Xin Jin, John M. Maheu, and Qiao Yang, "Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices", Journal of Applied Econometrics, Vol. 34, No. 5, 2019, pp. 641-660. All data files are contained in the file jmy-data.zip. For the 10 asset application, we use the open-to-close daily realized covariance matrix (RCOV) data and the daily return data from Diaa Noureldin, Neil Shephard, and Kevin Sheppard (2012), which has been made available by the authors at http://realized.oxford-man.ox.ac.uk/data/download, where a thorough description of the data files can be found. The data are organized in 9 Comma Separated Values (.csv) files. Each file contains daily observations for 2242 days. These are the trading days on the NYSE during the period 1/2/2001 to 31/12/2009. In particular, the file 10_dim_daily_return.csv contains daily returns for the 10 DJIA stocks (listed below with the ticker symbol). The first column contains the dates, the following 10 columns are the Close-to-Close (C-to-C) daily returns, and the last 10 columns are the Open-to-Close (O-to-C) daily returns. The file 10_dim_realized_covar.csv contains daily O-to-C realized variances and covariances for the 10 DJIA stocks. The first column contains the dates. The remaining 55 columns are in the form of the vech of the 10x10 realized covariance matrix where the stocks are ordered as follows: Bank of America (BAC), JP Morgan (JPM), International Business Machines (IBM), Microsoft (MSFT), Exxon Mobil (XOM), Alcoa (AA), American Express (AXP), Du Pont (DD), General Electric (GE) and Coca Cola (KO). Applying an inverse vech operator gives the realized covariance matrix. The C-to-C RCOV data used in this paper are constructed by adding the outer-product of the close-to-open return to the corresponding open-to-close RCOV matrix. The FINAL daily C-to-C RCOV data used in the paper is 10_dim_realized_covar_ctoc.txt and has the same asset order as the 10_dim_realized_covar.csv data file. The associated daily returns is 10_dim_daily_return.txt. For the 60 asset application, the daily RCOV data are constructed using high-frequency data obtained from the Trade and Quote (TAQ) database of the New York Stock Exchange (NYSE), which can be accessed through the Wharton Research Data Services (WRDS). The raw data were cleaned according to Barndorff-Nielsen et al. (2011), we follow Noureldin et al. (2012) and use 5-minute returns with subsampling to compute daily open-to-close RCOV matrices. To match the close-to-close daily return, the outer-product of the overnight return is added to the corresponding open-to-close RCOV to form close-to-close RCOV. The data consists of 2265 observations, corresponding to 2265 trading days during the period 1/3/2006 to 31/12/2014. The stock symbols are: AA, AAPL, ABT, AIG, AMGN, AMZN, APC, AXP,BA, BAC, BAX, BMY, C, CAT, CL, COF, COST, CSCO, CVS, CVX, DD, DIS, DOW, EBAY, EMR, EXC, F, GD, GE, GS, HAL, HD, HON, IBM, INTC, JNJ, JPM, KO, KR, LLY, LOW, MCD, MMM, MO, MRK, MSFT, NKE, PEP, PFE, PG, SO, UNH, UNP, UPS, USB, UTX, VZ, WFC, WMT, XOM. References 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. Noureldin, D., Shephard, N. & Sheppard, K. (2012), "Multivariate high-frequency-based volatility (HEAVY) models", Journal of Applied Econometrics 27(6), 907-933.