4. VPIN sensitivity to the starting point of a data set

In this section, first we present the problem of VPIN's sensitivity to the starting point of the bucketing process. Second, we present different calibrations to test its sensitivity. Third we make a summary of our results.

#### 4.1 The problem

VPIN received among critics one which is important to precisely assess. Indeed, Bodarenko and Anderson [7] pointed out in their work that VPIN is sensitive to the starting point of the bucketing process. More precisely, if one removes the first buckets of the data set, results change. It is indeed right. We would like to know to which extent one can or cannot mitigate this effect. One idea is to test the different price bar structures. Indeed a bar structure influences trade imbalance and thus influences the appearance of VPIN events.

#### 4.1.1 Methodology

There are at least two interesting ways of analyzing the sensitivity to the starting point of a data set:

• Study the sensitivity of best precision+recall rate to the number of trades erased and to the bar price option.

4.2.2 Sensitivity to local best parameter choice

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

We remark the following:

is about 3.5%.

phenomenon.

5.1 Summary of results

We found that:

5. Conclusion

of bar erased.

In Table 21 we summarize for each bar price structure the average percentabe change of the initial local best precision+recall rates with the number

• Again the amplitude of the sensitivity is not very large as the maximum change

In this last section, we present first a general summary of our findings. Then we

• VPIN has interesting predictive power (i.e., better than a naive algorithm and at least of local prediction+recall maximum higher than 1.2) for flash events of

instruments, where flash crashes are at least present (which is not the case for

• VPIN is sensitive to the starting point of computation, but the amplitude of this sensitivity is not really high. For practice, which means not changing local best

• Define a bigger constraint to capture crashes taking into account, for example, their V-shape. It would indeed filter out more events and enable analyzing

• Benchmark within this framework other predictive tools between them (VIX

• If previous predictive power of lower amplitude flash events is interesting for practitioners, analyze more precisely parameters that would be interesting for

parameters while erasing some data, last bar price structure is the least

lower amplitude than flash crashes (about 1.5%) for a certain class of

propose new suggestion of research concerning this precise subject.

currency Euro FX or Energy Light Crude NYMEX).

For further studies, this might be worth analyzing:

with a naive algorithm, with VPIN, etc.).

• Analyze VPIN time-clock version predictive power.

more accurately which kind of crash VPIN predicts better.

sensitive to this phenomenon.

5.2 Suggestion for further studies

them.

71

• Last bar price structure is less sensitive than other price structure to this

• Given one set of local optimal parameters, study the sensitivity of precision and recall rates to bar price option and data removed.

We have removed l∈ 0; 1000; 2000; 3000 number of bars to study the sensitivity in the two previous cases, which corresponds to several hours of trading data removed. Indeed one does not want to erase first flash crash detected in the data set and erase more buckets than the average prediction length to detect it. Moreover we would like to study to which extent VPIN is locally sensitive.
