# [Eventstudies-commits] r324 - pkg/vignettes

Mon May 12 16:53:23 CEST 2014

Author: chiraganand
Date: 2014-05-12 16:53:23 +0200 (Mon, 12 May 2014)
New Revision: 324

Modified:
pkg/vignettes/new.Rnw
Log:
Added text on augmented market model event study.

Modified: pkg/vignettes/new.Rnw
===================================================================
--- pkg/vignettes/new.Rnw	2014-05-12 14:15:52 UTC (rev 323)
+++ pkg/vignettes/new.Rnw	2014-05-12 14:53:23 UTC (rev 324)
@@ -16,7 +16,7 @@
\end{abstract}
\SweaveOpts{engine=R,pdf=TRUE}

-\Section{The standard event study in finance}
+\section{The standard event study in finance}

In this section, we look at using the eventstudies package for the
purpose of doing the standard event study using daily returns data in
@@ -59,10 +59,10 @@
(i.e. the name of the unit of observation) in the list of events.

The package gracefully handles the three kinds of problems encountered
-with real world data: (a) a firm in the returns where there is no
-event, (b) a firm with an event where returns data is lacking and (c)
-a stream of missing data in the returns data surrounding the event
-date.
+with real world data: (a) a firm where returns is observed but there
+is no event, (b) a firm with an event where returns data is lacking
+and (c) a stream of missing data in the returns data surrounding the
+event date.

With this in hand, we are ready to run our first event study, using
raw returns:
@@ -163,17 +163,19 @@
eventList = SplitDates,
width = 10,
type = "marketResidual",
-                    market.returns=OtherReturns$NiftyIndex, to.remap = TRUE, remap = "cumsum", inference = TRUE, - inference.strategy = "bootstrap") + inference.strategy = "bootstrap", + market.returns=OtherReturns$NiftyIndex
+                    )
@

In addition to setting type' to marketResidual', we are now required
to supply data for the market index, $r_{Mt}$. In the above example,
-this is the data object NiftyIndex supplied in the package. This is
-just a zoo vector with daily returns of the stock market index.
+this is the data object NiftyIndex supplied from the OtherReturns data
+object in the package. This is just a zoo vector with daily returns of
+the stock market index.

\begin{figure}
\begin{center}
@@ -191,14 +193,67 @@

We close our treatment of the standard finance event study with one
-step forward on further reducing the variance of $\epsilon$: by doing
+step forward on further reducing $\textrm{Var}(\epsilon)$ : by doing
an augmented market model' regression with more than one explanatory
-variable.
+variable. The augmented market model uses regressions like:

+$+r_{jt} = \alpha_j + \beta_1,j r_{M1,t} + \beta_2,j r_{M2,t} + \epsilon_{jt} +$
+
+where in addition to the market index $r_{M1,t}$, there is an
+additional explanatory variable $r_{M2,t}$. One natural candidate is
+the returns on the exchange rate, but there are many other candidates.
+
+An extensive literature has worked out the unique problems of
+econometrics that need to be addressed in doing augmented market
+models. The package uses the synthesis of this literature as presented
+in \citet{patnaik2010amm}.\footnote{The source code for augmented
+  market models in the package is derived from the source code written
+  for \citet{patnaik2010amm}.}
+
+To repeat the stock splits event study using augmented market models,
+we use the incantation:
+
+es.amm <- eventstudy(firm.returns = StockPriceReturns,
+                    eventList = SplitDates,
+                    width = 10,
+                    type = "lmAMM",
+                    to.remap = TRUE,
+                    remap = "cumsum",
+                    inference = TRUE,
+                    inference.strategy = "bootstrap",
+                    market.returns=OtherReturns$NiftyIndex, + others=OtherReturns$USDINR,
+                    market.returns.purge=TRUE
+                    )
+@
+
+Here the additional regressor on the augmented market model is the
+returns on the exchange rate, which is the slot USDINR in
+OtherReturns. The full capabilities for doing augmented market models
+from \citet{patnaik2010amm} are available. These are documented
+elsewhere. For the present moment, we will use the feature
+market.returns.purge without explaining it.
+
+Let us look at the gains in statistical efficiency across the three
+variants of the event study. We will use the width of the confidence
+interval at date 0 as a measure of efficiency.
+
+<<efficiency-comparison,results=verbatim>>=
+tmp <- rbind(es$eventstudy.output[10,], es.mm$eventstudy.output[10,])[,c(1,3)]
+rownames(tmp) <- c("None","MM")
+tmp[,2]-tmp[,1]
+@
+
+This shows a sharp reduction in the width of the bootstrap 95\%
+confidence interval from None to MM adjustment. Over and above this, a
+small gain is obtained when going from MM adjustment to AMM
`