R GARCH - Revision history

Rb: /* Introduction */

2018-05-04T00:41:32Z

‎Introduction

2018-05-03T23:24:13Z

‎Forecasts

2018-05-03T23:23:07Z

‎Forecasts

2018-05-03T23:20:51Z

‎Model Estimation

2018-05-03T23:19:20Z

‎Model Forecasting

2018-05-03T23:16:50Z

‎Model Estimation

2018-05-03T23:13:04Z

‎Model Estimation

2018-05-03T23:11:24Z

‎Model Specification

2018-05-03T23:10:49Z

‎Data upload

2018-05-03T23:05:29Z

‎Data upload

← Older revision		Revision as of 00:41, 4 May 2018
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	When you are dealing with financial time-series we often have relatively high frequency observations available. It is very common for instance to have daily observations available. In fact it is now possible to obtain hourly, minute, second or even millisecond observations. But here we will restrict ourselves to daily observations. For some assets these will be 7 days a week observations, but for others these will be work-day observations, so typically 5 days a week of observations.		When you are dealing with financial time-series we often have relatively high frequency observations available. It is very common for instance to have daily observations available. In fact it is now possible to obtain hourly, minute, second or even millisecond observations. But here we will restrict ourselves to daily observations. For some assets these will be 7 days a week observations, but for others these will be work-day observations, so typically 5 days a week of observations.
		+
		+	A video walk-through is available from https://youtu.be/8VXmRl5gzEU

	= Packages used =		= Packages used =

@@ Line 439: / Line 439: @@
 corf_IG &lt;- Rf[[1]][1,3,]  # Correlation forecasts between IBM and Google
 corf_BG &lt;- Rf[[1]][2,3,]  # Correlation forecasts between BP and Google</pre>
-<code>[[1]]</code> tells R to go to the first (and here only) list item and then <code>[1,2,]</code> instructs R to select the (1,2) element of all available correlation matrices.
+<code>[ [1] ]</code> tells R to go to the first (and here only) list item and then <code>[1,2,]</code> instructs R to select the (1,2) element of all available correlation matrices.
 As for the univariate volatililty model let us display the forecast along with the last in-sample estimates of correlation.

@@ Line 458: / Line 458: @@
 plot(c_BG,type = &quot;l&quot;,main=&quot;Correlation BP and Google&quot;)
 lines(cf_BG,type = &quot;l&quot;, col = &quot;orange&quot;)</pre>
-[[File:GarchModelling_files/figure-html/unnamed-chunk-34-1.png]]<!-- -->
+[[File:mg_forecast.png]]<!-- -->
 = Further thoughts =

@@ Line 363: / Line 363: @@
 <pre class="r">plot(cor_BG)</pre>
-[[File:GarchModelling_files/figure-html/unnamed-chunk-28-1.png]]<!-- -->
+[[File:correlation1.png]]<!-- -->
 If you transformed <code>cor_BG</code> to be a <code>xts</code> series the <code>plot</code> function automatically picks up the date information. As you can see there is significant variation through time with the correaltion typically varying between 0.2 and 0.5.
@@ Line 373: / Line 373: @@
 plot(as.xts(cor1[1,3,]),main=&quot;IBM and Google&quot;)
 plot(as.xts(cor1[2,3,]),main=&quot;BP and Google&quot;)</pre>
-[[File:GarchModelling_files/figure-html/unnamed-chunk-29-1.png]]<!-- -->
+[[File:correlation2.png]]<!-- -->
 == Forecasts ==

@@ Line 288: / Line 288: @@
 <pre class="r">ug_f &lt;- ugfore@forecast$sigmaFor
 plot(ug_f, type = &quot;l&quot;)</pre>
-[[File:GarchModelling_files/figure-html/unnamed-chunk-18-1.png]]<!-- -->
+[[File:ug_forecast3.png]]<!-- -->
 Note that the volatility is the square root of the conditional variance.
@@ Line 301: / Line 301: @@
 lines(ug_f, col = &quot;orange&quot;)
 lines(ug_var_t, col = &quot;green&quot;)</pre>
-[[File:GarchModelling_files/figure-html/unnamed-chunk-19-1.png]]<!-- -->
+[[File:ug_forecast4.png]]<!-- -->
 You can see how the forecast of the conditional variance picks up from the last estimated conditional variance. In fact it decreases from there, slowly, towards the unconditional variance value.

@@ Line 255: / Line 255: @@
 <pre class="r">plot(ug_res2, type = &quot;l&quot;)
 lines(ug_var, col = &quot;green&quot;)</pre>
-[[File:GarchModelling_files/figure-html/CondVar2.png]]<!-- -->
+[[File:CondVar2.png]]<!-- -->
 == Model Forecasting ==

@@ Line 113: / Line 113: @@
 You could call <code>ug_spec</code> again to check that the model specification has actually changed.
-The following is the specification for an # an example of the EWMA Model (although we will not se it below).
+The following is the specification for an # an example of the EWMA Model (although we will not use it below).
 <pre class="r">ewma_spec = ugarchspec(variance.model=list(model=&quot;iGARCH&quot;, garchOrder=c(1,1)),
          mean.model=list(armaOrder=c(0,0), include.mean=TRUE),
          distribution.model=&quot;norm&quot;, fixed.pars=list(omega=0))</pre>
 == Model Estimation ==

@@ Line 56: / Line 56: @@
 <pre class="r">chartSeries(GOOG)</pre>
-[[File:GarchModelling_files/figure-html/GoogleChart1.png]]<!-- -->
+[[File:GoogleChart1.png]]<!-- -->
 When we are estimating volatility models we work with returns. There is a function that transforms the data to returns.