Financial Intermediary Leverage and Monetary PolicyTransmission

Zehao Li ∗

September 19, 2019

Abstract

The effects of monetary policy strongly depend on the leverage of the primary dealers.

Monetary policy is more effective when the primary dealers have higher share of equity in

their total assets. Moreover, shocks to the leverage of the primary dealers account for thirty

percent of the forecast error variance of real GDP, investment, and consumption in the U.S.

The leverage of the primary dealers is counter-cyclical, so it explains why monetary policy

is more powerful during expansions as found in the literature. A possible explanation of the

differential effect is the financial accelerator. Higher capital ratio amplifies the responses of

stock prices to monetary policy, and the responses of credit spreads are also stronger and more

persistent.

JEL E44, E52, G12

∗Department of Economics, University of Wisconsin-Madison. 1180 Observatory Drive, Madison, Wisconsin53706. E-mail: zli424@wisc.edu.

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1 Introduction

Modern New Keynesian monetary theory suggests that the central bank can affect macroeconomicquantities (GDP, consumption, investment, etc.) by controlling the short-term interest rate. In prac-tice, there is a rich set of asset returns that have significant impacts on business cycle fluctuations.Gertler and Karadi (2015) point out that corporate bond credit spreads are important predictorsof economic fluctuations. Financial accelerator theory also suggests that stock prices matter formacroeconomic outcome. Changes in these asset returns are related to changes in the short-terminterest rate when investors rebalance their portfolios to exploit arbitrage opportunities. It followsthat the investor’s portfolio choice decisions are central to the transmission of monetary policy.Given a change in the short-term interest rate, the subsequent changes in other asset returns de-pend on the willingness of the investors to shift their portfolios. Portfolio choice, in turn, dependson the leverage. Little is known about how the transmission of monetary policy depends on theleverage of the marginal investors and this paper tries to fill in the gap.

This paper focuses on the primary dealers as the investors. Primary dealers are a group of largeand sophisticated financial institutions including Goldman Sachs, JP Morgan, and Deutsche Bank.They are active in virtually the entire universe of capital markets. Notably, primary dealers are thecounterparties of the Federal Reserve Bank of New York in its implementation of monetary policy.Over the period 1960-2012, primary dealers account for 96% of total assets of the broker-dealerssector and 60% of total assets of all banks in the U.S.. A list of all primary dealers is provided inthe appendix. He et al. (2017) show that the primary dealers are marginal investors in many assetmarkets, including the equity, government bond, corporate bond, derivatives, commodities, andforeign exchange markets, in the sense that shocks to their leverage ratio is priced in cross-sectionalasset returns. Haddad and Muir (2018) show that shocks to their leverage ratio also predicts timeseries of aggregate asset prices. The primary dealers seem to be the natural candidates for mypurpose, because they directly interact with the Fed in OMOs and are also marginal in other assetclasses.

Leverage is an important factor in portfolio choice and asset pricing theory. When the leverageis high, the investor is exposed to more risk in the value of the portfolio, so it should trade morecautiously. Given the primary dealer’s pivotal role in the asset market, the leverage of the primarydealers sector contains rich information of the movements in asset prices. The amount that theprimary dealers rebalance their portfolios affects the amount of changes in the supply or demand

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in the asset markets, hence the changes in asset returns. The private sector makes consumption andinvestment decisions based on all the available asset returns in addition to the short-term interestrate. Therefore, the transmission of monetary policy should crucially depend on the leverage of theprimary dealers who are marginal in the financial markets. Following He et al. (2017), I use theequity capital-to-asset ratio (abbrev. capital ratio) of the primary dealers sector, which is simplyequal to 1/(1 + leverage).

This paper investigates how the effects of monetary policy on asset prices and macroeconomicquantities depend on the capital ratio of primary dealers. I proceed in two steps. First, I study theinstantaneous effect of monetary policy on stock prices. I exploit the availability of high frequencystock price data to study the changes in stock prices in short windows bracketing FOMC pressreleases. Higher capital ratio of primary dealers amplifies the responses of stock prices to unan-ticipated changes in the short-term interest rate. Interestingly, it seems that most of the changesin stock prices are due to expansionary monetary policy surprises. So when the Fed surprises themarket with a reduction in the Fed funds rate, stock prices rise more when the primary dealers arebetter capitalized. Second, I estimate a vector autoregression (VAR) model with capital ratio, infla-tion, GDP, investment, consumption, and the Fed funds rate. This step mainly aims to understandthe effects of a shock to the capital ratio in a dynamic framework. The impulse response func-tions (IRFs) show that an increase in the capital ratio raises GDP, consumption, and investment,but decreases inflation. The Fed funds rate barely responds to the capital ratio shock. Forecasterror variance decomposition shows that the capital ratio shock accounts for about thirty percentof the forecast errors in GDP, investment, and consumption in the long run. Then I augment theVAR with an interaction term of the capital ratio with the Fed funds rate. This step aims to inves-tigate how the capital ratio affects the transmission of monetary policy. In general, I find that theresponses of the macroeconomic variables to the interest rate shock are stronger when the capitalratio is high. The capital ratio in the sample is positively correlated with GDP, so the result impliesthat the monetary policy is more powerful in expansions. As a robustness check, I show that themonetary policy’s lack of power in recessions is not primarily driven by the recent Great Reces-sion, probably because the capital ratio during the Great Recession is not substantially differentfrom other recession periods. The results are also robust to reordering the variables in the VAR.

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2 Literature review

The equity capital ratio of the primary dealers is a novel variable in the literature. To the best of myknowledge, He et al. (2017) are the first to construct the variable. They use the variable to show thatthe primary dealers are the marginal investors in most asset markets. Shocks to the capital ratio ofthe primary dealers possess strong explanatory power of cross-sectional asset returns, while shocksto the capital ratio of other banks or the corporate sector don’t. This paper extends their study tothe dynamic effects of the capital ratio shocks on macroeconomic quantities.

This paper is part of the literature on the asymmetric effect of monetary policy. Tenreyroand Thwaites (2016) and Mumtaz and Surico (2015) employ different methods to show that theU.S. monetary policy is more effective in expansions. Cloyne et al. (2016) and Auclert (2017)attribute the asymmetry to household’s portfolio compositions and credit constraints. Eickmeieret al. (2016), Aastveit et al. (2013), Pellegrino (2017) and Caggiano et al. (2017) explain theasymmetry with the counter-cyclicality of uncertainty. This paper proposes the capital ratio of theprimary dealers as an alternative explanation of the asymmetric effect of monetary policy.

Sine the recent financial crisis, a fast growing body of literature has been trying to relate thefinancial market conditions with the macroeconomy. Gilchrist and Zakrajšek (2012) and Gilchristet al. (2014) show that the credit spread has strong predictive power of macroeconomic variables,and influences the effective supply of credit. The credit spread mirrors the financial conditions ofthe financial intermediaries. Gertler and Kiyotaki (2010) and Dou et al. (2017) provide reviewsof the literature on how the financial conditions of the intermediaries affect the macroeconomicquantities.

The financial intermediaries are crucial not only for creating credit, but also for determiningasset prices. He and Krishnamurthy (2012) He and Krishnamurthy (2013) Brunnermeier and San-nikov (2014a), and Brunnermeier and Sannikov (2016) show that the asset returns are nonlinearfunctions of the net wealth of the financial intermediaries. Importantly, the financial intermedi-aries are the marginal investors in the asset markets in these papers. This paper provides empiricalevidence to the intermediary asset pricing and macrofinance literature by showing that the capi-tal ratio of the intermediaries has strong impact on macroeconomic quantities, and influences theeffectiveness of the monetary policy.

The interacted VAR employed in this paper is based on Kilian and Vigfusson (2011) and Koopet al. (1996). The interacted VAR has been employed in the study of energy prices on macroeco-

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nomic variables (Kilian and Vigfusson (2011)) and the interaction between uncertainty and interestrate (Aastveit et al. (2013), Pellegrino (2017) and Caggiano et al. (2017)) to generate state depen-dent impulse responses. This paper is one of the first to adopt the interacted VAR to study howthe monetary policy transmission depends on the stochastic discount factor in the financial mar-ket.Monetary policy shock and stock prices

3 Data

The macroeconomic variables are obtained from the FRED database of St. Louis Fed. Inflation isthe first difference of the logarithm of the GDP deflator series (GDPDEF), GDP is the real GDPseries (GDPC1), real investment is the real gross private domestic investment series (GPDIC96),consumption is the real personal consumption expenditures series (PCECC96), and the Fed fundsrate the quarter-end effective federal funds rate (FEDFUNDS). GDP, real investment, and realconsumption are transformed to natural logs, and the inflation and interest rate are annualizedpercentage points.

Data for computing the capital ratio are obtained from CRSP/Compustat and Datastream. Ithank Asaf Manela for making the time series of capital ratio of the primary dealers available onhis website1. The capital ratio is computed as the sum of market equity values of the primarydealers divided by the sum of market equity values and book debt of the primary dealers:

ηt =∑i Market equityi,t

∑i(Market equityi,t +Book debti,t

)where ηt is the capital ratio at quarter t and i is a NY Fed primary dealer at time t. See Heet al. (2017) for a discussion of using this specification versus using a simple average of individualcapital ratios. All variables are observed quarterly, from 1970: q1 to 2017: q2.

Figure 3.1 plots the time series of the capital ratio along with the U.S. recession dates. Therecession dates are obtained from NBER’s website. In all recessions, the capital ratio drops. Infour out of the five recessions in the sample, the capital ratio drops to the minimum; and in all re-cessions since 1990, the capital ratio reach a local maximum during the peak prior to the recession.Table 1 shows the correlations between the capital ratio and the contemporaneous macroeconomic

1http://apps.olin.wustl.edu/faculty/manela/data.html

5

05

1015

Cap

ital r

atio

of p

rimar

y de

aler

s

1970q1 1980q1 1990q1 2000q1 2010q1 2020q1date

Figure 3.1: Time series of of the capital ratio ηt of the primary dealers. The capital ratio is definedas the total value of the market equity divided by the sum of the total value of market equityand book debt of the primary dealers. The data are downloaded from Asaf Manela’s website:Intermediary capital risk factor. The sample covers 1970Q1-2017Q2. Shaded areas are recessionsdates (peaks to troughs) according to NBER’s Business Cycle Dating Committee.

variables. It is clear that the capital ratio of the primary dealers is pro-cyclical (the leverage iscounter-cyclical).

Expected GDP growth, corporate profit growth, and T-bill rates are taken from Survey of Pro-fessional Forecasters published by Federal Reserve Bank of Philadelphia.

4 Instantaneous effect on stock prices

Stock market responds instantaneously to news. When the short term interest rate changes, in-vestors rebalance their portfolios to exploit the arbitrage opportunities. The extent to which in-vestors rebalance their portfolios depend on their wealth. Investors with abundant wealth are lessworried about hitting the constraints so are willing to trade large amount of securities, resulting in

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Table 1: Correlation with macroeconomic variables

summary stat. mean std. dev. min max8.40 2.53 -5.91 5.00

correlations inflation GDP investment consumption-0.43 0.19 0.14 0.30

Note. The table shows summary statistics of the capital ratioand its contemporaneous correlations with the macroeconomicvariables. The capital ratio is measured in percentage points.Inflation is measured as the first difference of the logarithm ofthe GDP deflator, GDP, investment, consumption are all mea-sured as the first difference of the logarithms of the real levels.

large swings in asset prices. This section confirms the conjecture that a given unit change in theshort term interest rate leads to larger changes in stock prices when the capital ratio of the primarydealers is higher.

Short term interest rate endogenously respond to changes in economic conditions, so the iden-tification hinges on exogenous changes in the short term interest rate that are not anticipated bythe market. The identification strategy exploits the changes in stock prices over short time inter-vals bracketing FOMC press releases. The underlying assumption is that the event window is soshort that the only source of change in the short term interest rate is the Fed’s decision. FollowingGorodnichenko and Weber (2016), the shock is computed as

vt =D

D− t( f ft+∆t+− f ft−∆t−)

where t is the time when the FOMC issues an announcement, f ft+∆t+ is the Fed funds futures rateshortly after the press release, f ft−∆− is the Fed funds futures rate shortly before the press release,and D is the number of days in that month. The term D/(D− t) adjusts for the fact that the Fedfunds futures settle on the average effective overnight Fed funds rate. I exploit the 30-minute (tight)and 60-minute (wide) event windows. The tight (wide) window starts 10 (15) minutes before thethe press releases are issued.

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4.1 Baseline model

I estimate the static regression

∆SPt = constant+αMPSt +β (CAPt×MPSt)+ εt , (4.1)

where t denotes the time of the FOMC press release, ∆SPt is the realized percentage return on theSP500 index over the event window, MPSt is the monetary policy surprise, and CAPt is the capitalratio of the primary dealers, normalized to zero mean. All variables are measured in percentagepoints. Notice that all variables are contemporaneous, and the subscript t serves only as an indexfor each observation. The full sample ranges from February 1994 to December 2009, excludingthe release of September 17, 2001, with a total of 137 observations.

As a benchmark, I first estimate the model without the interaction term. Table 2 reports theresults of the estimate. The first two columns use the pre-2008 subsample, and the last two columnsuse the full sample. It seems that monetary policy shocks have stronger effects on stock pricesin the pre-crisis sample, so I only present results based on the pre-crisis sample in the rest of thissection. Columns (1) and (2) are consistent with the results established in the literature: on average,a 25 basis point unanticipated cut in the interest rate leads to an increase in the SP500 by more than1 percent.

Table 3 presents the estimates of α and β in equation 4.1. In columns (1) and (3) the capitalratio the monetary policy surprise is interacted with the level of the capital ratio. In columns (2)and (4) the monetary policy surprise is interacted with a dummy variable which equals to one whenthe capital ratio is higher than its 50th percentile. The coefficient β on the interaction term is thefocus of this exercise. If it has the same sign with α (negative), then it implies that a higher capitalratio amplifies the effect of the monetary policy surprise on stock prices. The estimation resultsconfirm the conjecture. Within the tight event window, when the capital ratio is 1 percentage pointhigher than average, the effect of the monetary policy shock on SP500 returns is amplified by 1.44percentage points. The effect is also statistically significant. Within the wide window, 1 basis pointsurprise in the short term interest rate has 1.32 percent additional impact when the capital ratio is1 percentage point above average.

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Table 2: Response of the S&P500 returns to monetary policyshocks.

(1) (2) (3) (4)wide, pre-2008 tight, pre-2008 wide, full sample tight, full sample

Wide surprise -4.91∗∗∗ -1.34(1.22) (2.66)

Tight surprise -5.14∗∗∗ -1.67(1.36) (2.93)

N 117 117 137 137adj. R2 0.274 0.332 0.009 0.020Standard errors in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

This table reports the results of regressing the realized percentage returns of the S%P500 in anevent window bracketing the FOMC press releases on the monetary policy shocks identified bythe changes in the federal funds futures rate in that event window. The wide (tight) window is60 (30) minutes and starts 15 (10) minutes before the press releases are issued. The full sampleranges from February 1994 to December 2009, excluding the release of September 17, 2001,with a total of 137 observations.

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Table 3: Response of the S&P500 returns to monetary policyshocks with interactions.

(1) (2) (3) (4)tight, pre-2008 tight, pre-2008 wide, pre-2008 wide, pre-2008

Surprise -3.38∗∗ -1.12 -3.44∗∗ -1.68(1.00) (1.33) (1.25) (1.77)

Surprise * capital ratio -1.44∗∗∗ -1.32∗∗

(0.42) (0.50)Surprise * high capital ratio -6.37∗∗∗ -5.26∗

(1.88) (2.16)N 117 117 117 117adj. R2 0.453 0.449 0.367 0.344Standard errors in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

This table reports the results of regressing the realized percentage returns of the S%P500 in anevent window bracketing the FOMC press releases on the monetary policy shocks identified bythe changes in the federal funds futures rate in that event window. The wide (tight) window is60 (30) minutes and starts 15 (10) minutes before the press releases are issued. The full sampleranges from February 1994 to December 2009, excluding the release of September 17, 2001,with a total of 137 observations. In columns (1) and (3) the monetary policy shock is interactedwith a continuous measure of capital ratio; in columns (2) and (4) the monetary policy shockis interacted with a dummy variable which equals to 1 when capital ratio is larger than its 50thpercentile.

10

−0.

60−

0.40

−0.

200.

000.

20M

onet

ary

polic

y su

rpris

e, %

1995q1 2000q1 2005q1 2010q1date

Figure 4.1: Monetary policy surprises identified in the tight event window. NBER recession datesare shaded.

4.2 Sign dependence

Are the results mainly driven by expansionary (negative) shocks, contractionary (positive) shocks,or both? Cieslak (2018) documents that investors make large and persistent errors in expectationsof the short term interest rate, and the largest errors arise in economic downturns and when Fedlowers the interest rate. Interestingly, the investors tend to overestimate future short rates in thosecases so the monetary policy surprises are negative. Figure 4.1 shows that the largest surprises areindeed negative and are in recessions. However, large negative shocks also exist in non-recessionperiods while positive shocks exist in recession periods. In this subsection I investigate whetherthe effects of the monetary policy surprises on stock returns depend on the signs of the surprises.

Table 4 reproduces the model without the interaction term. I split the pre-crisis sample into twosubsets: one with positive monetary policy surprises and the other with negative monetary policysurprises. Observations with zero monetary policy surprises are dropped. Over the 1994-2007period, the number of positive surprises and negative surprises are roughly comparable to each

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Table 4: Response of the S&P500 returns to monetary policyshocks conditional on signs.

(1) (2) (3) (4)wide- tight- wide+ tight+

Wide surprise -6.42∗∗∗ 0.81(1.39) (3.93)

Tight surprise -6.77∗∗∗ -1.12(1.73) (1.64)

N 51 52 39 42adj. R2 0.444 0.501 -0.024 -0.019Standard errors in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

This table reports the results of regressing the realized percentage returns of the S%P500 in anevent window bracketing the FOMC press releases on the monetary policy shocks identifiedby the changes in the federal funds futures rate in that event window. The wide (tight) windowis 60 (30) minutes and starts 15 (10) minutes before the press releases are issued. The sampleranges from February 1994 to December 2007, excluding the release of September 17, 2001.The total observations for wide and tight event windows are not equal because zero shocks areexcluded.

other, with a ratio of 4:5. Since the number of zero surprises are different event windows, the totalnumber of wide window surprises and tight window surprises are not equal. I run the regressionon the two samples separately. Columns (1) and (2) report the slope coefficient of the monetarypolicy surprise on the negative surprise sample. The magnitude of the slope coefficient is largerthan that estimated on the full sample. Within the negative event window, a 25 bps unanticipatedreduction in the Fed funds rate increases the S&P500 return by 1.7 percent. Within the wide eventwindow, a 25 bps unanticipated reduction in the Fed funds rate increases the S&P500 return by1.6 percent. On the contrary, the response of S&P500 return to positive monetary policy surpriseis not significantly different from zero. Within the tight event window, an unanticipated increasein the Fed funds rate decreases S&P500 return by 0.28 percent. Within the wide event window, anunanticipated increase in the Fed funds rate seems to increase S&P500 return by 0.2 percent butthe estimate is very noisy.

Table 5 presents the results of the model with interaction. Like in the full sample, capital

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Table 5: Response of the S&P500 returns to monetary policyshocks conditional on signs and capital ratio.

(1) (2) (3) (4)wide- tight- wide+ tight+

Surprise -4.42∗∗ -4.09∗ -0.99 -1.92(1.40) (1.62) (3.20) (1.75)

Surprise * capital ratio -1.04∗ -1.39∗ -1.52 -0.84(0.47) (0.52) (1.22) (0.79)

N 51 52 39 42adj. R2 0.507 0.624 0.016 -0.026Standard errors in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

This table reports the results of regressing the realized percentage returns of the S%P500 in anevent window bracketing the FOMC press releases on the monetary policy shocks identifiedby the changes in the federal funds futures rate in that event window. The wide (tight) windowis 60 (30) minutes and starts 15 (10) minutes before the press releases are issued. The sampleranges from February 1994 to December 2007, excluding the release of September 17, 2001.The total observations for wide and tight event windows are not equal because zero shocks areexcluded.

ratio amplifies the effects of monetary policy surprises but the effects are mostly due to negativeshocks. When the capital ratio of primary dealers is at the mean level, a 25 bps unanticipatedreduction in the Fed funds rate increases S&P500 return by more than 1 percent. Additionally, a 1percent increase in the capital ratio further amplifies the response of S&P500 return by 0.26 (0.34)percent depending on the width of the event window. Contractionary monetary policy surpriseshave limited effects on S&P500 returns. Intuitively, when the short term interest rate turns out tobe lower than expected, investors increase their demand for stocks. But higher portfolio weighton stocks exposes the investor’s wealth to larger risk. When the capital ratio is low, the investorsare more concerned about the downside risk, so they demand for less amount of stocks comparedto the states when the capital ratio is high. Therefore, the response of realized stock returns to anexpansionary monetary policy surprise is smaller when the capital ratio is low.

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4.3 Financial frictions v.s. expectations

Since the capital ratio of the primary dealers is closely related to asset prices which are forward-looking, it might be simply reflecting the market’s belief about future economic outcomes. Inthat case, financial frictions play little role and the response of stock returns to monetary policysurprises might depend on the market’s expectation of future dividend flows. In this section, Icompare the financial frictions channel with the expectation channel by estimating the followingregression:

∆SPt = αMPSt +β1 (CAPt×MPSt)+β2 (Fact×MPSt)+ εt , (4.2)

where Fact denotes the factors that measure the market’s expectation of future macroeconomicoutcome.

The measures of expectations are taken from Survey of Professional Forecasters. I take themean of forecasts for GDP growth and corporate profit growth as the measures for expectations.The survey is taken each quarter. In each survey conducted in quarter t, the forecasters are askedto forecast a set of macroeconomic variables for the following 4 quarters t + 1, . . . , t + 4. Sincethe survey is conducted before the macroeconomic data for quarter t are available, the forecastersare also asked to nowcast the variables for quarter t. In the regression, I include the nowcast, the4 forecasts, and the realized value of the variable in quarter t as measurements of the market’sexpectation.

Table 6 compares the dependence of the response of S&P500 to monetary policy surpriseson expected GDP growth and the capital ratio. In columns (1) and (3), I replace the interactionterm in equation 4.1 by interactions between the monetary policy surprise and the measures ofexpectation. The purpose is to investigate the individual effects of the expectation measures onthe transmission of monetary policy. In columns (2) and (4), I estimate equation 4.2 to run ahorse race between the expectation measures and the capital ratio. Columns (1) and (3) report theresults when the monetary policy surprise is interacted with expected GDP growth. Consistentwith the benchmark linear model, the slope coefficient on the monetary policy surprise suggeststhat a 25 bps unanticipated reduction in the short term interest rate raises the realized S&P500return by roughly 1 percent. Expectations of GDP growth don’t seem to amplify or dampen theresponses of S&P500 to monetary policy surprises. Adding an additional interaction term betweenthe monetary policy surprise and the capital ratio, columns (2) and (4) show the result of the horserace between expected GDP growth and the capital ratio. Controlling for expected GDP growth,

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Table 6: Comparing the state dependence on capital ratio andexpected GDP growth.

(1) (2) (3) (4)tight, pre-2008 tight, pre-2008 wide, pre-2008 wide, pre-2008

Surprise -3.71∗∗∗ -2.84∗∗ -4.39∗∗∗ -3.42∗∗

(0.98) (0.97) (1.16) (1.19)Surprise* est. GDP growth t -1.31 -0.72 -0.54 -0.20

(1.77) (1.53) (2.37) (2.13)Surprise* exp. GDP growth t+1 7.95∗ 3.28 2.93 -2.86

(3.54) (2.93) (3.39) (3.57)Surprise* exp. GDP growth t+2 -7.29 -4.29 -1.84 2.76

(4.97) (4.24) (6.57) (5.57)Surprise* exp. GDP growth t+3 -1.73 -2.55 4.23 2.22

(3.97) (3.50) (5.33) (4.62)Surprise * exp. GDP growth t+4 6.29 6.99 -2.71 -1.19

(5.55) (4.34) (5.33) (3.67)Surprise * actual GDP growth 0.29 0.44 0.42 0.77

(0.60) (0.51) (0.87) (0.75)Surprise * capital ratio -1.35∗∗ -1.75∗∗

(0.42) (0.63)N 117 117 117 117adj. R2 0.391 0.448 0.267 0.355Standard errors in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

This table reports the results of regressing the realized percentage returns of the S%P500 in anevent window bracketing the FOMC press releases on the monetary policy shocks identifiedby the changes in the federal funds futures rate in that event window. The wide (tight) windowis 60 (30) minutes and starts 15 (10) minutes before the press releases are issued. The sampleranges from February 1994 to December 2007, excluding the release of September 17, 2001.

1 percent increase in the capital ratio relative to the mean level increases the realized return onS&P500 by 1.35 percent for any given unit of expansionary monetary policy surprise within thetight event window, and 1.75 percent within the wide event window.

Table 7 compares the dependence of the response of S&P500 to monetary policy surprises onexpected corporate growth and the capital ratio. Similar with expected GDP growth, there is little

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Table 7: Comparing the state dependence on capital ratio andexpected corporate profit growth.

(1) (2) (3) (4)tight, pre-2008 tight, pre-2008 wide, pre-2008 wide, pre-2008

Surprise -2.87∗ -2.17∗ -4.96∗∗∗ -4.23∗∗

(1.15) (1.08) (1.27) (1.27)Surprise * est. corp. profit growth t 0.45 0.45∗ -0.13 -0.12

(0.27) (0.22) (0.27) (0.23)Surprise * exp. corp. profit growth t+1 -0.04 -0.25 0.27 0.02

(0.44) (0.37) (0.48) (0.43)Surprise * exp. corp. profit growth t+2 -0.12 -0.39 0.54 0.30

(0.56) (0.54) (0.74) (0.69)Surprise * exp. corp. profit growth t+3 1.14∗ 0.97∗ 0.72 0.58

(0.51) (0.44) (0.57) (0.53)Surprise * exp. corp. profit growth t+4 -1.04 -0.42 -1.53 -0.96

(0.74) (0.73) (0.98) (0.92)Surprise * actual corp. profit growth -0.02 -0.00 -0.02 0.01

(0.04) (0.03) (0.05) (0.05)Surprise * capital ratio -1.10∗∗∗ -1.09∗∗

(0.27) (0.41)N 117 117 117 117adj. R2 0.441 0.476 0.330 0.361Standard errors in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

This table reports the results of regressing the realized percentage returns of the S%P500 in anevent window bracketing the FOMC press releases on the monetary policy shocks identifiedby the changes in the federal funds futures rate in that event window. The wide (tight) windowis 60 (30) minutes and starts 15 (10) minutes before the press releases are issued. The sampleranges from February 1994 to December 2007, excluding the release of September 17, 2001.

evidence that expected corporate profit growth amplifies or dampens the responses of S&P500returns to monetary policy surprises. Controlling for expected corporate profit growth, a 1 percentincrease in the capital ratio amplifies the response of S&P return to any given level of expansionarymonetary policy shock by 1.10 percent within the tight event window, and 1.09 percent in the wideevent window.

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It does not seem that expectations of future dividend flows have any significant impact on theresponses of S&P500 returns to monetary policy surprises. Instead, an increase in the capital ratiosignificantly amplifies the effects of the monetary policy shocks on stock returns. I take it assupportive of the financial frictions channel as opposed to the expectation channel.

4.4 Household wealth v.s. intermediary wealth

In traditional business cycle models, the financial sector is only an accounting device and its wealthhas no explicit impact on asset prices. Households are marginal in the asset markets and theirconsumption-to-wealth ratio captures risk aversion in the market so is crucial for determining assetprices. If the capital ratio of primary dealers simply reflects risk aversion in the market, we shouldexpect the consumption-to-wealth ratio of households to have similar effects on the responses ofstock returns to monetary policy surprises. I investigate whether household’s consumption-to-wealth ratio has any significant impact on the responses of S&P500 to monetary policy surprises.Although household consumption-to-wealth is not observable, Lettau and Ludvigson (2001a) showthat the deviation from the shared trend in consumption c, labor income a, and asset wealth y

captures salient predictive power of the consumption-to-wealth ratio for stock returns.Denoting the deviation from trend as cay, Table 8 presents the impact of cay on the responses

of S&P500 returns to monetary policy surprises. Columns (1) and (3) shows that the interactionbetween the surprise and cay is not statistically significant and the magnitude of the coefficientis also small. Adding the interaction between the monetary policy surprise and the capital ratio,columns (2) and (4) shows that the higher capital ratio significantly amplifies the responses ofstock returns to monetary policy shocks but cay still has no significant effects. The magnitudes ofthe coefficients on monetary policy surprise and its interaction with the capital ratio are consistentwith the baseline results in Table 3. When the capital ratio is 1 percent above average, the responseof S&P500 return to any level of monetary policy surprise is 0.35 percent larger.

Recent research on intermediary asset pricing argues that households are not marginal in manysophisticated asset markets, and their marginal utility offers poor indication of asset price move-ments in those markets. However, stock market is relatively less intermediated and householdconsumption seems to perform well in explaining cross section and time series of stock returns(e.g., Lettau and Ludvigson (2001a)Lettau and Ludvigson (2001b)). Moreover, Haddad and Muir(2018) finds that the predictive power of cay does not vary across asset classes to the extent the

17

Table 8: Comparing the state dependence on capital ratio andexpected corporate profit growth.

(1) (2) (3) (5)tight, pre-2008 tight, pre-2008 wide, pre-2008 wide, pre-2008

Surprise -6.95∗∗∗ -3.48 -5.17∗ -2.63(1.63) (2.92) (2.61) (3.48)

Surprise * cay 1.28 0.06 0.20 -0.55(0.69) (1.37) (1.37) (1.59)

Surprise * capital ratio -1.43∗ -1.40∗

(0.64) (0.70)N 117 117 117 117adj. R2 0.350 0.448 0.268 0.366Standard errors in parentheses∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

This table compares the effects of household’s consumption-to-wealth ratio and capital ratio ofthe primary dealers on the responses of S&P500 to monetary policy surprises. The wide (tight)window is 60 (30) minutes and starts 15 (10) minutes before the press releases are issued. Thesample ranges from February 1994 to December 2007, excluding the release of September 17,2001, with a total of 117 observations. The variable “cay” denotes household’s consumption-to-wealth ratio, taken from Martin Lettau’s website. For details, see Lettau and Ludvigson(2001a).

intermediary wealth variable does, so cay indeed seems to be capturing time variations in aggre-gate risk aversion. The results in Table 8 suggest that the state dependence of the responses ofS&P500 returns to monetary policy surprises does not seem to be due to time varying aggregaterisk. I take it as another piece of evidence supportive of the financial frictions channel of monetarypolicy transmission.

5 Dynamic effects on macroeconomic quantities

Financial market and macroeconomic quantities are closely related. High stock prices prop upinvestment through many channels such as Tobin’s q, financial accelerator à la Bernanke et al.(1999) and Brunnermeier and Sannikov (2014b). If high capital ratio amplifies the effects of

18

monetary policy surprises on stock prices, it should also amplify the effects on macroeconomicquantities. More interestingly, macroeconomic variables feed back into asset prices so the effectsof monetary policy surprises should persist over time. In this section, I use vector autoregressivemodels to study the dynamic effects of monetary policy surprises. I first use a linear VAR model tounderstand the average response of the capital ratio to the monetary policy shock and the responsesof macroeconomic quantities to the capital ratio shock. I find that the capital ratio shock contributesto substantial amount of fluctuations in the macroeconomic variables over the business cycles.Then I add an interaction term between Fed fund rate and the capital ratio to investigate whetherthe dynamic effects of the monetary policy shock depends on the level of capital ratio. I find thatthe responses of macroeconomic quantities to the monetary policy shock are stronger and morepersistent when the capital ratio is higher. In the impulse response exercises, all shocks are one-standard-deviation positive shocks, and confidence levels are 95%. The confidence intervals areall computed by bootstrap method.

5.1 Linear VAR

The linear VAR is

Y t = A0 +p

∑l=1

AlY t−l +ut . (5.1)

Y =(capital ratio, inflation, GDP, investment, consumption, Fed funds rate)′, ordered as listed, andut ∼N (0,Ω) where Ω is a positive semi-definite matrix. The structural IRFs are identified byCholesky decomposition of Ω. The optimal order p is based on AIC, which gives p = 3. The or-dering is motivated by Ludvigson et al. (2017), who find that “uncertainty about financial marketsis a likely source of the fluctuations”. Financial uncertainty is likely to be correlated with shocksto leverage and intermediary risk-bearing capacity, so it is reasonable to order the capital ratio firstin the recursively identified VAR. As a robustness check, the capital ratio is ordered after all themacroeconomic variables but in front of the Fed funds rate. The results are similar.

Impulse response functions (IRF) Figure 5.1 plots IRFs to a one-standard-deviation shock tothe primary dealers’ capital ratio. The capital ratio increases by 0.7% when the shock hits, anddies out after 17 quarters. GDP and consumption show a hump-shaped response to the capitalratio shock, with peaks 10 quarters after the shock at 0.4%. Investment shows a larger response,increasing by 2% 5 to 10 quarters after the shock. Inflation initially drops by 0.2%, but then

19

5 10 15 20-0.2

0

0.2

0.4

0.6

0.8

1Capital ratio

5 10 15 20-0.4

-0.2

0

0.2

0.4Inflation

5 10 15 20-0.2

0

0.2

0.4

0.6

0.8GDP

5 10 15 20-1

0

1

2

3Investment

5 10 15 200

0.2

0.4

0.6

0.8Consumption

5 10 15 20-0.3

-0.2

-0.1

0

0.1

0.2

0.3Fed funds rate

Figure 5.1: IRFs to a capital ratio shock. The solid lines show impulse responses to a one-standard-deviation shock to the capital ratio. Dashed lines are 95% confidence intervals using bootstrapmethod. The unit of the vertical axis is percentage points, and the horizontal axis is quarters.

rises above trend by 0.1%, as a consequence of the increase in the macroeconomic quantities.Interestingly, the Fed funds rate barely responds to the capital ratio shock.

What affects the capital ratio? In particular, does the capital ratio respond to monetary policyshocks? Figure 5.2 shows the impulse responses of the capital ratio to a one-standard-deviationshock to each of the variables in the VAR. Besides the shock to the capital ratio itself, only theshock to consumption has statistically significant impact on the capital ratio. This evidence sug-gests that the fluctuation in the capital ratio in the sample is unlikely the result of the monetarypolicy shock, or shocks to other macroeconomic variables except for consumption. Instead, it ismostly driven by a shock that is attributable to itself and orthogonal to shocks to other variables.

Forecast error variance decomposition (FEVD) Figures 5.3 and 5.4 show the amount of fore-cast error variances due to the capital ratio shock and the interest rate shock, respectively. Thecapital ratio shock accounts for 30% of the forecast error variance of GDP, and almost 40% ofinvestment and consumption. On the contrary, the interest rate shock explains less than 10% of theforecast errors of the real variables. Neither of the shocks explains more than 10% of the forecasterror variance of inflation.

20

5 10 15 20-0.2

0

0.2

0.4

0.6

0.8

1Capital ratio to Capital ratio

5 10 15 20-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1Capital ratio to Inflation

5 10 15 20-0.2

-0.1

0

0.1

0.2

0.3Capital ratio to GDP

5 10 15 20-0.2

-0.1

0

0.1

0.2Capital ratio to Investment

5 10 15 20-0.4

-0.3

-0.2

-0.1

0

0.1

0.2Capital ratio to Consumption

5 10 15 20-0.3

-0.2

-0.1

0

0.1

0.2Capital ratio to Fed funds rate

Figure 5.2: IRFs of the capital ratio to different shocks. The solid lines show impulse responses to aone-standard-deviation shock to the respective variable. Dashed lines are 95% confidence intervalsusing bootstrap method. The unit of the vertical axis is percentage points, and the horizontal axisis quarters.

5 10 15 200

0.2

0.4

0.6

0.8

1Capital ratio

5 10 15 200

0.2

0.4

0.6

0.8

1Inflation

5 10 15 200

0.2

0.4

0.6

0.8

1GDP

5 10 15 200

0.2

0.4

0.6

0.8

1Investment

5 10 15 200

0.2

0.4

0.6

0.8

1Consumption

5 10 15 200

0.2

0.4

0.6

0.8

1Fed funds rate

Figure 5.3: FEVDs to the capital ratio shock. The solid lines show the fractions of forecast errorvariances of each due to the capital ratio shock, plotted against the number of quarters. Dashedlines are 95% confidence intervals using bootstrap method. The vertical axis is fraction (1 means100%), and the horizontal axis is time.

21

5 10 15 200

0.2

0.4

0.6

0.8

1Capital ratio

5 10 15 200

0.2

0.4

0.6

0.8

1Inflation

5 10 15 200

0.2

0.4

0.6

0.8

1GDP

5 10 15 200

0.2

0.4

0.6

0.8

1Investment

5 10 15 200

0.2

0.4

0.6

0.8

1Consumption

5 10 15 200

0.2

0.4

0.6

0.8

1Fed funds rate

Figure 5.4: FEVDs to the Fed funds rate shock. The solid lines show the fractions of forecast errorvariances of each due to the Fed funds rate shock, plotted against the number of quarters. Dashedlines are 95% confidence intervals using bootstrap method. The vertical axis is fraction (1 means100%), and the horizontal axis is time.

Historical decomposition (HD) Figure 5.5 depicts the contributions of each shock to the vari-ables in the VAR. Notably, the capital ratio shock (blue) accounted for substantial amount of thereal variables (GDP, investment, consumption) during the expansion prior to the Great Recession,as well as the expansion in the late 1980s.

5.2 Interacted VAR

The purpose of the this subsection is to investigate how the capital ratio of the primary dealersaffect the transmission of monetary policy.

The interacted VAR is the linear VAR (5.1) augmented by capital ratiot−l ×Fed funds ratet−l

(i.e. Y 1t−lY

6t−l) in each equation:

Y t = A0 +p

∑l=1

AlY t−l +p

∑l=1

Bt−lY 1t−lY

6t−l +ut . (5.2)

The linear terms and the interaction term have the same number of lags. The interaction termallows the IRFs to the interest rate shock to depend on the level of the capital ratio.

22

Capital ratio

Q2-71 Q4-76 Q1-82 Q3-87 Q1-93 Q3-98 Q1-04 Q3-09 Q1-15-6

-4

-2

0

2

4

6

8

Capital ratioInflationGDPInvestmentConsumptionFed funds rate

Inflation

Q2-71 Q4-76 Q1-82 Q3-87 Q1-93 Q3-98 Q1-04 Q3-09 Q1-15-6

-4

-2

0

2

4

6

8

Capital ratioInflationGDPInvestmentConsumptionFed funds rate

GDP

Q2-71 Q4-76 Q1-82 Q3-87 Q1-93 Q3-98 Q1-04 Q3-09 Q1-15-10

-8

-6

-4

-2

0

2

4

6

8

10

Capital ratioInflationGDPInvestmentConsumptionFed funds rate

Investment

Q2-71 Q4-76 Q1-82 Q3-87 Q1-93 Q3-98 Q1-04 Q3-09 Q1-15-40

-30

-20

-10

0

10

20

30

Capital ratioInflationGDPInvestmentConsumptionFed funds rate

Consumption

Q2-71 Q4-76 Q1-82 Q3-87 Q1-93 Q3-98 Q1-04 Q3-09 Q1-15-10

-8

-6

-4

-2

0

2

4

6

8

10

Capital ratioInflationGDPInvestmentConsumptionFed funds rate

Fed funds rate

Q2-71 Q4-76 Q1-82 Q3-87 Q1-93 Q3-98 Q1-04 Q3-09 Q1-15-6

-4

-2

0

2

4

6

8

10

Capital ratioInflationGDPInvestmentConsumptionFed funds rate

Figure 5.5: Historical decomposition of the macroeconomic variables. The bar of each color showsthe cumulative contribution of the respective shock to the variable observed at a particular date.

23

Following Kilian and Vigfusson (2011), the IRF of the interacted VAR is defined as

Iy (h,ε1,t ,ωt−1) = E(yt+h|ε1,t ,ωt−1)−E(yt+h|ωt−1) ,

where h is the horizon of the IRF, ε1,t is the structural shock, and ωt−1 is the information setat the time of the shock. In my setting, ωt−1 =

(yt−1, . . . ,yt−p

), consisting of the set of lagged

variables (not just the interacted variables) of the VAR. In the rest of the paper, I refer to ωt−1

as the “initial condition”, and t − 1 as the “initial date” of the IRF of the interacted VAR. Theconditional expectations are computed by simulations. The detailed procedures can be found inKilian and Vigfusson (2011), page 434.

The IRF depends on the realizations of Y t throughout the forecasting horizon (ωt−1 varies overtime), so the IRF is history-dependent. I plot the IRF conditional on each date in my sample beingthe initial date (resulting in 187 IRFs). Then I group the dates into two classes. IRFs correspondingto the dates in each class are then averaged to obtain the average IRF for that class. In this exercise,I am primarily interested in the “high capital ratio” v.s. “low capital ratio” classification. As acomparison, I also study the “normal times” v.s. “zero lower bound” classification. The IRFsaccording to the former classification show significant differences, but not according to the latterclassification.

Notice that the coefficients of the interacted VAR are independent of the state and time, andare estimated using the full sample. Therefore, the estimates of the coefficients are independentof the classification of the states. The state-dependent IRF given each initial condition is pinneddown by the estimates of coefficients, so it is also independent of the classification of the states.The classification only affects the average IRFs.

Why interacted VAR? The interacted VAR allows the IRF to depend on the realizations of theendogenous variables throughout the horizon. This feature is crucial for answering my question.The investors in the financial market make their investment decisions depending on the macroeco-nomic environment, so their capital ratio is endogenous to the system of macroeconomic variables.For example, suppose that the interest rate shock hits at time t. The real variables respond at t +1.The capital ratio, which is the result of the investors’ portfolio choice decisions, also endogenouslyresponds to changes in the real variables at t + 1. The change in the capital ratio changes assetprices hence real variables further in the future. Therefore, standard economic theory indicates

24

Figure 5.6: History-dependent IRFs to the Fed funds rate shock. The figure shows IRFs to a one-standard-deviation Fed funds rate shock at each date in the sample. Lighter color is associated withlater initial date.

that the endogenous changes in the capital ratio in response to changes in the real variables areimportant for the monetary transmission, even without capital ratio shocks throughout the horizonof the IRF. In simpler setups such as putting a dummy variable for high / low capital ratio statesin front of the slope coefficients, or the exogenous interacted VAR model in Aastveit et al. (2013),the endogenous response of the state variable (capital ratio) is absent.

Figure 5.6 depicts how the IRFs to a one-standard-deviation Fed funds rate shock depend onthe initial value. The IRFs are plotted for each date in the sample as the initial value. Since the IRFof the interacted VAR depends on the realizations of the interacted variables, the same shock hits atdifferent dates will generate different impulse responses. In the figure, the IRFs are listed along thetimeline according to the initial dates, with lighter color indicating later initial dates. A commonfeature shared by all variables is that the impulse responses are extremely strong when the initialdates are in the early 2000s, which coincides with the periods when the capital ratio reaches thelargest values in the sample.

The evidence from the history-dependent IRF suggests that high capital ratio enhances theeffect of monetary policy shocks. In order to illustrate this point in more detail, I classify thesample into two states: the “low capital ratio” state and the “high capital ratio” state. The “lowcapital ratio” state is defined as the state where the primary dealers’ capital ratio is below the 10th

25

percentile of the time series. The cutoff threshold is chosen such that “zero lower bound (ZLB)”period is exactly in the low capital ratio state. This state also contains periods in the previousrecessions, such as those in the early 1980s. The “high capital ratio” state includes the rest of theobservations. The IRFs with initial dates in each state are averaged to produce the average IRF inthat state.

Figure 5.7 plots the average IRFs to the Fed funds rate shock in the two states, together withthe 95% confidence intervals. The blue solid line is the point estimate of the average IRF in the“high capital ratio” state, while the red dashed line is the point estimate of the average IRF in the“low capital ratio” state. The Fed funds rate shocks in both states are of the same magnitude. It isclear that in the “high capital ratio” state the real variables have stronger impulse responses to theFed funds rate shock than in the “low capital ratio state”. The impulse responses in the “low capitalratio” states are not only weaker, but also statistically insignificant. Figure 5.8 further illustratesthe difference by showing the point estimates and the 95% confidence intervals of the difference inthe IRFs (“low state” minus “high state”). The differences in the responses of all real variables to aone-standard deviation Fed funds rate shock are significant at the 95% level. Interestingly, the Fedfunds rate itself has significantly different responses to the shock in high versus low capital ratiostates.

The response of the capital ratio seems to confirm the idea that the intermediaries invest morecautiously when their leverage is high. In response to a monetary policy tightening, the capitalratio decreases, so the leverage increases. Moreover, the peak response of the capital ratio haslarger magnitude when the capital ratio is high. This evidence suggests that the monetary policyshock has larger effect on the portfolio of the primary dealers when the capital ratio is high. Actingthrough the supply and demand in the asset markets, it implies larger impact on asset prices. InSection ??, I show VAR evidence that the monetary policy shock indeed has larger impact on creditspreads when the capital ratio is high.

Figure 5.9 compares the IRFs of the interacted VAR with the linear VAR (green starred). TheIRF of the linear VAR lies between the average IRFs of the interacted VAR in the two states. Alikelihood ratio test of the linear VAR against the interacted VAR strongly rejects the former ateven the 99% confidence level. Therefore, the evidence in Figure 5.9 suggests that if one uses theconventional linear VAR to estimate the dynamic effects of the monetary policy shock, she wouldunderestimate the impulse responses in the “high capital ratio” states, and overestimate the impulseresponses in the “low capital ratio” states.

26

Capital ratio

5 10 15 20-0.4

-0.2

0

0.2Inflation

5 10 15 20-0.2

0

0.2

0.4

0.6

GDP

5 10 15 20-1

-0.5

0

0.5

1Investment

5 10 15 20-4

-2

0

2

Consumption

5 10 15 20-1

-0.5

0

0.5

1Fed funds rate

5 10 15 20-0.5

0

0.5

1High capital ratioLow capital ratio

Figure 5.7: IRFs to the Fed funds rate shock, conditional on the initial date being a high capitalratio state v.s. a low capital ratio state. The threshold for the two states is the 10th percentile of thecapital ratio time series. The figure shows IRFs to a one-standard-deviation Fed funds rate shockwith 95% confidence intervals. Unit of the vertical axis is percentage points.

Is the ZLB causing the difference? A concern is that the “low capital ratio” state is intertwinedwith the “zero lower bound” period, when the conventional monetary policy is relatively ineffec-tive. Therefore, the fact that the monetary policy is less effective in the “low capital ratio” statemight be mainly caused by the zero lower bound. To investigate whether the ZLB period is theprimary cause of the difference in the monetary transmission, I redo the classification as “normaltimes” versus “ZLB period”. The history-dependent IRFs are averaged according to the new clas-sifications. Figure 5.10 plots together the average IRFs in normal times and the ZLB period. Thepoint estimates almost coincide with each other, though the lower confidence band for the ZLBperiod becomes wider 10 quarters after the shock. Figure 5.11 plots the bootstrapped difference inthe IRFs of these two periods. The differences in the IRFs to the interest rate shock are not signif-icantly different from zero, so there is little evidence that the ZLB is the primary driving force ofthe difference in the monetary transmission.

Summing up, the capital ratio of the marginal investors in the financial market has strong impacton the transmission of monetary policy. High (low) capital ratio enhances (reduces) the effect ofmonetary policy on real variables. This is a serious issue when the monetary authority wishes tostimulate the economy during recession because the capital ratio of the marginal investors is alsolow during recessions, reducing the power of the conventional monetary policy.

27

Capital ratio

5 10 15 20-0.5

0

0.5Inflation

5 10 15 20-0.5

0

0.5

GDP

5 10 15 20-1

0

1Investment

5 10 15 20-5

0

5

Consumption

5 10 15 20-1

0

1Fed funds rate

5 10 15 20-1

0

1

Figure 5.8: Test of the differences in the IRFs to the Fed funds rate shock, high capital ratio v.s. lowcapital ratio. The solid line shows the point estimate of the “low capital ratio” state IRF minus thepoint estimate of the “high capital ratio” state IRF. The shaded area is the 95% bootstrap confidenceinterval of the difference.

28

Capital ratio

5 10 15 20-0.4

-0.2

0

0.2Inflation

5 10 15 20-0.2

0

0.2

0.4

0.6

GDP

5 10 15 20-1

-0.5

0

0.5

1Investment

5 10 15 20-4

-2

0

2

Consumption

5 10 15 20-1

-0.5

0

0.5

1Fed funds rate

5 10 15 20-0.5

0

0.5

1High capital ratioLow capital ratioLinear VAR

Figure 5.9: IRFs to the Fed funds rate shock, nonlinear and linear. IRFs of the linear VAR andthe interacted VAR are plotted together. 95% confidence intervals for the interacted IRFs are alsoplotted.

Capital ratio

5 10 15 20-0.4

-0.2

0

0.2

0.4Inflation

5 10 15 20-0.4

-0.2

0

0.2

0.4

GDP

5 10 15 20-1.5

-1

-0.5

0

0.5Investment

5 10 15 20-6

-4

-2

0

2

Consumption

5 10 15 20-1.5

-1

-0.5

0

0.5Fed funds rate

5 10 15 20-1

0

1

2Normal timesZLB

Figure 5.10: IRFs to the Fed funds rate shock, conditional on the initial date being normal v.s.ZLB. The figure shows IRFs to a one-standard-deviation Fed funds rate shock with 95% confidenceintervals. Unit of the vertical axis is percentage points.

29

Capital ratio

5 10 15 20-0.5

0

0.5Inflation

5 10 15 20-0.2

0

0.2

GDP

5 10 15 20-1

0

1Investment

5 10 15 20-5

0

5

Consumption

5 10 15 20-2

0

2Fed funds rate

5 10 15 20-1

0

1

Figure 5.11: Test of the differences in the IRFs to the Fed funds rate shock, high capital ratiov.s. low capital ratio. The solid line shows the point estimate of the “low capital ratio” state IRFminus the point estimate of the “high capital ratio” state IRF. The shaded area is the 95% bootstrapconfidence interval of the difference.

30

6 Conclusion

The capital ratio of primary dealers has strong influence on the transmission of monetary policy.Monetary policy has larger effects on asset prices and macroeconomic variables when the capitalratio of the primary dealers is high. Due to the pro-cyclical behavior of the capital ratio, monetarypolicy seems to be more powerful in expansion periods of the business cycles. A main channelthrough which the capital ratio affects the monetary transmission is asset prices. Stock prices risemore following an expansionary monetary policy surprise when the capital ratio is high. The creditspreads in various credit markets also respond more strongly to monetary policy shocks when theprimary dealers have stronger balance sheets, giving the monetary authority more control overthe cost of financing. The results of this paper suggests that the portfolio choice of the tradingcounterparts of New York Fed has strong impacts on the magnitudes of the effects of monetarypolicy. It is interesting to look for direct evidence of the primary dealers’ portfolios when theytrade with New York Fed. I leave this for future research.

31

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A List of primary dealers

Table 9: List of primary dealers.

Primary dealer Start date End date Primary dealer Start date End date

ABN Amro 9/29/1998 9/15/2006 HSBC 5/9/1994 CurrentAubrey Lanston 5/19/1960 4/17/2000 Hutton 11/2/1977 12/31/1987BA Securities 4/18/1994 9/30/1997 Irving 5/19/1960 7/31/1989Banc One 4/1/1999 8/1/2004 Jefferies 6/18/2009 CurrentBank of America 5/17/1999 11/1/2010 JP Morgan 5/19/1960 CurrentBank of America 11/17/1971 4/15/1994 Kidder Peabody 2/7/1979 12/30/1994Bank of Nova Scotia 10/4/2011 Current Kleinwort Benson 2/13/1980 12/27/1989Bankers Trust 5/19/1960 10/22/1997 Lehman 11/25/1976 9/22/2008Barclays 4/1/1998 Current Lehman 2/22/1973 1/29/1974Barclays De Zoetre Wedd 12/7/1989 6/30/1996 LF Rothschild 12/11/1986 1/17/1989Bartow Leeds 5/19/1960 6/14/1962 Lloyds 12/22/1987 4/28/1989Bear Stearns 6/10/1981 10/1/2008 Malon Andrus 5/19/1960 11/24/1965Becker 11/17/1971 9/10/1984 Manufac. Hanover 8/31/1983 12/31/1991Blyth 4/16/1962 1/14/1970 Merrill Lynch 5/19/1960 2/11/2009Blyth Eastman Dillon 12/5/1974 12/31/1979 Merrill Lynch 11/1/2010 CurrentBMO 10/4/2011 Current MF Global 2/2/2011 10/31/2011BMO Nesbitt 2/15/2000 3/31/2002 Midland-Montagu 8/13/1975 7/26/1990BNP Paribas 9/15/2000 Current Mizuho 4/1/2002 CurrentBNY 8/1/1989 8/9/1990 Morgan Stanley 2/1/1978 CurrentBrophy, Gestal, Knight 5/8/1987 6/19/1988 NationsBanc 7/6/1993 5/16/1999BT Alex Brown 10/23/1997 6/4/1999 Nesbitt Burns 6/1/1995 2/14/2000BZW 7/1/1996 3/31/1998 Nikko 12/22/1987 1/3/1999Cantor Fitzgerald 8/1/2006 Current Nomura 12/11/1986 11/30/2007Carroll McEntee 9/29/1976 5/6/1994 Nomura 7/27/2009 CurrentCF Childs 5/19/1960 6/29/1965 Northern Trust 8/8/1973 5/29/1986Chase 7/15/1970 4/30/2001 Nuveen 11/18/1971 8/27/1980Chemical 5/19/1960 3/31/1996 NY Hanseatic 2/8/1984 7/26/1984

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Table 9: List of primary dealers.

Primary dealer Start date End date Primary dealer Start date End date

CIBC 3/27/1996 2/8/2007 Paine Webber 11/25/1976 12/4/2000Citigroup 6/15/1961 Current Paine Webber 6/22/1972 6/27/1973Continental 5/19/1960 8/30/1991 Paribas 5/1/1997 9/14/2000Country Natwest 9/29/1988 1/13/1989 Pollock 5/19/1960 2/3/1987Countrywide 1/15/2004 7/15/2008 Prudential 10/29/1975 12/1/2000Credit Suisse 10/13/1993 Current RBC 7/8/2009 CurrentCRT 12/22/1987 7/15/1993 RBS 4/1/2009 CurrentDaiwa 12/11/1986 Current REFCO 11/19/1980 5/7/1987Dean Witter Reynolds 11/2/1977 4/30/1998 Robertson Stephens 10/1/1997 9/30/1998Deutsche Bank 12/13/1990 Current Solomon Smith Barney 5/19/1960 4/6/2003Dillon Read 6/24/1988 9/2/1997 Sanwa 6/20/1988 7/20/1998Discount Corp. 5/19/1960 8/10/1993 SBC 3/29/1990 6/28/1998DLJ 3/6/1974 1/16/1985 Second District 6/15/1961 8/27/1980DLJ 10/25/1995 12/31/2000 Securities Groups 5/19/1960 6/5/1983Dresdner Kleinwort 5/8/1997 6/26/2009 Security Pacific 12/11/1986 1/17/1991Drexel Burnham 5/19/1960 3/28/1990 SG Americas 2/2/2011 CurrentDW Rich 5/19/1960 12/31/1969 SG Cowen 7/1/1999 10/31/2001Eastbridge 6/18/1992 5/29/1998 SG Warburg 6/24/1988 7/26/1995FI Dupont 12/12/1968 7/18/1973 Smith Barney 8/22/1979 8/31/1998First Boston 5/19/1960 10/11/1993 Souther Cal. S&L 6/7/1983 8/5/1983First Chicago 5/19/1960 3/31/1999 TD 2/11/2014 CurrentFirst Interstate 7/31/1964 6/17/1988 Thomson McKinnon 12/11/1986 7/7/1989First N.B. of Boston 3/21/1983 11/17/1985 UBS 12/7/1989 CurrentFirst Pennco 3/7/1974 8/27/1980 Weeden 6/17/1976 5/15/1978Fuji 12/28/1989 3/31/2002 Wertheim Schroder 6/24/1988 11/8/1990Goldman Sachs 12/4/1974 Current Westpac Pollock 2/4/1987 6/27/1990Greenwich 7/31/1984 4/1/2009 White Weld 2/26/1976 4/18/1978Harris 7/15/1965 5/31/1995 Yamaichi 9/29/1988 12/4/1997

Zions 8/11/1993 3/31/2002

36