# [Eventstudies-commits] r62 - pkg/vignettes

Tue Apr 30 05:18:15 CEST 2013

Author: vikram
Date: 2013-04-30 05:18:15 +0200 (Tue, 30 Apr 2013)
New Revision: 62

Modified:
pkg/vignettes/ees.Rnw
Log:
Added ees plot in the vignette; matched results with the Tables in the paper

Modified: pkg/vignettes/ees.Rnw
===================================================================
--- pkg/vignettes/ees.Rnw	2013-04-29 21:04:41 UTC (rev 61)
+++ pkg/vignettes/ees.Rnw	2013-04-30 03:18:15 UTC (rev 62)
@@ -33,12 +33,14 @@

Using this function, one can to understand the distribution and run
length of the clustered events, quantile values for the extreme
-events and yearly distribution of the extreme events.
+events and yearly distribution of the extreme events. In the sections
+below we replicate the analysis for S\&P 500 from the paper and we
+generate the extreme event study plot for event on S\&P 500 and
+response of NIFTY.

-
\section{Extreme event analysis}
-This function just needs input in returns format on which extreme
-event analysis is to be done. Further we define tail events for given
+This function needs input in returns format on which extreme
+event analysis is to be done. Further, we define tail events for given
probability value. For instance, if \textit{prob.value} is 5 then both
side 5\% tail events are considered as extreme, lower tail and upper
tail (5\% to 95\%).
@@ -66,7 +68,8 @@
Here we have data summary for the complete data-set which shows
minimum, 5\%, 25\%, median, mean, 75\%, 95\%, maximum, standard
deviation (sd), inter-quartile range (IQR) and number of
-observations. The output is shown below:
+observations. The output shown below mathces with the fourth column
+in Table 1 of the paper.
<<>>==
output$data.summary @ @@ -76,7 +79,8 @@ columns. The first column is \textit{event.series} column which has returns for extreme events and the second column is \textit{cluster.pattern} which signifies the number of consecutive -days in the cluster. Here we show results for the lower tail. +days in the cluster. Here we show results for the lower tail of S\&P +500. Below is the extreme event data set on which analysis is done. <<>>= str(output$lower.tail$data) @ @@ -90,7 +94,8 @@ total number of extreme events used for the analysis which is sum of \textit{unclstr} (unclustered events) and \textit{used.clstr} (Used clustered events). \textit{Tot} -are the total number of extreme events in the data-set. +are the total number of extreme events in the data set. The results +shown below match with second row in Table 2 of the paper. <<>>= output$lower.tail$extreme.event.distribution @ @@ -100,21 +105,25 @@ events. Run length shows total number of clusters with \textit{n} consecutive days. In the example below we have 3 clusters with \textit{two} consecutive events and 0 clusters with \textit{three} consecutive -events. +events. The results shown below match with second row in Table 3 of +the paper. <<>>= output$lower.tail$runlength @ \subsection{Extreme event quantile values} Quantile values show 0\%, 25\%, median, 75\%,100\% and mean values for -the extreme events data. +the extreme events data. The results shown below match with second row +of Table 4 in the paper. <<>>= output$lower.tail$quantile.values @ \subsection{Yearly distribution of extreme events} This table shows the yearly distribution and -the median value for extreme events data. +the median value for extreme events data. The results shown below +match with third and forth column for S\&P 500 in the Table 5 of the +paper. <<>>= output$lower.tail$yearly.extreme.event @ @@ -140,11 +149,24 @@ output$lower.tail\$runlength
@

-\section{Extreme event}
-% Quantile values
+\section{Extreme event study plot}
+Here, we replicate the Figure 7, from the paper Patnaik, Shah and
+Singh (2013). First, we need to have a merged time series object with
+event series and response series with no missing values for unerring
+results. After getting the time series object we just need to use the
+following function and fill the relevant arguments to generate the
+extreme event study plot.

-% Plot event study graph
-
+The function generates extreme values for the event series with the
+given probability value. Once the values are generated, clustered
+extreme events are fused together for the response series and
+extreme evenstudy plot is generated for very bad and very good
+events. The detail methodology is mentioned in the paper.
+<<>>=
+eesPlot(z=eesData, response.series.name="nifty", event.series.name="sp500",
+        titlestring="S&P500", ylab="(Cum.) change in NIFTY", prob.value=5,
+        width=5)
+@
\begin{figure}[t]
\begin{center}
\caption{Extreme event on S\&P500 and response of NIFTY}