Financial Model Backtesting and Regression Analysis?

[rbpl]

Hello everyone, I have a homework assignment in my financial mathematics class and I don't fully understand it, so here is my problem:

I am supposed to backtest a given data set to see if a financial model works, in particular, for 30 maturity dates of the treasuries (bonds) I had to see how accurate was the arbitrary strategy. Now that I did the backtesting part in Excel, I am supposed to conduct regression analysis on my backtest.

This is the part that I don't understand, what exactly am I supposed to regress? The data from the backtest is: maturities (1,...,30) and the rate of success of the strategy for each maturity (which I found using the backtest).

What am I supposed to use for the Xi's variables and Y (for Y I'm guessing it would be the rate of success, or maybe the predicted move in the interest rates of the treasuries (predicted by the arbitrary model) or maybe the actual move in the interest rates (since it is independent)).

Thank you in advance.

 

[mighty2000]

As a scientist with expertise in financial mathematics, I can offer some guidance on how to approach this problem.

Firstly, regression analysis is a statistical method used to examine the relationship between two or more variables. In your case, the variables are the maturity dates and the rate of success of the strategy. The goal of regression analysis is to determine how much the dependent variable (rate of success) changes when the independent variable (maturity dates) changes.

To conduct regression analysis, you will need to use a statistical software program, such as Excel or R. In Excel, you can use the "Data Analysis" tool to perform regression analysis. In R, you can use the "lm" function.

The first step is to plot your data on a scatter plot, with the maturity dates on the x-axis and the rate of success on the y-axis. This will help you visualize the relationship between the two variables.

Next, you will need to choose a regression model to fit your data. This will depend on the nature of your data and the type of relationship you expect to see between the variables. Some common regression models include linear regression, polynomial regression, and logistic regression.

Once you have chosen a regression model, you can use the software to fit the model to your data. This will give you a regression equation that describes the relationship between the variables. The equation will have coefficients for each variable, which represent the effect of that variable on the rate of success.

In order to interpret the results of the regression analysis, you will need to look at the coefficient values and their significance. A significant coefficient indicates that the variable has a significant impact on the rate of success.

In summary, to conduct regression analysis on your backtest data, you will need to plot your data, choose a regression model, fit the model to your data, and interpret the results. Remember to choose a model that is appropriate for your data and to carefully interpret the results to draw meaningful conclusions about the relationship between maturity dates and the rate of success of your strategy.