mfefm 2nd assignment.pdf

8/20/2019 MFEFM 2nd Assignment.pdf

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Problem Statement : Forecast the value of Sensex for first week of September

based on previous six months values.

Graph of Sensex

The above graph shows that the data chosen for the study is continuous and that

there is no gap/break in the data.


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Correlogram of Sensex

From the correlogram, we can see that the autocorrelation demonstrates an

exponentially decaying pattern. This is an indication of the presence ofseasonality.

From the partial correlation graph we can see the presence of AR(1) and possibly

SAR(7). However, we shall conduct the unit root test before going for regression.


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ADF Unit Root Test on Sensex

From the unit root test we can say that, there is 28.76% probability that the series

has a unit root.

Unit root tests in isolation are inconclusive, but along with the correlogram, they

give an insight into the presence of trend in the data being studied. Here, both

the unit root test and the correlogram suggest that trend is present in the data

being studied.


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ADF Unit Root Test on Sensex first Difference

This validates our claim of white noise being achieved directly.

So, instead of de-trending first lets go for de-seasonalizing and check the results.


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Correlogram of Deseasonalized Sensex

We can see the presence of various variables here from this correlogram. Lets

prepare a model through which we can run the regression and check the results.


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First regression after Deseasonalizing

We will need to remove insignificant variables from this which is MA(2).


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FINAL REGRESSION RESULTS

Now, we have prepared a model in which we have accounted for all the relevant

variables for Sensex. Before going for forecasting let’s have a look at the

correlogram to check whether white noise has been achieved or not.


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CORRELOGRAM OF DESEASONALIZED SENSEX

As can be seen from the above correlogram we have been able to achieve the

White Noise process. So now we can go for forecasting.


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STATIC FORECAST RESULTS

As we can see that the Mean Absolute Percent Error for our model is pretty low

i.e. 0.78%. It shows that our model captures the Sensex value with enough

precision. Lets have a look at the graph to see the pattern.


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GRAPHS FOR STATIC FORECAST

In our original dataset we had left blank the values of Sensex from 01/09/2015 to

10/09/2015. Lets have a look at the values our model has given for the following

dates.


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DYNAMIC FORECAST RESULTS

mfefm 2nd assignment.pdf

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