A direct transformation of a time series can help you exploring your data and creating econometrics models in a blink of an eye. This is because you can use any variation of a time series in your model without having to insert the transformed time series in the spreadsheet. The list of transformations available in the Econometrics Toolbox is:
Exp 
2nd_Difference 
Power(n) 
Log 
Returns 
Fitted_AR(p) 
Sqrt 
AccumulatedReturns 
Fitted_MA(q) 
Normalization 
LogReturns 
Fitted_ARIMA(p,d,q) 
Abs 
AccumulatedLogReturns 
EWMA(lambda) 
Lag 
Square 
Fitted_Garch(p,q) 
Seasonal_Difference 
SeriesMean_Squared 
Error 
1st_Difference 


Using a transformation in your time series is just a matter of enclosing the transformation in the variable operators (Y[], X1[], X2[], … ) . Multiple operators are allowed, just type the transformations followed by a semicolon. Evaluations are made from the left to the right. So, for instance, Y[power(2); exp] will first apply the function f(x) = x^2 to every data x of the series denoted by Y and then apply the function g(f(x)) = exp(f(x)). As you can note, transformations that contain parenthesis need to have the letters enclosed replaced by appropriate parameters.
There is also one special transformation, the “error” transformation. This transformation returns the difference between the original series and the subsequent transformations until its evocation. Therefore, a transformation like Y[power(2); error] returns a transformed series Z = Y  Y^2, where Y is the original series in the spreadsheet.
All transformations are also available through the function “sTimeSeriesTransform” where you can simply type a transformation string — ex: “Y[log;power(2)]” — as one of the function's arguments.
To practice the above concepts a little bit, we are going to build a transformation to explore how the volatility of the following time series of some stock prices behaves, considering the series below:
A 
B 
C 
D 
E 
F 

1 
Apple Inc. (Adjusted close price) 





2 






3 
20121231 
71.03051 




4 
20130102 
73.28087 




5 
20130103 
72.3559 




6 
20130104 
70.34045 




7 
20130107 
69.92669 




8 
20130108 
70.11489 




9 
20130109 
69.01907 




10 
20130110 
69.87463 




11 
20130111 
69.44619 




12 
20130114 
66.97025 




13 
20130115 
64.85737 




14 
20130116 
67.54952 




... 
... 
... 




746 
20151211 
112.5692 




747 
20151214 
111.873 




748 
20151215 
109.8937 




749 
20151216 
110.7391 




750 
20151217 
108.3918 




751 
20151218 
105.4578 




752 
20151221 
106.7507 




753 
20151222 
106.6513 




754 
20151223 
108.0238 




755 
20151224 
107.447 




756 
20151228 
106.2435 




757 
20151229 
108.1531 




758 
20151230 
106.7408 




759 
20151231 
104.6919 




760 






We need the following chain of transformations: 1º Transform price to log returns, 2º Square the returns to get a measure of an instantaneous volatility, 3º smooth the volatility using the exponential weighted moving average model.
For the first transformation, we can use directly the “LogReturns” transformation or the pair 1st_Difference;Log. The second transformation can be done by the power(2) transformation or to be picky with volatility definition we can use the “SeriesMean_Squared” (the mean of logreturns is very close to zero for daily returns). Now, to smooth the results, we can apply an EWMA model with a decaying factor of 0.95, that is, use the transformation “EWMA(0.95)”. Putting it all together, we come to the following transformation string: Y[LogReturns;Power(2);EWMA(0.95)]. The screenshot of insertion box and the result is displayed below:
And double clicking the chart to expand it:As a final remark, please note that when you have only one time series the explicitness of the operator Y[] is not necessary. Besides, the command window is not case sensitive. So, typing “Power” and “POWER” will lead to the same results.