This paper studies empirically the link between financial markets volatility and primary placements of stocks and bonds for the US economy. We find that the impact of volatility on primary placements is not statistically significant.Financial risk, Primary placements

Enzo Dia

Fabrizio Casalin

English

Research Papers in Economics

 
DEPARTMENT OF ECONOMICS 
UNIVERSITY OF MILAN - BICOCCA 
 
 
 
WORKING PAPER SERIES 
 
 
Financial Market Volatility and Primary 
Placements 
 
Fabrizio Casalin, Enzo Dia 
No. 156 – April 2009 
 
 
 
 
 
 
 
 
 
 
Dipartimento di Economia Politica 
Università degli Studi di Milano - Bicocca 
http://dipeco.economia.unimib.it Financial Market Volatility and Primary
Placements∗
Fabrizio Casalina† , Enzo Diab
a Newcastle University Business School, Newcastle upon Tyne, NE1 7RU, UK.
b Department of Economics, Universit` a degli Studi di Milano Bicocca, Milan, Italy.
Abstract
This paper studies empirically the link between ﬁnancial markets volatil-
ity and primary placements of stocks and bonds for the US economy. We
ﬁnd that the impact of volatility on primary placements is not statistically
signiﬁcant.
Keywords: Financial risk; Primary placements
JEL classiﬁcation: G32
∗We thank Jacques Melitz, Luca Stanca and Patrizio Tirelli for useful comments.
†Corresponding author. Tel.: +44-0191-222-6850; Email: fabrizio.casalin@newcastle.ac.uk.
11 Introduction
The opinion that volatility of ﬁnancial markets has a negative impact on the
real economy lies at the heart of proposals such as the Tobin Tax. Following
Keynes (1936), many economists argue that uncertainty can negatively affect in-
vestment, pushing ﬁrms to delay the instalment of new capital.1 Nevertheless, the
impact of volatility on asset prices and, eventually, on real quantities, is essen-
tially an empirical issue, and not entirely settled. Poterba and Summers (1986)
show that changes in risk premia, reﬂecting volatility, have a modest impact on
stock prices, since risk premia are stationary. French et al. (1987), on the contrary,
ﬁnd that the expected market risk premium is positively related to the volatility of
stock returns. Their results are conﬁrmed by Chou (1988) who, by estimating the
volatilitybymeansofGARCHtechniques, ﬁndsthatvolatilityishighlypersistent.
IntwoseparatestudiesSchwert(1989,2002)suggeststhatstockmarketvolatil-
ity is a leading indicator for economic activity, with heightened volatility often
associated with recessions, and that clusters of high volatility are explained, to a
large extent, by technological shocks.
Overall the literature on volatility and returns suggests that clusters of high
volatilityarenormallyconcentratedinperiodsoflowreturns, andanticipatereces-
sions or other (technological) shocks affecting the real economy. On the contrary,
there is no clear-cut evidence that stock market volatility affects real variables
1Keynes’ argument is that while individual shareholders can liquidate their investments, the
society as whole cannot, so that ﬁrm managers are constrained by the short-term ﬂuctuations of
share prices due to speculative activity.
2and investment in particular. Indeed, there is no neat evidence that stock prices af-
fect investment. For instance, Chirinko and Schaller (1996) found that bubbles are
present in the US stock market, but they have no inﬂuence on investment, which is
insteaddrivenbyfundamentals;Blanchardetal.(1993)concludethat“marketval-
uation appears to play a limited role, given fundamentals, in the determination of
investment decisions.”2 The above mentioned literature has focused on the analy-
sis of stock prices and investment. But should any relationship between stock and
bond prices (and their volatility) and investment exist, this relationship must take
place through the impact of stock and bond prices on primary placements. Since
ﬁrms raise funds by issuing new shares and bonds, and the amount raised depends
on the value of the outstanding shares and bonds, through this channel stock and
bond prices can directly affect their investment decisions. We thus test empirically
if the volatility of stock and bond prices inﬂuences the amount of resources that
ﬁrms raise from ﬁnancial markets. The empirical analysis is conducted by taking
into consideration potential endogeneity between the two different sources of ex-
ternal ﬁnance. We model stock and bond market volatility as GARCH stochastic
processes and we ﬁnd that both volatilities do not have any signiﬁcant impact on
the issuance of stocks and bonds.
2Blanchard et al. (1993), p.132.
32 Dataset and Empirical Model
The dataset consists of US monthly aggregate data ranging from March 1973
to June 2006. The data on issuance of bonds and stocks are taken from the Statisti-
cal Supplement to the Federal Reserve Bulletin. Figures represent gross proceeds
of issues maturing in more than one year, and include both ﬁnancial and nonﬁ-
nancial corporations, and both private and public placements in the case of bonds,
while public placements only in the case of shares.3 All ﬁgures are deﬂated by
using the Consumption Price Index. The dataset includes also the series of the US
industrial production index, while the stock and bond market indices considered
are the Standard&Poor500 (S&P500) and the Lehman Brothers Corporate Bond
Index (LBCB).4
The basic relations we want to test involve linear relationships among the volumes
raised by means of primary placements of shares (St) and corporate bonds (Bt),
plus a set of predeterminated variables which includes stock (RS,t) and bond (RB,t)
market returns, their volatility (sRS,t and sRB,t), and the growth rate of the US in-
dustrial production index (yt). Stock and bond market returns are computed by
employing monthly S&P500 and LBCB. Stock and bond market volatilities are
modelled by ﬁtting ARMA-GARCH processes to the same series.5
3Figures exclude secondary offerings, employee stock plans, investment companies other than
closed-end, intra-corporate transactions, and Yankee bonds.
4These series are obtained from the FRED database at the Federal Reserve Bank of St Louis
and Datastream.
5The best ﬁtting models for monthly stock and bond market returns are respectively a
ARMA(1,1)-GARCH(1,1) and a ARMA(1,0)-GARCH(1,1). These speciﬁcations deliver stan-
dardized residuals and squared residuals not serially correlated. Moreover, ARCH LM tests sug-
gest the absence of GARCH effects in standardized residuals. To save space these results are not
4The structural form of our model is as follows:
DSt = a0+a1DBt +a2RS,t +a3sS,t +a4Dyt +
kS
å
i=1
ai+4DSt−i+eS,t (1)
DBt = b0+b1DSt +b2RB,t +b3sB,t +b4Dyt +
kB
å
i=1
bi+4DBt−i+eB,t (2)
where the inclusion of lagged endogenous variables has a twofold valence. First,
to be consistent (under the null that the parameters ai and bi are zero for i=1,..,4)
with their stationary nature, the sources of external ﬁnance are allowed to evolve
as mean reverting AR stochastic processes. Second, lagged dependent variables
can capture the effects of omitted factors. In fact, it is plausible to think that the
variables included in the model are not the only determinants of the sources of ex-
ternal ﬁnance. Moreover, this speciﬁcation of the model should ensure residuals
not serially correlated. The hypothesis we want to test is that no relationship exists
between issuance of stocks and corporate bonds and ﬁnancial market volatilities.
There are two potential problems related to this study. The ﬁrst is that the different
sources of external ﬁnance are non stationary. The second is their endogeneity. To
overcome the ﬁrst problem we ﬁrst-difference the two series.6 To investigate the
second problem we make use of a system version of the Hausman Test developed
by Revankar and Yoshino (1990). Results suggest that issues of stocks and cor-
reported.
6Both the Augmented Dickie-Fuller and the Philips-Perron tests suggest that when the series
are taken in levels, the null of unit root cannot be rejected at standard signiﬁcance levels. The null,
however, is strongly rejected when the series are considered in their ﬁrst differences. These results
are consistent for different speciﬁcations of the two tests.
5porate bonds are endogenously determined.7 As a result, empirical estimations of
eqs.(1) and (2) are carried out by means of Three-Stage Least Squares (3SLS).8
3 Results
The empirical estimates of the system of eqs.(1) and (2) are reported in Ta-
ble 1. The two regressions are initially estimated without any lagged dependent
variables. Then, in order to account for lagged dependent variables which might
become signiﬁcant, the two regressions are supplemented with lags of order one,
two, and so forth. Following this procedure it can be shown that eq.(1) includes
lagged dependent variables up to the fourth lag, while eq.(2) includes lagged de-
pendent variables up to the ﬁfth lag.9
Thevariablethatexertsthestrongestinﬂuenceonprimaryplacementsofshares
is the returns on S&P500 which are signiﬁcant at the 1% level. This result is
in line with the literature.10 The coefﬁcient associated with volatility is posi-
tive, but not statistically signiﬁcant. Consequently, the volatility of stock market
prices does not affect primary placements of shares. This surprising result can
be explained recalling that primary placements include not only IPOs of privately
ownedﬁrms, buttheyalsoincludetheissuanceofconvertiblebondsandcapitalin-
7To save space these results are not reported.
8The model has been supplemented with dummy variables to account for monthly seasonality,
the Stock Market Crash of October 1987 and the collapse of LTCM fund of November 1998.
9When lagged dependent variables of order higher than four and ﬁve are included, empirical
results show that these terms are not statistically signiﬁcant. Thus, the identiﬁcation process of the
two regressions implies kS = 4 and kB = 5.
10See, for instance, Lowry (2003) and Welch (2004).
6Table 1: 3SLS estimates of eqs.(1) and (2).
DSt = a0 +a1DBt +a2RS,t +a3sS,t +a4Dyt +å
4
i=1ai+4DSt−i +eS,t
a0 a1 a2 a3 a4 a5 a6 a7 a8 R2
-0.153 0.298 2.319 1.252 -2.521 -0.426 -0.304 -0.134 -0.147 0.336
(-2.071) (0.373) (3.598) (0.816) (-0.942) (-3.496) (-2.095) (-2.636) (-2.664)
Q(8)† = 8.543 (0.382) Q(16)† = 13.38 (0.645) BG‡ = 1.368 (0.209) W[ = 17.95 (0.458)
DBt = b0 +b1DSt +b2RB,t +b3sB,t +b4Dyt +å
5
i=1bi+4DBt−i +eB,t
b0 b1 b2 b3 b4 b5 b6 b7 b8 b9 R2
0.040 0.266 3.579 -1.806 -6.333 -0.743 -0.547 -0.396 -0.246 -0.205 0.534
(1.159) (1.537) (4.820) (-1.173) (-3.124) (-13.033) (-9.919) (-7.440) (-4.659) (-4.823)
Q(8)† = 5.718 (0.679) Q(16)† = 20.21 (0.211) BG‡ = 0.739 (0.657) W[ = 14.36 (0.705)
Notes: empirical estimates worked out for the period 1973:03-2006:06. T-statistics in parenthesis. † Ljung-Box
Q-statistics of standardized errors at lags 8 and 16. ‡ Breusch-Godfrey tests for serial correlation up to lag 8.
[ White tests for heteroscedasticity. P-values in parenthesis.
creases of public ﬁrms. While the literature suggests that decreasing stock returns
have a negative impact on IPOs, ﬁrms, in the aggregate, issue large amounts of
new shares and convertible bonds to raise capital when their ﬁnances are strained.
Thus, our results might suggest that following strong negative shocks, associated
with peaks in volatility, the amount of equity raised by means of capital increases
or convertible bonds raises, thus at least partially offsetting the reduced volumes
of IPOs. Moreover, peaks in volatility might have a retarded impact on primary
placements of shares. We investigate this hypothesis by supplementing eq.(1)
with lagged values of volatility and testing for their signiﬁcance. Empirical re-
sults, however, show no evidence of lagged responses.11 The empirical estimates
show similar results for the issuance of corporate bonds. They are mainly driven
by returns on LBCB, while the volatility of bond markets is not statistically sig-
11To save space these results are not reported.
7niﬁcant at standard signiﬁcance levels. Unlike for the issuance of stocks, however,
changes in the industrial production index have now a signiﬁcant negative impact
on the issuance of bonds.
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Financial Market Volatility and Primary Placements

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