Example 1 - Revision history

Rb: /* Algorithm */

2015-10-22T13:40:25Z

‎Algorithm

2014-11-04T13:58:35Z

‎Construction of Log-returns

2012-10-18T09:11:19Z

2012-10-16T12:54:53Z

‎Constructing y and X

2012-10-16T12:54:38Z

‎Constructing y and X

2012-10-16T12:52:27Z

‎Constructing y and X

2012-10-16T12:51:53Z

‎Constructing y and X

2012-10-16T12:50:06Z

‎OLS implementation

2012-10-16T12:48:52Z

‎Constructing y and X

2012-10-16T12:48:36Z

‎OLS implementation

@@ Line 21: / Line 21: @@
 = Algorithm =
-# Import data in MATLAB (data are downloaded from Yahoo finance, http://finance.yahoo.com, adjusted daily close prices, MSFT from Jan, 1 2000 to Sept, 10 2012).
+# Import data in MATLAB (data are downloaded from Yahoo finance, http://finance.yahoo.com, enter the Ticker MSFT into the search box and after clicking enter go to the historical prices link, adjusted daily close prices, from Jan, 1 2000 to Sept, 10 2012).
 # Construct a vector of log-returns
 # Construct <math>y</math> vector and <math>X</math> matrix. For this purpose a dummy for the day of the week is needed.

@@ Line 48: / Line 48: @@
 == Construction of Log-returns ==
-Log-returns are defined as <math>r_t=\ln(p_t)-\ln(p_{t-1})</math> The first return <math>r_1</math> is not defined, since <math>p_0</math> is not known. The long way to
+Log-returns are defined as <math>r_t=\ln(p_t)-\ln(p_{t-1})</math> The first return <math>r_1</math> is not defined, since <math>p_0</math> is not known. Let's assume that your price series is saved in a variable called <source enclose="none">p</source>  and you created a variable <source enclose="none">T</source> with the length of that price series. The long way to implement this in MATLAB is:
 <source>r=zeros(T,1);
@@ Line 71: / Line 69: @@
 <source>r=[0;diff(log(p))];</source>
 == Constructing day-of-the-week dummy ==

@@ Line 200: / Line 200: @@
 Summary statistics:
 <source>
-sum_stat=@(x)([mean(x);std(x);skewness(x);kurtosis(x)]); % For details, please check anonymous functions
+sum_stat=@(x)([mean(x);std(x);skewness(x);kurtosis(x)]); % For details, please check anonymous functions section
 disp('y')
 fprintf('\t Mean\t\tStd\t\tSkew\t\tKurt\n')

@@ Line 200: / Line 200: @@
 Summary statistics:
 <source>
-sum_stat=@(x)([mean(x);std(x);skewness(x);kurtosis(x)]);
+sum_stat=@(x)([mean(x);std(x);skewness(x);kurtosis(x)]); % For details, please check anonymous functions
 disp('y')
 fprintf('\t Mean\t\tStd\t\tSkew\t\tKurt\n')
@@ Line 208: / Line 208: @@
 disp(sum_stat(X(:,3))')
+y
-	 Mean		Std		Skew		Kurt
+	 Mean	Std	Skew	Kurt
-   -0.0001    0.0212   -0.0896   11.6643
+      -0.0001  0.0212  -0.0896  11.6643
 X(:,3)
-	 Mean		Std		Skew		Kurt
+	 Mean	Std	Skew	  Kurt
-   -0.0001    0.0212   -0.0901   11.6670
+      -0.0001  0.0212   -0.0901   11.6670
 </source>

@@ Line 198: / Line 198: @@
 <source>y=r(2:end);
 X=[ones(size(y)) Dw(2:end)  r(1:end-1)];</source>
 = OLS implementation =

@@ Line 222: / Line 222: @@
 == nor the NAG toolbox were available =====================</source>
 Output: X consists of three columns: intercept, Friday dummy and lagged return.
-According to the regression results, we can reject <math>H_0</math>. However, our standard errors are computed under the assumption of homoscedasticity of error terms. Given the heteroscedastic nature of the data, robust standard errors have to be used instead.
+According to the regression results, we can accept <math>H_0</math>. However, our standard errors are computed under the assumption of homoscedasticity of error terms. Given the heteroscedastic nature of the data, robust standard errors have to be used instead.