Everyone knows how the impact what oil has on the Russian economy. But how it can be explained in the context of math and how it changes in time? I calculated the regression equation and pairwise comparisons for BR futures and USDRUB. All data I took from FINAM open database.
The first thing I did was import data arrays over a period of the 2012-2017 year.After a simple transformation of data the following
the chart has turned out:
On a vertical axis is a price of USDRUB, on a horizontal axis – BRENT future price. Even on this step, we see high dependence.
Output calculations will be the following:
The regression equation is:
With the main coefficients:
Variable A in the red square is approximation error.We see it goes least at exponential regression. Hereafter we’ll use this particular kind of regression. Here are the output charts:
Blue line – line regression, Red- exponential.
Chart also shows the exponential dependence between USDRUB and BRENT price over the five years distances.
During that period share of oil and gas incomes in Russian trade balance have been about 47,6% (statistic average).Currently, the percentage has been declined to the smaller amount.
The table below shows how oil and gas share have been changing during the last decade.
Let’s explore how the correlation between USDRUB and BRENT was changing at this period. Likely that dependence should decline with reduct considering the part of oil and gas share in the trade balance. Here what show statistical calculations:
We should keep in mind that Central Bank of Russia moved to floating rate policy towards national currency in November of 2017. It reflected in investigating dependence. Remind that Central Bank of Russia had controlled ruble’s price by сorridor rule.
It shows how correlation had changed after removal the corridor rule. Best seen displays 2013 and 2014 year chart. Since 2014 USDRUB/BRENT have begun to obey the power low and we can see how correlation increased compared to previous periods.
2013 year
2014 year
This kind of dependence is observing up to the 2017 year. In sum, this gives a high statistical significance for prediction power of this model. Here is the equal for 2016:
However, there is one question left: Why the correlation has been reminding at the high level during the 2012 year? I’ll gonna think about it in upcoming research.
If calculates the theoretical price of Russian ruble with this formula, the following value will be around $64.99 (BRENT = 48.99). Considering that USDRUB is equal 57.04, we get the 14% error.It’s more than enough to say that similar ceased to reflect the reality.Also, the chart for 2017 shows the same:
BR/USDRUB 2017
It is evident that power dependence disappeared utterly. The correlation falls to 37.89%, which is the lowest since 2012. Why does it happen? It’ll be more clear at the end of 2017 when I can calculate the total data.In any case, it’s clear that the Russian economy is changing, as reflected in the graphs.
The main conclusion I drow from this research is that in the long term period there is a high power dependence between the USDRUB and oil price. This instrument can become useful for macroeconomic forecast inside trading strategies.
In fact, I found more questions than answers, so the investigation keeps up.Next time I’ll take a more significant time period with other macroeconomic indicators.
For those purposes, I need the stronger tool than excel.I think about R. let’s see.
