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Modified CDR: A Common-Use Proxy
for Business Cycle to the
Asymmetric Causality between the
Stock Returns and
Economic Growth
Yuan-Ming Lee
Kuan-Min Wang
T.T.Binh Nguyen
2016/7/12
李源明
王冠閔
阮氐清萍
朝陽科大-2008 研討會
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Motivation
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Economic Growth
Stock Returns
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Motivation
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Causality between the Stock Returns and
Economic Growth
linear model ?
non-linear model ?
The strength or weakness of business cycle
indicates the growing status of one economy.
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Motivation
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The asymmetry of output and economic
growth under different business cycler
regimes
Delong and Summers(1986)
Hamilton(1989), Hussey (1992)
Beaudry and Koop (1993)
Henry et al. (2004) Öcal (2006)
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Motivation
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Pedersen and Elmer (2003) found the
“business cycle” contains many aspects
including most economic activities
“business cycle” was applied as a
threshold variable of the nonlinear
threshold model
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Motivation
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The empirical model switched from
linear form to nonlinear form is the
common way used by researchers.
Tong (1978) and Tong and Lim (1980)
develop the threshold autoregressive
model (TAR).
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Motivation
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The threshold vector autoregressive
model (TVAR) is adopted when dealing
with multivariate models.
Tsay(1989, 1998), Hansen (1996, 1999),
Weise (1999), Chen et al. (2003),
Huang and Yang (2004) and Huang et
al.(2005)
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Motivation
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Original CDR
CDR being the indicator of business
cycle which is presented by Beaudry
and Koop (1993)
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Motivation
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Henry et al. (2004) apply the characteristics
of CDR to their empirical study
uses CDR as a switching factor of the regime
model for creating the nonlinear model.
find the significant lead of stock returns over
economic growth rates appears during
recession, which disappears during expansion.
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purpose
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The major aim of this study is
bringing CDR into the threshold
model
making CDR be a proper proxy variable
for business cycle as well as become a
common-use and simple threshold
variable.
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purpose
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We explore the correlation between
stock returns and economic growth
rates
MCDR which is used as the threshold
variable
To create the TVAR model.
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Model
• CDR=0 represents the expansive period
of economy
• CDR>0 indicates the recessional period
of economy.
• our new CDR as CDR3
• The CDR3 still maintains the original
characteristics of CDR
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Model
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CDR3 is no other than the CDR
enlarged bilaterally.
CDR3 not only retains the original CDR but
also quantifies the expansive state that has
formerly been zero.
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Model
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CDR3 values are normalized by its
standard deviation.
To avoid the confusions caused by After
the CDR3 multiplied by -1
“modified CDR”, aka MCDR:
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Model
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Threshold VAR Model –TVAR Model
Threshold variable is MCDR (qt-d)
t  ( A1 1 t i ) I (qt  d   )
 ( A2   2,i t i )(1  I (qt  d   ))  
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Empirical Results
Analyzing the nonlinear relationship
between Stock Returns and Economic
Growth
 This study uses the annual data of 25
countries from 1960 to 2003.
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Empirical Results
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Unit root test：
Table 1 reports the results of unit-root
tests.
All results of tests show that all
variables are I(0).
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Empirical Results
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The nonlinear test by Tsay (1998)
Table 2 presents the results of nonlinear
test for the bivariate model of 25
countries.
These results show it is allowable to use
the MCDR as a threshold variable and to
build the TVAR model.
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Empirical Results
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Table 3 reports the estimations of TVAR
model
The causality between the stock returns
and the economic growth rates.
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Empirical Results
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During expansion (regime 1)
there obviously exists a causal relation
between the stock returns and the growth
rates in 6 countries;
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Empirical Results
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During recession (regime 2)
21 countries display significantly the causal
relation between the stock returns and the
growth rates
none with negative sign.
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Conclusions
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In two regime TVAR framework
The positive causality between the stock
returns and the growth rates occurs
mostly during recession.
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Conclusions
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In 25 countries used for analysis, the
positive relation stock returns and the
growth rates is significant in more than
80% countries.
This result is very close to the arguments
of Mauro (2003) and Henry et al. (2004).
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Conclusions
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After modifying partly the CDR
equation
We obtain MCDR that is applicable to
time series data of different countries to
create their own threshold model
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Conclusions
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These empirical results provide indirect
evidences on the discernment of MCDR
which is sufficient to become a good
proxy for business cycle.
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Conclusions
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Four main contributions of MCDR
emerge：
1. MCDR is not only the good proxy
variable for business cycle but also
applicable to data of different countries.
2. MCDR can improve the defect of CDR .
3. MCDR is smoothly adopted into the
threshold model
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Conclusions
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Main contributions of MCDR emerge：
4. from academic perspective, this study
provides an easy and reliable proxy
variable for business cycle, MCDR.
It has a common use and is able to apply
as the threshold variable in threshold
model fitted for data of different countries.
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感謝您的聆聽
Thanks your attention
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