[Eventstudiescommits] r62  pkg/vignettes
noreply at rforge.rproject.org
noreply at rforge.rproject.org
Tue Apr 30 05:18:15 CEST 2013
Author: vikram
Date: 20130430 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 20130429 21:04:41 UTC (rev 61)
+++ pkg/vignettes/ees.Rnw 20130430 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 dataset which shows
minimum, 5\%, 25\%, median, mean, 75\%, 95\%, maximum, standard
deviation (sd), interquartile 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$data)
@
@@ 90,7 +94,8 @@
total number of extreme events used for the analysis which is sum of
\textit{unclstr} (unclustered events) and \textit{used.clstr} (Used
clustered events). \textit{Tot}
are the total number of extreme events in the dataset.
+are the total number of extreme events in the data set. The results
+shown below match with second row in Table 2 of the paper.
<<>>=
output$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}
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