 2009. Bootstrapping realized volatility. Econometrica 77 (1), 283–306.
Han, Y., 2006. Asset allocation with a high dimensional latent factor stochastic volatility model. Review of Financial Studies 19 (1), 237–271.
Hansen, P. R., Lunde, A., 2005. A realized variance for the whole day based on intermittent highfrequency data. Journal of Financial Econometrics 3, 525–554.
Hansen, P. R., Lunde, A., 2006. Realized variance and market microstructure noise. Journal of Business & Economic Statistics 24 (2), 127–161.
Hansen, P. R., Lunde, A., Nason, J. M., 2011. The model confidence set. Econometrica 79 (2), 453–497.
Hautsch, N., Kyj, L. M., Malec, P., 2015. Do high-frequency data improve high-dimensional port30 folio allocations? Journal of Applied Econometrics 30 (2), 263–290.
Holtz-Eakin, D., Newey, W., Rosen, H. S., 1988. Estimating vector autoregressions with panel data.
Jagannathan, R., Ma, T., 2003. Risk reduction in large portfolios: Why imposing the wrong constraints helps. Journal of Finance 58 (4), 1651–1684.
Komunjer, I., Ng, S., 2014. Measurement errors in dynamic models. Econometric Theory 30 (1), 150–175.
Laurent, S., Rombouts, J. V., Violante, F., 2013. On loss functions and ranking forecasting performances of multivariate volatility models. Journal of Econometrics 173 (1), 1–10.
- Lawrence, C. T., Tits, A. L., 2001. A computationally efficient feasible sequential quadratic programming algorithm. Siam Journal on Optimization 11 (4), 1092–1118.
Paper not yet in RePEc: Add citation now
Ledoit, O., Wolf, M., 2003. Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of Empirical Finance 10 (5), 603–621.
- Ledoit, O., Wolf, M., 2004a. Honey, I shrunk the sample covariance matrix. Journal of Portfolio Management 30 (4), 110–119.
Paper not yet in RePEc: Add citation now
Ledoit, O., Wolf, M., 2004b. A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis 88 (2), 365–411.
Li, J., 2015. Sparse and stable portfolio selection with parameter uncertainty. Journal of Business & Economic Statistics 33 (3), 381–392.
Liu, Q., 2009. On portfolio optimization: How and when do we benefit from high-frequency data? Journal of Applied Econometrics 24 (4), 560–582.
- Lunde, A., Shephard, N., Sheppard, K., 2015. Econometric analysis of vast covariance matrices using composite realized kernels and their application to portfolio choice. Journal of Business & Economic Statistics (forthcoming).
Paper not yet in RePEc: Add citation now
Magnus, J. R., Neudecker, H., 1980. The elimination matrix: some lemmas and applications. SIAM Journal on Algebraic Discrete Methods 1 (4), 422–449.
Marcellino, M., Stock, J., Watson, M., 2006. A comparison of direct and iterated multistep ar methods for forecasting macroeconomic time series. Journal of Econometrics 135, 499–526.
Noureldin, D., Shephard, N., Sheppard, K., 2012. Multivariate high-frequency-based volatility (HEAVY) models. Journal of Applied Econometrics 27 (6), 907–933.
Oh, D. H., Patton, A. J., 2015. High dimension copula-based distributions with mixed frequency data. Journal of Econometrics, Forthcoming.
Pakel, C., Shephard, N., Sheppard, K., Engle, R. F., 2014. Fitting vast dimensional time-varying covariance models. Working Paper.
Patton, A. J., 2011. Volatility forecast comparison using imperfect volatility proxies. Journal of Econometrics 160 (1), 246–256.
- Politis, D. N., Romano, J. P., 1994. The stationary bootstrap. Journal of the American Statistical Association 89 (428), 1303–1313.
Paper not yet in RePEc: Add citation now
Pooter, M. d., Martens, M., Dijk, D. v., 2008. Predicting the daily covariance matrix for S&P 100 stocks using intraday data–but which frequency to use? Econometric Reviews 27 (1-3), 199–229.
Sizova, N., 2011. Integrated variance forecasting: Model based vs. reduced form. Journal of Econometrics 162, 294–311.
Staudenmayer, J., Buonaccorsi, J. P., 2005. Measurement error in linear autoregressive models. Journal of the American Statistical Association 100 (471), 841–852.
Tu, J., Zhou, G., 2011. Markowitz meets Talmud: A combination of sophisticated and naive diversification strategies. Journal of Financial Economics 99, 204–215.
Varneskov, R., Voev, V., 2013. The role of realized ex-post covariance measures and dynamic model choice on the quality of covariance forecasts. Journal of Empirical Finance 20, 83–95.
Voev, V., 2008. Dynamic modelling of large-dimensional covariance matrices. In: High Frequency Financial Econometrics (eds. L. Bauwens, W. Pohlmeier and D. Veredas). Physica-Verlag, pp. 293–312.
White, H., 2000. A reality check for data snooping. Econometrica 68 (5), 1097–1126.