Appendix

See Table 22.


Author details

Antoine Bambade<sup>1</sup>

group, USA

73

\* and Kesheng Wu<sup>2</sup>

2 Lawrence Berkeley National Laboratory, Scientific Data Management (SDM)

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: antoine.bambade@polytechnique.edu

1 École Polytechnique (Palaiseau), France

An Assessment of the Prediction Quality of VPIN DOI: http://dx.doi.org/10.5772/intechopen.86532

provided the original work is properly cited.

#### Table 22.

List of future contracts and their total volume of trades from January 2007 to July 2012.

An Assessment of the Prediction Quality of VPIN DOI: http://dx.doi.org/10.5772/intechopen.86532

• Describe more precisely to which class of financial instrument VPIN predictive

• Define a normalization of events defining crash events within a whole cluster of instruments. It is not easy to put in place as instruments are more or less correlated by crashes and response times are not trivial to analyze, but it would be also interesting indeed to assess prediction quality on common events shared by different instruments of a same cluster. It would make it possible to see whether or not VPIN predictive power is effective beyond different financial instruments embedding different aspects of the financial world to

• This area of research studies a very particular class of events: those that are potentially very rare. Taking into account this setting and that the algorithms used are fed with previous information and are sensitive to the starting point of computation, is it possible to build a consistent cross-validation approach? This aspect has not been treated yet as others needed to be first addressed, but it is

Symbol Description Exchange Class Volume ES S&P500 E-mini CME Equity 478,029 EC Euro FX CME Currency 188,837 CL Light Crude NYMEX NYMEX Energy 165,208 YM Dow Jones E-mini CBOT Equity 110,122 NQ Nasdaq 100 CME Equity 173,211

List of future contracts and their total volume of trades from January 2007 to July 2012.

power is most effective (if such one is worth being more studied for

practitioners).

which VPIN is sensitive to.

Advanced Analytics and Artificial Intelligence Applications

still important to be studied.

Appendix

Table 22.

72

See Table 22.
