firms. For instance, insurance companies sell their products to employees of other keiretsu firms. This complementary marketing may allow for cheaper marketing
costs and a more reliable source of revenue for keiretsu firms. As an example, Gerlach 1992 states that the employees of Japanese keiretsu commercial bank
clients often have their wages automatically transferred to savings accounts at the keiretsu commercial banks.
3. Model specification and research method
3
.
1
. The four factor return-generating model The objective is to examine the stock return sensitivity of Japanese horizontal
keiretsu financial firms to market returns, interest rates, an interest rate spread variable, and exchange rates. Except for life insurance companies, each of the
major financial firms in the six keiretsu have separately traded stock. We thus estimate the Eq. 1 GARCH four factor return-generating model using weekly
stock returns from January 14, 1986 through December 29, 1992, where R
jt
is the weekly return for financial firm j in week t including dividend reinvestment;
INMKT
t
is the innovation in the weekly return on the PACAP Japanese value- weighted equity market proxy including dividend reinvestment; INBD
t
is the innovation in the weekly return of the J.P. Morgan long-term government bond
index; INFX
t
is a weekly trade-weighted yen exchange rate return innovation, estimated using data from the J.P. Morgan economics department; DSPD
t
is the weekly, non-innovation change in the spread between the Japanese short-term
prime rate and the Japanese 3-month deposit rate; and o
1jt
is the error term modeled as an AR1-GARCH1,1 process.
9
R
jt
= b
01
+ b
11
INMKT
t
+ b
21
INBD
t
+ b
31
DSPD
t
+ b
41
INFX
t
+ o
1t
− u
1
o
1jt − 1
, h
jt
= k + d
1
h
jt − 1
+ a
1
o
t − 1 2
, o
t
= h
jt
e
t
, 1
where h
jt
is the conditional variance for the jth financial institution and e
t
is distributed normally and independently with a mean of 0 and constant variance.
The AR1-GARCH1,1 volatility model is adopted for this study to model the error terms. The lag order of the independent terms is one in accordance
9
Note that bond index returns will move in the opposite direction of interest rate changes. INBD is chosen for the bond return variable because it is the innovation in the J.P. Morgan 7 – 10 year maturity
bond price series return.
with the principle of parsimony. In the estimation of the volatility model, o
t
is the residual from a Yule-Walker estimation.
10
The Eq. 1 mean model is estimated for each of the individual Japanese financial firm stock return time-series as well as various keiretsu stock return portfolios
described later.
11
The long-term bond return is used because the liquidity of the Japanese long-term government bond markets is much greater than that of the
short-term government bond markets. No variables are orthogonalized in either model. Instead, innovations of the independent variables are calculated, in part,
because the innovations provide a more powerful result based on a market efficiency rationale.
12
This study uses a relatively long time-period when stock prices were less influenced by government regulators. Pettway et al. 1988 document an increase in
market efficiency for daily Japanese bank stock returns during the period 1984 – 1986 compared to 1982 – 1983. The period chosen is also included within the
1985 – 1996 Japanese monetary regime identified by Cargill et al. 1997.
3
.
2
. Description of keiretsu portfolio regressions We also test for sensitivity using portfolios of keiretsu financial firm stock
returns.
13
In particular, we estimate Eq. 1 for two different portfolios formed from
10
Yule-Walker estimation is used due to the inclusion of the additional lag 1 error term in the mean model. A GARCH volatility model is proposed for this study due to the indication of time-varying
heteroscedasticity for most of the stock return time series, based on Engle 1982 Lagrange multiplier tests, as well as the finding of time-varying heteroscedasticity by Engle and Ng 1993 for Japanese daily
stock returns.
11
The use of stock portfolios does not allow estimation of the complete effect of financial firm specific assetliability structures on risk. Thus, time-series of individual firm stock returns are used in some
regression. For example, because individual banks may have either net asset or net liability duration structures, the stock response of a positive asset duration bank may cancel that of a bank with a net
liability duration structure.
12
An appendix available from the authors describes the derivation of the ARIMA innovations used as independent variables. When available, only data previous to the forecast date are used to estimate
new models each year and to calculate one-step ahead forecasts. For instance, a model is estimated for the period January 5, 1986 to January 1, 1988. A one-step ahead forecast is then estimated and an
innovation calculated. The same model is used for the next one-step ahead forecast except the data period is moved up one week to cover the period January 12, 1986 to January 8, 1988. A new model is
estimated beginning with the start of the next year. This procedure allows for a more accurate measurement of the innovations than the use of model residuals as the innovation measures. The use of
residuals assumes that market participants use data which are not available to them in forecasting interest rate changes since the residuals are estimated using all the data. In the interest rate sensitivity
literature reviewed, only Kwan 1991 and Saunders and Yourougou 1990, use a similar procedure to that used in this paper. Kwan allows his period of model identification to extend much longer into the
past than the period, in which interest rate sensitivity is measured, which allows for the possibility of structural changes in his forecast models.
13
Saunders and Yourougou 1990 use a portfolio approach to compare market betas and interest rate betas between portfolios of American bank stock returns and those for industrial firm stocks using
ARIMA-derived forecast innovations.
firms in each keiretsu. The first portfolio includes only the city banks, trust banks and propertycasualty insurance companies Group A. The second portfo-
lio includes all keiretsu financial firms that had enough valid stock returns over the period of the study Group B.
14
All stock returns were weighted by the combined market value of equity and book value of debt of each firm. Appendix
A outlines the procedure used to generate the weighted-portfolio returns includ- ing an adjustment for cross-holdings.
One difficulty with interpreting the keiretsu portfolio results is that large Japanese life insurance firms are mutuals and thus do not issue traded equity.
This may influence the results of the portfolio regressions because any assetli- ability effects related to the life insurance firms may not be reflected fully in the
portfolio stock returns. Komiya 1990 states that life insurance companies, while holding significant equity and loan portfolios in specific keiretsu firms, have had
few life insurance employees transferred to these firms either as line employees or directors as compared to keiretsu city banks. Thus, they had little influence over
the management of these firms. He also states that the relationship of life in- surance companies to a keiretsu is weak, based partially on their mutual form of
corporate governance.
4. Results
