## Thursday, December 29, 2011

### Monetary Policy & Credit Easing pt. 4: More Independent Variable Definitions

Support for Depositary Institutions

This variable will account for the Federal Reserves support of Depository Institutions through direct lending to these institutions.  Support will be measured by how much the Fed made up for any shortfalls in Depository Institutions main source of cash- time and savings deposits. Federal Reserve support for our first estimation period is operationalized as follows:

Support_{t}={TotalBorrowingFed^{DI}_{t}\ Total Time & Savings Deposits_{t}^{DI}}x 100

Where,
Support_{t}= Fed funds at depository institutions as a percentage of their main financing streams (total savings and time deposits) at time, t

TotalBorrowingFed^{DI}_{t}= Total Borrowings of Depository Institutions from the Federal Reserve (BORROW) at time, t

Total Time & Savings Deposits_{t}^{DI}= Total Time and Savings Deposits at All Depository Institutions (TOTTDP) at time, t

For our second estimation period from 4/1/01 to 4/1/11 we will use a different variable that excludes time-deposits as the series that we would ideally like to use above was discontinued in 2006.

Support_{t}={TotalBorrowingFed^{DI}_{t}\ Total Savings Deposits_{t}^{DI}}x 100

Where,
Support_{t}= Fed funds at depository institutions as a percentage of their main financing stream (total saving deposits) at time, t

TotalBorrowingFed^{DI}_{t}= Total Borrowings of Depository Institutions from the Federal Reserve (BORROW) at time, t

Total Savings Deposits_{t}^{DI}= Total Savings Deposits at all Depository Institutions (WSAVNS) at time, t

The expected beta coefficient should be positively related to short-term risk premia, as tighter credit conditions require Depository Institutions to go to the Fed for help.  Only after the risk-premia goes up and these institutions have no where else to go do they borrow from the Fed at the discount rate.

We expect the effect of lending support to depository institutions to be positively related to short-term risk premia, therefore:

H_{0}: ß ≤ 0 vs. H_{a}: ß > 0

Furthermore, we expect Fed support to depository institutions to have a negative effect on long-term risk premia because of the expectations component.  As the Fed steps in with lending support, markets calm their fears about the future.  This is directly the opposite of short-term risk premia as the support in that situation is in direct response to the risk premia. Therefore,

H_{0}: ß ≥ 0 vs. H_{a}: ß < 0

Data Issues

The following time-series data were provided by FRED and the details are as follows:

(a) Total Borrowings of Depository Institutions from the Federal Reserve (BORROW), Monthly, Not Seasonally Adjusted, 1919-01-01 to 2011-10-01

(b) Total Savings Deposits at all Depository Institutions (WSAVNS), Weekly, Ending Monday, Not Seasonally Adjusted, 1980-11-03 to 2011-10-17

(c) Total Time and Savings Deposits at All Depository Institutions (DISCONTINUED SERIES) (TOTTDP), Monthly, Seasonally Adjusted, 1959-01-01 to 2006-02-01

The Federal Funds Rate

The motivation behind putting the Federal Funds rate into our regression model is simple.  This is the main policy tool that the Fed has used to manipulate short-term credit conditions and influence the rate of inflation.  The Fed controls this rate in hopes of influencing other rates such as the Prime Bank Loan Rate along with other short term credit instruments like Commercial Paper. Additionally, this variable is very easy to account for because it requires virtually zero manipulation.  Notationally we will define it in the following manner:

FF_{t}= Federal Funds rate at time, t

The expected beta coefficient should be positively related to the short-term risk premia and negatively related to long-term risk premia.  They theory is that by lowering the federal funds rate, or the rate at which banks lend to each other, the Fed is encouraging banks to lend and thus ease credit conditions.  When the Fed feels that tightening is appropriate, maybe the result of a jump in inflation expectations or an deep acceleration in the economy, they respond by raising the federal funds rate.  This tightens credit conditions and thus theoretically at least, should result in an increase in short-term risk premia. The opposite holds true for the federal funds rate effect on long-term risk premia.  Since long-term rates are a function of shorter-term rates, investors are inclined to sell Treasuries which decreases the spread between Aaa and 10 year nominal treasuries, therefore decreasing long-term risk premia.

For short-term risk premiums we expect the federal funds rate to be positively related:

H_{0}: ß ≤ 0 vs. H_{a}: ß > 0

For long-term risk premiums we expect the federal funds rate to be negativity related:

H_{0}: ß ≥ 0 vs. H_{a}: ß < 0

Data Issues

We get the series for the federal funds rate from FRED and the details are as follows:

(a) Effective Federal Funds Rate (FF), Weekly, Ending Wednesday, 1954-07-07 to 2011-10-19

Interest Paid On Excess Reserves

This variable is also easy to account for because the introduction of interest paid on reserves has a direct impact on the physical quantity of excess reserves of depository institutions held at the Federal Reserve.  The motivation behind this variable is that banking institutions would not hold excess reserves with the Fed without some compensation i.e. that opportunity cost has to be greater than zero.  That is where the interest paid on excess reserves come in.  Without it there is an opportunity cost of letting the reserves sit with the fed instead of seeking more profitable safe havens for their cash. That is why we will be using the quantity of excess reserves as our explanatory variable and notationally defining it as follows:

ER_{t}= Excess Reserves of Depository Institutions at time, t

When the Fed initiated its policy of paying interest on reserves it created an incentive for banks to shore up their finances with the Fed.  It gave the Fed a way to conduct large scale asset purchases without suffering inflationary consequences. As long as the reserves are held with the Fed, they cannot be inflationary therefore an increase in excess reserves at the Fed is contractionary. Additionally, since the Fed had not initiated the policy of paying interest on reserves until October of 2008 there was no incentive for banks to hold any excess reserve balances before then. That is why our two estimation periods must have two different hypothesis tests for this variable:

For our estimation covering the 4/1/71 to 7/1/97 time period the interest paid on excess reserves policy was non-existent and therefore:

H_{0}: ß = 0 vs. H_{a}: ß ≠ 0

In other words, we are looking to not reject the null hypothesis that beta is equal to zero.

For our second estimation covering the 4/1/01 to 4/1/11 time period the interest paid on excess reserves policy was in effect, if only for a short-time before the end of the sample and therefore:

H_{0}: ß ≤ 0 vs. H_{a}: ß > 0

Data Issues

We get the series from FRED and the details are as follows:

(a) Excess Reserves of Depository Institutions (EXCRESNS), Monthly, Not Seasonally Adjusted, 1959-01-01 to 2011-09-01

Control Variables: Accounting For Factors Outside of Monetary Policy

We must account for changes in the risk premia that aren't necessarily related to monetary policy.  These include things like market fear, corporate default risk and controlling for changes due to underlying fundamentals like Corporate Profits After Tax.

The Yield Curve

The motivation behind including the yield curve is due to its known predictive power of economic growth and recessionary risk. As recessionary risk increase investors are more likely to put their funds into the safest assets with the highest return.  Historically, this involves the purchase of longer-term Treasuries as they have zero default risk.  When a bond is purchased its price goes up, and its effective yield decreases so the slope of the yield curve or the spread between the most liquid and shortest maturity bond and the most liquid, longest maturity bond decreases or flattens. As this slope flattens we expect risk premia to increase for longer maturity and less liquid debt instruments. As the yield curve flattens we expect T-bills to be sold and longer-term Treasuries to be purchased.  This puts upward pressure on T-bill rates, thus narrowing the spread between Commercial Paper and T-bills and reducing the short-term risk premia.
The yield curve as we define it is the spread between the 10-Year Nominal Treasury Note rate and the 3-Month Nominal Secondary Market Treasury Bill rate.  Notationally,

YC_{t}=GS10_{t} – TB3MS_{t}

Where,

YC_{t}= Yield curve at time, t

GS10_{t}= 10-Year Treasury Constant Maturity Rate at time, t

TB3MS_{t}= 3-Month Treasury Bill: Secondary Market Rate at time, t

The beta coefficient for both of our estimation periods is expected to be negatively related to long-term risk premia. Therefore:

H_{0}: ß ≥ 0 vs. H_{a}: ß ≤ 0

The beta coefficient for both of our estimation periods is expected to be positively related to short-term risk premia. Therefore:

H_{0}: ß ≤ 0 vs. H_{a}: ß > 0

Data Issues

We get the two times-series data from FRED and the details are as follows:

(a) 10-Year Treasury Constant Maturity Rate (GS10), Monthly, 1953-04-01 to 2011-09-01

(b) 3-Month Treasury Bill: Secondary Market Rate (TB3MS), Monthly, 1934-01-01 to 2011-09-01

Stock Market Volatility

We will control for market fear by including a measure of stock market volatility into our regression model. We define volatility as follows:

Volatility_{t}= CBOE DJIA Volatility Index (VXDCLS) at time, t

The motivation behind this control variable is that the returns from owning stocks become more volatile in times of fear. Thus the risk premia on assets that aren't risk free like corporate bonds may increase in response to this market volatility.

This variables is literally only relevant in the regression on long-term premiums for our second estimation period. We cannot reasonably assume it has any effect on short-term risk premia because stock market volatility signals the fear of financial markets which leads to flight to safety into longer term treasuries not short-term commercial paper. The reason is that an investor probably couldn't even have the capital to buy commercial paper to begin with and secondly when fear strikes investors tend to pour their capital into longer term treasuries because they can pick up some extra yield. This would widen the spread between Aaa and Treasuries thus increasing the risk premium.  Therefore we include this variable only in our second estimation and its only real effect will be on the long-term risk premia.

For the regression over our second estimation period, increased volatility is expected to increase the long-term risk premium:

H_{0}: ß ≤ 0 vs. H_{a}: ß > 0

For the regression over our second estimation period, volatility is not expected to have an effect on short-term risk premia, therefore:

H_{0}: ß = 0 vs. H_{a}: ß ≠ 0

In other words, we are looking to not reject the null hypothesis that $\beta$ is equal to zero.

Data Issues

We get the times-series data from FRED and the details are as follows:

(a) CBOE DJIA Volatility Index (VXDCLS), Daily, Close, 1997-10-07 to 2011-11-02

Corporate Bond Default Risk

It would be prudent to control for the perceived credit default risk of the corporate bonds market. To do this we use the spread between the AAA and BAA rates bonds which would theoretically correspond to increased compensation for the risk of a default. The reason being we want to see how much the Fed's actions influence the risk premia and factor out movements in the spread that may incorporate other things like flat out default risk. Ideally we would like to use a Credit Default Swap Index to control for default risk as it would help us control directly for default risk and not other things like liquidity risk, but given the sample length data limitations we are forced to stick with what we've got.

The control variable we use for corporate default risk is the spread between Moody's rated Baa's and Aaa's. This is used because data exists for the full length of our desired samples and therefore is operationalized as follows:

Where,

BAA_{t}= Moody's Seasoned Baa Corporate Bond Yield at time, t

AAA_{t}= Moody's Seasoned Aaa Corporate Bond Yield at time, t

The beta coefficient on our corporate default control variable is expected to be positive in our regression on long-term rates since an increase in the spread between BAA and AAA would indicate that these bonds expected default risk would increase:

H_{0}: ß ≤ 0 vs. H_{a}: ß > 0

In our regressions on short-term risk premia, the expected effect of this control variable is effectively zero as this variable deals with longer-term interest rates not exactly pertinent to short-term financing like commercial paper or Treasury Bills:

H_{0}: ß = 0 vs. H_{a}: ß ≠ 0

In other words, we are looking to not reject the null hypothesis that $\beta$ is equal to zero.

Data Issues

For the AAA and BAA data we use FRED:

(a) Moody's Seasoned Aaa Corporate Bond Yield (AAA), Monthly, 1919-01-01 to 2011-09-01

(b) Moody's Seasoned Baa Corporate Bond Yield (BAA), Monthly, 1919-01-01 to 2011-09-01

Corporate Profits After Tax

As corporate profits after tax increase the risk premium on corporate bonds decrease.  This fundamentally negative relationship should be controlled for in our regression model.  Operationally, this is defined as:

CP_{t} = Corporate Profits After Tax at time, t

The beta coefficient on our CP control variable should be negatively related to our dependent variables.  This is because as corporate profits after tax increase the risk that they will renege on their debt obligations will decrease. This gives us the following test.

H_{0}: ß ≥ 0 vs. H_{a}: ß < 0

Data Issues

We get the times-series data from FRED and the details are as follows:

(a) Corporate Profits After Tax (CP), Quarterly, Seasonally Adjusted Annual Rate, 1947-01-01 to 2011-04-01

Keep dancin'

Steven J.

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