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Over recent decades, equity market dynamics have changed. High-frequency and other forms of algorithmic trading have increased the speed of price responses to new information. Further, social media and the Internet at large have led to an increase in the breadth of information processed by markets. Both developments suggest the need for a more fine-grained event study framework where an individual event' becomes more accurately located in time than by traditional daily event studies. Intraday event studies take the event study methodology one step further in this direction and contract the time unit the method analyses from days to minutes.

With the increasing availability of intraday price data, intraday event studies are quickly becoming the new standard, beginning with high-profile use cases such as securities litigation. There are two groups of significance test methods for intraday event studies, traditional methods, and novel jump detection techniques.

As its name suggests, the traditional approach applies the classic event study methodology to the intraday context. It tends to be complemented by a programmatic determination of the length of the event window - typically the amount of minutes after the event when the CAR ceases to be statistically relevant. The formulas for this approach read as follows:

\begin{equation}CAR_T = \sum\limits_{t=0}^T AR_t,\end{equation}

where $T$ denotes a period of time in minutes. The CAR is calculated both for each time period $0$ to $T$ of the control time frame (e.g. 30 days before the event) and for the period immediatelly followed the event. The event window is then determined data-driven by following procedure:

1.  Calculate the CAR value for the first $m$ minutes (e.g. 5 Minutes) after the event. Denote this value by $CAR_0$.
2. Assuming $CAR_0$ is found to be statistically significant. We continue calculating preceeding CAR values ($CAR_1, CAR_2, ...$) on the event day and test each of this CAR value on significance.
3. The first CAR value that is not significant, defines the end of the event window, e.g. $T^*$. Our API will then deliver the sequence $CAR_1, CAR_2, ..., CAR_{T^*}$.

The CAR test statistics for intraday event studies differ from the ones of daily event studies. This is the case because volatility structurally differs throughout a trading day; notably, it is highest right after the opening of the market and shortly prior to its closing. To accommodate for this particularity, the following adjustments to the test statistics calculation have been suggested:

1. Assume we have a control period of 60 days and a p value of 5%. Then we calculate for each day in the control period the corresponding $CAR_T$ value (mapped by the minutes). $CAR_T$ of the event day is then marked as significant iff this value is greater or less than the $0.975$- or $0.025$-percentile of the $CAR_T$ values from the control period.
2. Step 1 is repeated till the event window CAR value is not anymore significant.

Novel jump detection techniques

Besides of the above presented direct adaptation of the event study methodology, novel jump detection techniques are proposed. They differ from the traditional ESM as they leverage specific characteristics of intraday data and address issues common in intraday data, such as microstructure noise.

• Ait-Sahilia:
• Bi-Power Variation:
• Jiang-Omen Statistics:
• Statistical Finance: