A Mark Sadowski post
In particular, we are going to add the yield of 10-Year Treasury Constant Maturity Securities (GS10) and yield of Moody’s Seasoned Aaa Corporate Bonds (AAA).
The first thing I want to do is to demonstrate that the monetary base Granger causes bond yields during the period from December 2008 through May 2015. Here is a graph of the natural log of SBASENS and of GS10 measured in percent.
And here is a graph of the natural log of SBASENS and of AAA measured in percent
The following analysis is performed using a technique developed by Toda and Yamamoto (1995).
Using the Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests I find that the order of integration is one for all three series. I set up two two-equation VARs in the log levels of the data including an intercept for each equation.
Most information criteria suggest a maximum lag length of two for the VAR that includes 10-year Treasury Bond yields as a variable. An LM test suggests that there is no problem with serial correlation at this lag length. The AR roots table suggests that the VAR is dynamically stable, and the Johansen’s Trace Test and Maximum Eigenvalue Test both indicate that the two series are not cointegrated at this lag length.
Most information criteria suggest a maximum lag length of five for the VAR that includes Aaa Corporate Bond yields as a variable. The LM test suggests that there is a problem with serial correlation, but this problem disappears when the lag length is increased to six. The AR roots table suggests that the VAR is dynamically stable at this lag length, and Johansen’s Trace Test and Maximum Eigenvalue Test both indicate that the two series are not cointegrated at this lag length.
Then I re-estimated the two level VARs with one extra lag of each variable in each equation. But rather than declare the lag interval for the two endogenous variables to be from 1 to 3 and 1 to 7 respectively, I left the intervals at 1 to 2 and 1 to 6 and declared the lag of each variable to be exogenous variables. Here are the Granger causality test results.
Thus, the results are as follows:
- I fail to reject the null that 10-year Treasury Bond yields does not Granger cause the monetary base, but I reject the null that the monetary base does not Granger cause 10-year Treasury Bond yields at the 5% significance level.
- I fail to reject the null that Aaa Corporate Bond yields does not Granger cause the monetary base, but I reject the null that the monetary base does not Granger cause Aaa Corporate Bond yields at the 1% significance level.
In other words there is strong evidence that the monetary base Granger causes bond yields, but not the other way around.
Since the monetary base Granger causes bond yields they should probably be added to our baseline VAR model. This is because, under these circumstances, we might expect shocks to the monetary base in the VAR model to lead to statistically significant changes in bond yields.
With 10-year Treasury Bond yields added to the baseline VAR model, most information criteria suggest a maximum lag length of three. However, an LM test suggests that there is problem with serial correlation at this lag length. Increasing the lag length to four eliminates this problem. An AR roots table shows the VAR to be dynamically stable.
With Aaa Corporate Bond yields added to the baseline VAR model, most information criteria suggest a maximum lag length of five. An LM test suggests that there is no problem with serial correlation at this lag length. An AR roots table shows the VAR to be dynamically stable.
The Johansen’s Trace Test and Maximum Eigenvalue Test both indicate that there exists one cointegrating equation at this lag length in both VARs. But this is expected, since we already have evidence that the monetary base is cointegrated with industrial production. This matter is addressed in greater detail in the three posts where the baseline VAR is developed.
I am using a recursive identification strategy (Cholesky decomposition), which is the dominant practice in the empirical literature on the transmission of monetary policy shocks. Such a strategy means that the order of the variables affects the results. For the four-variable VARs I am arranging the output level first, the price level second, the monetary policy instrument third, and the financial variable last in the vector. This ordering assumes that the Federal Open Market Committee (FOMC) sees the current output level and price level when it sets the policy instrument, and that the output level and price level respond to a policy shock with one lag, but that financial markets respond to a policy shock with no lag.
As before, the response standard errors I will show are analytic, since Monte Carlo standard errors change each time an Impulse Response Function (IRF) is generated. Here are the responses to the monetary base and bond yields in the four-variable VARs.
The instantaneous response of bond yields to a positive shock to the monetary base is positive, but statistically insignificant in both cases. This is followed by a statistically significant positive response in the third and fourth month. Furthermore a positive shock to 10-year Treasury Bond yields in month one leads to a statistically significant positive response in the level of industrial production in months three and four, and a positive shock to Aaa Corporate Bond yields in month one leads to a statistically significant positive response in the level of industrial production from months three through five.
The IRFs show that a positive 2.1% shock to the monetary base in month one leads to a peak increase in 10-year Treasury Bond yields of 0.13 percentage points in month four. In turn, a positive 0.17 percentage point shock to 10-year Treasury Bond yields in month one leads to a peak increase in industrial production of 0.19% in month ten.
The IRFs also show that a positive 2.2% shock to the monetary base in month one leads to a peak increase in Aaa Corporate Bond yields of 0.097 percentage points in month four. In turn, a positive 0.13 percentage point shock to Aaa Corporate Bond yields in month one leads to a peak increase in industrial production of 0.21% in month thirteen.
So Quantitative Easing (QE) raises 10-year Treasury Bond and Aaa Corporate Bond yields?!?
Yes, in fact Michael Darda has repeatedly shown that long-term Treasury yields have generally risen under QE programs in figures such as the following.
Confusingly, this runs counter to the stated objectives of the Federal Reserve’s Large Scale Asset Purchase Program (LSAP).
If the Federal Reserve purchases 10-year Treasury Bonds in large quantities that should drive the price of 10-year Treasury Bonds up and their yields down, right? Wrong!
That would be true if households or firms did that. But when the Federal Reserve increases the monetary base to purchase 10-year Treasury Bonds it increases expected Nominal GDP (NGDP), or expected aggregate demand (AD), and higher expected AD means higher inflation expectations, ceteris paribus.
Why might an increase in inflation expectations lead to an increase in bond yields? Well one reason would be that an increase in inflation expectations lowers the expected return for bonds causing the demand for bonds to decline and their demand curves to shift to the left.
This is almost certainly true in the case of 10-year Treasury Bonds, since it is unlikely that their supply would increase endogenously to an increase in expected NGDP. Rather, as NGDP increases it is more likely that the quantity supplied of 10-year Treasury Bonds will decrease, as Federal tax revenues increase, and Federal spending on social insurance, such as unemployment compensation, decreases, resulting in a decrease in the Federal deficit. Thus whatever increased demand that the Federal Reserve created for 10-year Treasury Bonds through its LSAPs was almost certainly more than counterbalanced by decreased demand by households and firms due to increased inflation expectations.
Moreover, there is another way that increased inflation expectations may lead to an increase in bond yields. For a given interest rate, when inflation expectations increases, the expected real borrowing cost falls, hence the quantity of corporate bonds supplied increases at any given bond price and interest rate. Thus an increase in inflation expectations causes the supply of corporate bonds to increase and the supply curve to shift to the right.
Furthermore, an increase in NGDP expectations may lead to an increase in the expected profitability of physical investment opportunities. The more profitable equipment and structure investments that a corporation expects it can make, the more willing it will be to issue bonds in order to finance those investments. Thus an increase in expected NGDP may cause the supply of corporate bonds to increase and the supply curve to shift to the right, resulting in a decrease in the price of bonds and an increase in their yields.
And, pointedly, increased nominal spending on equipment and structures probably means increased real output.
Next time I shall add domestic stock market indices to the baseline VAR. I suspect that the results will be less controversial than the results on long term interest rates probably are, but who knows?