Strategic Alliance: Arima Modelling – Research Paper – Sowmya Manjunath
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Strategic Alliance: Arima Modelling
[pic 1]MFEFM ASSIGNMENTARIMA Modelling[pic 2][pic 3][pic 4]ObjectiveThe objective is to analyze the closing Prices of stock of ITC over the period 7-29-2015 to 9-28-2015 and forecast the same for the next 3 days.ARIMA ModelingStage 1 – IdentificationData Spreadsheet: Closing Prices of stock of ITC over the period 7-29-2015 to 9-28-2015 are imported and shown below.[pic 5]Line graph of the Data set: Neither trends nor seasonality was visible; no conclusions were made about the same.[pic 6]Correlogram of the data: There is no decaying trend in the correlogram. There might be seasonality in the data. AR(1) and MA(1) signatures can be seen.[pic 7]Unit-root test:  We performed Unit root test to verify this. Selection of “Level” at “Trend & Intercept” gave the results shown below in which the t-statistic values at all three significance levels are more than the Augmented Dickey-Fuller test statistic, suggesting that the Null Hypothesis “CLOSE has a unit root” is rejected. This implied that the data does not have a trend. [pic 8]Stage 2 – EstimationEstimation of values of co-efficients – As there was no clear signature for seasonality in the data, we proceed with estimation. Using least square technique we estimated the following: ls d(close,1,0) c ar(1) ma(1)This gave us the results shown below. AR(1) is significant at 5% level and MA(1) is significant at 5% level. So this is an AR(1) MA(1) process.[pic 9]Stage 3 – Diagnostic CheckingResidual test: Now we will check for white noise process by taking the residual Q-statistics test. The residual test gave the above result where all p-values are >5%, except that at lag 3. However, as that value is 4.9%, which is very close to 5%, we will accept it. So we have reached the white noise process and no further information can be extracted from it. We now proceed for Static Forecasting.[pic 10]Stage 4 – Forecasting
Forecasting: The following results were obtained for static forecasting for the last 5 days in the data set:[pic 11]9/22/2015310.95315.19159/23/2015316.1312.94749/24/2015322.55318.74119/25/2015322.55322.46789/28/2015319.25320.9391The Static forecasting values are shown above in Gray Vs the actual values on the left.The MAPE is as low as 0.819% suggesting that the model is good.Now we will proceed to dynamic forecast, which gave the following results.9/28/2015318.44369/29/2015318.704110/01/2015318.8438Now, we will also try de-seasonalizing the data to see which model is a better fit.De-seasonalizing: We use the following command to generate a new de-seasonalized series – series dd=d(close,0,5). The correlogram of the new series hence generated is given below, in which a clear ar(1) signature can be identified.[pic 12]Estimation of values of co-efficients: Using least square technique we estimated the following: ls d(close,0,5) c ar(1). This gave us the results shown below. AR(1) is significant at 5% level. So this is an AR(1) process.[pic 13]Residual test: Now we will check for white noise process by taking the residual Q-statistics test. The residual test gave the below result where all p-values are >5%. So we have reached the white noise process and no further information can be extracted from it.[pic 14]
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By: Sowmya Manjunath
Submitted: October 18, 2015
Essay Length: 585 Words / 3 Pages
Paper type: Research Paper Views: 477
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