Computational Methods in Financial Mathematics Harvard Case Solution & Analysis

Computational Methods in Financial Mathematics Case Study Solution 

The code provides the visual and graphical interpretation of the data given in the excel file. The excel file contains the data of inflation and interest rate per year. The code is designed in a way that it first takes reads the value from the excel file. The inflation and interest rates are picked from each sheet of the data and placed into the separate variables. Then the dates were also extracted from the data and placed in new text variables. Once the data is extracted the data is reviewed and the values of months and dates of inflation data are separated from the combined string.

This segment a) of part 1 requirements were to find out the dates that provide the best possible return on interest rates over inflation rates. The job was first to make inflation rates out of from inflation values and also to convert interest rates into monthly investment rates. This was done by simply using the formulas stated in the assignment file and the results were produced by inserting a simple comparison function that watches the data of interest rates when to be higher than the inflation rates. The index values were returned and the months and dates were found on those locations and placed invariably. This variable is then written to the excel file and that’s how the first requirement is achieved. The other requirement was to see how the data values of last few years affect the current rate. This is also done by taking year values of 1, 5, 10, 20, 30 and 40. The result is then printed on the command window. The data clearly suggested that the inflation rate is more than interest rate in all years.

The segment b) of part 1 dealt with the ways of plotting the data extracted from the given file. The code produced results for the determining the trend of the data set. Inflation is seen to be a normal curve with strong seasonality while the interest is a decreasing trend.

The data is tested for the stationary using KPSS and ADF rule for both inflation and interest rates. Both produced a positive result for inflation rate that means that the data have a constant variation and mean while for interest rate ADF rule showed that the data is not stationary.

The data was tested for correlation with each other and the results generated by the MATLAB simulations suggests that they have a positive correlation to each other. It means if one is decreased other is effected simultaneously. They have a strong relation with each other.

The data was fitted on a curve using Hodrick – Prescott (HP) filtering method and plots were produced.We got a fitted line for both of our inputs and can be shown in blue lines in the results segment.

The cross-correlation was used just to find out if there happens to be any relation and the relation is lagging. At last the clustering was done using k-means command and 10 clusters were made. The values along with the centroids are plotted.

All of the results with a proper title message were plotted and displayed. The file has just one function file and the main file named as part1 that needs to be run.................

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