Granger Causality


Granger's Test for Causality Used On Gold Prices

Given two time series Yt,XtY_{t}, X_{t} Granger's test for causality is a statistical test to see if Ytγ,γ>0Y_{t - \gamma}, \gamma > 0 has predictive significance on XtX_{t}. Grangers test is a weaker test for causality in that the test only fulfills the conditions for the Humean definition for causality. Hence the motivation for this piece is to see if Granger causality will be sufficient on fitting a useful model.

Article Source Code

Code for the project can be found here

Preliminaries

We will be using a basket of features (gold prices, oil prices, unemployment, interest rate, and market capitalization) and fitting a VAR model to see if any of the features above has predictive significance on gold prices using the Granger criterion, and if it does we will test the fitted model's performance on a validation dataset. We will not do any adjustment in response to COVID as it is important to have models robust in the face of rapidly changing macroeconomic situations


Exploratory Analysis

First we grab each of our feature of interest then perform joins on the date, looking at the number of missing after performing the joins we get

So to deal with this we do simple filling with padding from the previous observation.

Now we can properly plot figure of all our series to get an idea how they look over time

Just by glancing at each of the series we can see only unemployment has a chance of being stationary with all other series displaying non-stationary behavior To fit a VAR model and use Granger's test we want our time series to be stationary and preferably our error term to be normally distributed. Using Augmented Dickey-Fuller Test to test the null hypothesis that our series is has unit root against the alternative that it is stationary.

Given an α=0.05\alpha = 0.05 as our p-value criterion for statistical significance we see we can only reject the null hypothesis of unit-root for unemployment series in favor of a stationary series. For the rest we will have to find find I(d),d>0I(d), d > 0, or the minimum number of differences to reach a covariance-stationary time series. To do so we difference our series once and twice to look at the p-values of I(1)I(1) under the ADF test.

Under I(1)I(1) the rest of our data now easily passes our α=0.05\alpha = 0.05 criterion, we leave unemployment is I(0)I(0).

The next step is to do a test of cointegration between the remaining variables, given they all have the same coifficient of integration. If two series are cointegrated they trend in tandem.

Here we will utilize Johansen's Test as it allows us to test cointegration with a basket of time series giving us more freedom in how we fit our future model. We will use the original dataset with an AR(1)AR(1) term in the test since I(1)I(1) processes can be represented as an AR(1)AR(1) process. The null hypothesis is that there is no cointegration with the alternative being that there is at least the series is cointegrated with at least one other series.

We can see above that voil,>α=0.05v_{oil}, > \alpha = 0.05 hence we reject the null in favor that oil prices is cointegrated with at least one other variable. The rest of the variables failing to reject the null hypothesis hence we retain the claim of no cointegration. Hence, as we used AR(1)AR(1) this implies at either oiltoil_t or oilt1oil_{t- 1} will trend with Xt=β1goldt+β2interestt+β3markett,βiRX_{t} = \beta_{1} gold_t + \beta_2 interest_t + \beta_3 market_t, \beta_i \in \R

Before moving to use Granger's test we want a clearer picture of the distribution of the series over time. Given all we found a stationary variant of all our series we can do a simple normality test. We will use Anderson Fuller's Test to achieve this goal. The null hypothesis being that the series is normally distributed

Above we see all test statistics are greater than the critical value. We below is a plot of our series so we can get a better picture.

As we see above while the data does behave similarly to a normal distribution both the skew and kurtosis seems to be far off from a normal distribution's. Below we perform a skew test and a kurtosis test to get an idea.

As kurtosis approaches infinity the data becomes concentrated at a single point. When you difference white noise you would expect a very high kurtosis. So with respect to our data the lower the kurtosis the easier it is to extract actionable signals.

Granger's Test and Fitting a model

Now that we have a general idea how well behaved our data is we can move towards actually fitting a model for forecasting gold prices. First we will perform Granger's test with the null hypothesis that the series, XtX_t, does not have a statistically granger casual effect on our series of interest, YtY_t. We will use our first differenced gold series as YtY_t and the rest of our stationary data as XtX_t

We see that only unemployment has granger casual relationship on first differenced gold value at α=0.05\alpha = 0.05 at lags 1 and 2. With only first differenced oil under having a debatable granger casual relationship on the of first differenced gold. Given the first differenced value of gold essentially means the movement between two timesteps of gold we can simplify the above statement to say that unemployment level has a statistically significant granger casual relationship on the movement of gold at lags 1 and 2 and the price movement of Oil having a debatable relationship at lags 1 and 2. Furthermore the movement of interest and market prices significantly fail to reject the null hypothesis and hence we fail to establish a granger casual relationship between them and the price movement of gold.

Now moving forward we can fit a VAR to see how well we can predict the gold movement. Given our data is stationary we can proceed with our analysis directly. Instead of using the strength movement forecast directly we can extract actionable information from just the direction of movement. Hence our error heuristics is if our model could correctly predict the direction of movement one step ahead. We will using a sliding window to gradually forecast 1 step ahead for 100 time steps.

Above we can see we only predicted the movement 41% of the time, which is not too good, this makes sense however, as it implies that there is more factors at play in the price movement of gold. This does however tell us that our model would not be sufficient as being able to predict movement of a time series better than random guessing is the base standard for a useful model.

If we utilize all of our stationary data rather than our Granger casual variables we get.

Above we see an improved ability in terms of prediction despite including variables that are significantly not granger casual. Hence we can stipulate that Granger causality is not a good criterion on fitting a model itself and further tests are required to fit a time series based on financial instruments.

References

  • http://www.scholarpedia.org/article/Granger_causality
  • https://en.wikipedia.org/wiki/Granger_causality
  • https://en.wikipedia.org/wiki/Johansen_test
  • https://en.wikipedia.org/wiki/Humean_definition_of_causality

Data Sources

  • https://fred.stlouisfed.org/series/