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\begin{document}


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\noindent \large{Siem Jan Koopman, Rutger Lit, Andr{\'e} Lucas and
 Anne Opschoor, \\ ``Dynamic Discrete Copula Models for High Frequency
 Stock Price Changes'', \emph{Journal of Applied Econometrics}, 2018, 33,
 7, 966--985. }

\subsection*{Data source and preparation}
In this document we provide the steps we took to obtain and prepare the data.
We use data from the Trades and Quotes (TAQ) database of the New York Stock Exchange at a one-second frequency.
VU University Amsterdam has a subscription to the Wharton Research Data Services (WRDS) which offers among other data sources, access to the TAQ database.
It contains intraday transactions data (trades and quotes) for all securities listed on the New York Stock Exchange (NYSE) and American Stock Exchange (AMEX), as well as Nasdaq National Market System (NMS) and SmallCap issues.
We have selected 10 companies from different industries and with different trade intensities for all trading days in 2012. The following corresponding Ticker symbols are USB,
AIG, COF, MS, AXP, BAC, GS, WFC, C and JPM. We analyze the tick-by-tick data without the ``odd-lots'' that represent trades with volumes less than 100 and that are not recorded on the consolidated tape; see for example \citet{OHara(14)}. The data require standard pre-processing. For a review of high-frequency data cleaning procedures; see for example \citet{Falkenberry(02)}.
We apply the cleaning algorithm of \citet{Brownlees(06)} after applying a rudimentary filter corresponding to the cleaning steps P1, P2, P3 and T1, T2, T3 of \citet[p.~8]{Barndorff(08)}.
In cleaning step T2, only trades with the sale condition \{blank, @, *, E, F\} are kept, see the TAQ user guide for details.
Descriptive statistics are presented in Table 2 of the paper.

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\bibitem[\protect\citeauthoryear{Barndorff-Nielsen, Hansen, Lunde, and
  Shephard}{Barndorff-Nielsen et~al.}{2008}]{Barndorff(08)}
Barndorff-Nielsen, O.~E., P.~R. Hansen, A.~Lunde, and N.~Shephard (2008).
\newblock Realised kernels in practice: Trades and quotes.
\newblock {\em Econometrics Journal\/}~{\em 4}, 1--33.

\bibitem[\protect\citeauthoryear{Brownlees and Gallo}{Brownlees and
  Gallo}{2006}]{Brownlees(06)}
Brownlees, C.~T. and G.~M. Gallo (2006).
\newblock Financial econometric analysis at ultra-high frequency: Data handling
  concerns.
\newblock {\em Computational Statistics \& Data Analysis\/}~{\em 51},
  2232--2245.

\bibitem[\protect\citeauthoryear{Falkenberry}{Falkenberry}{2002}]{Falkenberry(02)}
Falkenberry, T.~N. (2002).
\newblock High frequency data filtering.
\newblock Technical report, Tick Data.

\bibitem[\protect\citeauthoryear{O{'}Hara, Yao, and Ye}{O{'}Hara
  et~al.}{2014}]{OHara(14)}
O{'}Hara, M., C.~Yao, and M.~Ye (2014).
\newblock What's not there: Odd lots and market data.
\newblock {\em Journal of Finance\/}~{\em 69(5)}, 2199--2236.

\end{thebibliography}

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