Pan-Pacific Conference XXXIV. Designing New Business Models in Developing Economies

Variance Assessment in Growth Rates
Fahad Javed Malik
Monash University, Australia
fahad.malik@khi.iba.edu.pk
Mohammed Nishat, Phd
Institute of Business Administration (iba), Karachi – Pakistan
mnishat@iba.edu.pk
ABSTRACT
The paper investigates volatility in growth rate of
Pakistan’s economy, along with its major trading
partners in Asia; India and Japan and including
USA as benchmark economy. The volatility has
been measured using GARCH(1,1) model, the
standardized residuals were not normally
distributed. Then a two state Markov switching
model is estimated to capture the structural break in
the variance of growth rate of the four economies.
The variance plots of switching regimes largely
coincided with conditional variance plots of the
GARCH models.
INTRODUCTION
For Pakistan, trade represents a substantial share of
gross domestic product. The trading magnitude and
variance is highly dependent on rate of economic
growth in Pakistan. The average growth rate of an
economy can be emphasized on the economic
growth rates or growth in GDP per Capita, the
growth rate deviates from it mean depends upon
aggregate investment and capital accumulation
contributing towards economic growth. Discussing
the growth rates among the emerging markets, there
are other reason that leads to structural volatility due
to under efficient performance of economic and
financial institutions. High volatility in GDP, is also
associated to a high budget deficit, underdeveloped
financial institutions hence making
economy more sensitive to international capital
market fluctuations. Similarly, the volatility of per
capita GDP impacted due to liquidity issues in
financial markets and limited credit creation.
Therefore, high deviation in business cycle is
dependent on overall financial stability taking into
account the impact national debt level, saving
generation level and financial instrument operation
etc.
One of the major trading partners of Pakistan in
Asia are India and Japan, where India is classified
as newly industrialized economy and Japan is
termed as developed economy. The US growth rate
is also taken into account as the benchmark
economy to assess overall variance fluctuation in
GDP. The fluctuation in US
growth rate is transmitted to other economies based
on their level of dependence, the paper estimates
growth rate volatility among Pakistan and its trading
using GARCH model and then contrasted the results
by using a two state Markov switching model is
used to
capture the shifts. Over the time, industrialization,
globalization, improved technology, multinational
operations including out- sourcing plays an
important role in global trade.
The objective of this study is to evaluate variance in
the growth rate of Pakistan with relation to its major
trading partners. The rest of the paper is organized
such that section 2 presents the econometric
methodology and data followed by discussion of
results in section 3. The conclusion is presented in
section 5.
MODEL SPECIFICATION
The model is estimated using the maximum
likelihood method and the likelihood function is
constructed following [13]. The specification of
most commonly used models to evaluate overall
volatility or heteroscedasticity is the GARCH model
introduced by [5] and then [2] it was further
analyzed by [3]. Later, Hamilton and Susmel (1994)
introduced Markov Switching Autoregressive
conditional heteroscedasticity model (SWARCH)
that allowed ARCH process parameter to come
from one of the several different regimes. This
study confirmed that ARCH effects eventually dies
out, the simplified version is given as;
y t = σ St u t
u t = h t v t
v t ~ i. i. d. with Student t Distribution
2 2
h t = α 0 + α 1 u t−1 + α 2 u t−2
2
+ βd t−1 u t−1
Where σ St captures Markov switching variances
and the d t−1 is the dummy variable to capture the
leverage effects. In the above specification, ARCH
(2) process is used in the model within a given
volatility regime. When the ARCH effect dies out,
this suggest heteroscedasticity in GDP growth rate
can be modelled by enforcing Markov switching
variance model. Therefore, we considered a two
state Markov switching model:
y ~
t
N
2
(0,
t
)
σ t 2 = σ 1 2 S 1t + σ 2 2 S 2t
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