EXCHANGE RATE VOLATILITY IN PAKISTAN'S POLITICAL CONTEXT

Abstract

Exchange rates play a crucial role as economic indicators in developing nations such as Pakistan, where their instability can have profound effects on economic stability. This study investigates the dynamics of asset returns, risk factors, and exchange rate volatility under different political systems, focusing on both daily and monthly frequencies. Utilizing daily and monthly time-series data on U.S. dollar exchange rates from 1981 to 2021, the research categorizes this period into three political eras: pre-autocracy (1988-1998), autocracy (1999-2008), and post-autocracy (2009-2022). The GARCH family of models is employed to analyze exchange rate volatility within these political contexts. The results indicate that pre- and post-autocracy regimes exhibit minimal or insignificant average daily returns, while monthly returns are positive and statistically significant. In contrast, autocracy regimes show zero or insignificant average returns in both daily and monthly exchange rates. The study identifies an asymmetric impact of positive and negative news across all regimes and frequencies, except for post-autocracy's monthly exchange rate, which displays symmetry. Notably, positive news has a more pronounced influence than negative news in all regimes, highlighting an asymmetry in news impact. However, the impact of positive and negative news is symmetric in the monthly exchange rate during post-autocracy. The study suggests a direct correlation between the magnitude of democracy and autocracy and heightened exchange rate volatility. Emphasizing the lower risk and higher returns of monthly exchange rates, it recommends that governments, investors, and traders focus on monthly investments, trading strategies, and policy formulation. The observed symmetry in the impact of disruptive events within the monthly exchange rate, particularly in the current era, holds promise for informed policy and decision-making.

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EXCHANGE RATE VOLATILITY IN PAKISTAN'S POLITICAL
CONTEXT
Dr. Saghir Pervaiz Ghauri
Professor
Department of Economics
Jinnah University for Women
Anum Hayat
Lecturer
Department of Economics
Jinnah University for Women
Noman Ahsan
Research Scholar
Department of Public Administration
University of Karachi
Abstract
Exchange rates play a crucial role as economic indicators in developing nations such as
Pakistan, where their instability can have profound effects on economic stability. This
study investigates the dynamics of asset returns, risk factors, and exchange rate volatility
under different political systems, focusing on both daily and monthly frequencies.
Utilizing daily and monthly time-series data on U.S. dollar exchange rates from 1981 to
2021, the research categorizes this period into three political eras: pre-autocracy (1988-
1998), autocracy (1999-2008), and post-autocracy (2009-2022). The GARCH family of
models is employed to analyze exchange rate volatility within these political contexts. The
results indicate that pre- and post-autocracy regimes exhibit minimal or insignificant
average daily returns, while monthly returns are positive and statistically significant. In
contrast, autocracy regimes show zero or insignificant average returns in both daily and
monthly exchange rates. The study identifies an asymmetric impact of positive and
negative news across all regimes and frequencies, except for post-autocracy's monthly
exchange rate, which displays symmetry. Notably, positive news has a more pronounced
influence than negative news in all regimes, highlighting an asymmetry in news impact.
However, the impact of positive and negative news is symmetric in the monthly exchange
rate during post-autocracy. The study suggests a direct correlation between the magnitude
of democracy and autocracy and heightened exchange rate volatility. Emphasizing the
lower risk and higher returns of monthly exchange rates, it recommends that governments,
investors, and traders focus on monthly investments, trading strategies, and policy
formulation. The observed symmetry in the impact of disruptive events within the monthly
exchange rate, particularly in the current era, holds promise for informed policy and
decision-making.
Keywords: Exchange Rate Volatility, GARCH Model, Political Systems of Pakistan.

114 Exchange Rate Volatility in Pakistan's Political Context
Introduction
Investigating the returns on financial market variables such as volatility of
exchange rates and stock market indexes has been a strong area of focus for the researchers
since they have a significant impact on the decision-making of an individual and the
progress of an economy of a country (Sánchez-Fung, 2003). Volatility models are utilized
to determine a nation's economic performance for risk management and portfolio
allocation. Because it shocks domestic and foreign investment, analysis of exchange rate
volatility (ERV) is a key factor in risk management, which is crucial for investors.
Furthermore, it is important within bilateral trade agreements for trading nations because
it catalyzes trade in positive and negative directions. Consequently, policymakers examine
exchange rate volatility to plan appropriate fiscal and monetary policies.
Pakistan is a developing nation, so looking into the performance and volatility of
the currency rate there and how it can impact the national economy is critical. A few
contributions have been made in this regard, one of which is by Mahmood et al. (2011),
who use the GARCH model to highlight exchange rate volatility and macroeconomic
variables in Pakistan and investigate the possibility that exchange rate volatility influences
trade openness, GDP, and growth rate favourably while adversely affecting foreign direct
investment. Shah et al. (2009) conducted a noteworthy study on Pakistan in which the
author examines the high exchange rate volatility in developing nations and the role
played by central banks in controlling these fluctuations to stabilize economic
performance. Furthermore, the central banks can increase exchange rate volatilities if the
central bank's intervention is continuous and persistent.
A few political scientists have also investigated the relationships between
international financial markets, namely currency markets and national politics. They used
various research methods to do this, and the findings provide valuable information on how
currency traders respond to politics in developing democracies. For example, Bernhard
and Leblang (2002) demonstrate that politics influences the link between spot and forward
currency prices during election campaigns, cabinet discussions, and government
dissolutions. The following researchers have studied the political economics of
developing nations:
Haggard and Macintyre claim that exchange rate volatility and democratization
in developing market nations are skewed indicators of future exchange rates. Frieden
(2002) found that average yearly depreciation rates and coefficient variation in currency
rates in fourteen OECD nations are influenced by electoral times and measures of the
relative size of specific economic sectors. Research on security markets and politics
complement each other.
Regarding exchange rate volatility, there is a wealth of work on generalized
autoregressive conditional heteroscedasticity (GARCH) and autoregressive conditional
heteroskedasticity (ARCH). Exchange rate data is subjected to symmetrical effects
analysis using GARCH and ARCH models. Additional GARCH variants are employed
for comprehensive analysis. These include Nelson's (1991) exponential GARCH
(EGARCH), ZakoŊan's (1994) and Glosten, Jagannathan, and Runkle's (1993) threshold

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GARCH (TGARCH), and Engle, Lilien, and Robins' (1987) GARCH in a mean
(MGARCH), which can be utilized to investigate asymmetric effects and leverage effects
(Y. et al., 2014). These variations have been used to examine the volatility of currency
rates in different nations. These GARCH model extensions enhance the study as bad news
tends to have a stronger or larger impact than good news.
The studies have adopted several different econometric techniques to explore the
volatility of exchange rates during autocracy and democracy. This research thesis will
focus on GARCH models to investigate the influence of autocracy and democracy (pre-
and post-autocracy) on the exchange rate of Pakistan.
Importance Of Study
Many studies have been conducted on the research topic internationally, but no
published research was found with context to Pakistan. This paper made a unique
contribution to the literature regarding Pakistan, being a unique attempt to explore the
volatility of the exchange rate and risk associated with this asset daily and monthly in
scenarios of different political systems.
Problem Statement
Exchange rate volatility is a major economic problem in developing countries
like Pakistan; therefore, it is essential to investigate the volatility and risk associated with
the exchange rate for U.S. dollars during the political system of democracy and autocracy,
along with the daily and monthly frequencies. (Mohsin et al., 2019).
Research Questions
1. What is the extent of exchange rate volatility in Pakistan on daily and monthly
frequencies, and how does it affect the attractiveness of holding this asset in
international trade and investments, considering associated risks and returns?
2. How do various political systems influence the magnitude of exchange rate volatility,
and can categorizing news as "bad" or "good" news help policymakers develop
effective strategies for exchange rate stability, leading to balanced net exports and
economic growth?
Objectives of the Study
Pakistan is a lower middle-income and labour-intensive economy and widely
depends on imports. The availability of capital resources in Pakistan is highly insufficient,
therefore continuously facing a budget deficit and relying on debts. On the other hand,
Pakistan is also facing a current account deficit due to an oriented economy. In this
scenario, the exchange rate and its volatility play a vital role in disturbing and stabilizing
the economy by increasing or decreasing the country's deficits.
Therefore, the objectives of this study are:

116 Exchange Rate Volatility in Pakistan's Political Context
• To investigate the volatility of the exchange rate in Pakistan during daily and monthly
frequencies to discover the viability of holding this asset and its risks and returns in
international trade and investments.
• To investigate the impact and magnitude of different political systems on the
volatility of exchange rates as bad news or good news so that policymakers can make
efficient policies for the stability of exchange rates, which leads to balanced net
export and economic growth.
Hypotheses
H1: The exchange rate during democracy is more volatile than autocracy in Pakistan.
H2: A more risk factor is associated with daily exchange rate returns than monthly.
H3: Autocracy has an impact on the volatility of the exchange rate as bad news, while
democracy has an impact as good news.
H4: Bad news has a greater effect than good news on the volatility of the exchange rate.
H5: There is a relationship between the magnitude of autocracy and the exchange rate
volatility.
H6: There is a relationship between the magnitude of democracy and the exchange rate
volatility.
Literature Review
In order to enhance the ability to predict outcomes and make informed future
decisions, a high level of precision in handling high-frequency data is essential. The
volatility of exchange rates holds significant implications for macroeconomic variables
such as capital flows and international trade flows. Consequently, there is a pressing need
to thoroughly examine and understand this volatility. Scholars, governments, and
policymakers are keenly interested in exploring and analyzing the behavior of volatility
to effectively control and manage the associated risks to economic growth.
An analysis conducted by Kantar (2021) delves into GARCH models, including
GARCH, GJR, and EGARCH, which are nonlinear models, testing their validity in the
context of exchange rate volatility in Turkey. The study's findings reveal that the GARCH
(1,1) model successfully elucidates the volatility of the exchange rate.
A study by KUTU et al. (2021) takes a nuanced approach by decomposing oil
prices into positive and negative shocks. Their results indicate that both positive and
negative oil shocks exert a symmetric impact on exchange rates, while political and
institutional factors introduce an asymmetric impact. The study suggests the establishment
of robust political institutions to foster good governance, accountability, and transparency,
ultimately preventing the negative consequences of oil price shocks on import costs.

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Research conducted by Quinn and Weymouth (2016) identifies the degree of
competitiveness in political institutions, rather than other attributes of democratic
institutions, as a key factor influencing the valuation of currencies. Democratic societies
with consumer-friendly policies tend to, albeit modestly, overvalue exchange rates. The
study also highlights that sustained currency depreciation leads to electoral repercussions
for incumbent governments, providing a disincentive for democratic governments to
engage in prolonged currency wars.
Bernhard (2015) investigates the impact of democratic political events on
currency markets by examining the link between forward and spot exchange rate markets.
The study concludes that the forward rate often provides a misleading impression of
potential future fluctuations in exchange rates, attributed to a risk premium sought by
currency traders for holding the currency.
Steinberg and Malhotra (2014) shed light on exchange rate manipulation, finding
it to be less frequent under civilian dictatorships compared to military and monarchical
dictatorships. Abdalla's work (2012) utilizing the panel GARCH model for 19 Arab
currencies captures volatility clustering and leverage impact, revealing unstable volatility
for some currencies and persistent volatility for others.
The asymmetrical EGARCH (1,1) results demonstrate a leveraging effect for
most currencies, indicating higher volatility in the next period following negative shocks.
Additionally, research suggests that exchange rate volatility is associated with reduced de
facto persistence, advocating for a more flexible exchange rate regime.
Statistical results based on the post-Bretton Woods era indicate a negative
association between democratic regimes and de facto exchange rate persistence. This
relationship strengthens with voter inclination toward domestic production and societal
groups' influence. Hayes et al. (2003) utilize the Markov regime-switching model to show
how political developments continuously alter the probability of different currency market
equilibriums in young and emerging economic democracies.
The study aids in determining the compatibility of financial globalization and
democratization. Domestic political institutions, as evidenced by Leblang's empirical
study (1999), suggest that political preferences influence the choice of exchange rate
regimes, with floating exchange rate regimes being more expected in democratic politics.
Reynolds (1983) further emphasizes the role of political change as a primary driver of
economic expansion, positing that consistent policies can propel Pakistan towards high
economic growth.
Methodology
The approach of this research study is carefully planned to examine the daily and
monthly frequencies of fluctuations in the U.S. dollar exchange rate in Pakistan under
different political regimes. The State Bank of Pakistan's historical exchange rate records,
which span from 1981 to 2021 and include both authoritarian and democratic periods of
Pakistan's political history, provide the basis of the core dataset used in this analysis. A

118 Exchange Rate Volatility in Pakistan's Political Context
logarithmic adjustment is used to minimize possible problems with heteroskedasticity,
which could potentially skew the research results and guarantee that the data is suitable
for thorough examination.
Generalized Autoregressive Conditional Heteroskedasticity (GARCH) fashions,
consisting of GARCH(p,q), GARCH-M(1,1), T-GARCH(1,1), and E-GARCH(1,1), form
the main basis of the have a look at's framework. These GARCH models are broadly
recognized in economic econometrics and provide a strong platform for dissecting
volatility dynamics inside economic time collection information. These models permit us
to discover the average returns and risk factors and provide a lens into the evolving nature
of volatility inherent in U.S. Dollar change price returns.
The study method takes a systematic method of statistics evaluation. Initially, it
entails visually identifying patterns of volatility clustering through conditional variance
graphs and straightforward data visualizations. This step gives an initial glimpse into the
inherent volatility shape of the facts.
The evaluation then includes a heteroskedasticity check with the Autoregressive
Conditional Heteroskedasticity (ARCH) method to find any prospective Autoregressive
Conditional Heteroskedasticity repercussions. This investigation is a vital phase in
determining whether or not conditional volatility is the inside of the facts required to
understand how the exchange rate behaves. This investigation is vital in determining
whether conditional volatility is present in the information required to recognize how the
exchange fee behaves.
After heteroskedasticity is set up, time-varying volatility and threat factors
related to returns on U.S. Dollar change fees are also tested by estimating GARCH
models. The selected models are tailor-made to analyze how unique political structures
affect the volatility of change costs, presenting valuable insights into the effects of
political regimes.
Both dynamic and static forecasting techniques are used to test the GARCH
models' resilience and dependability to confirm their capacity to provide precise insights
into exchange rate volatility throughout various political eras.
A normality check and a histogram test are used in the research's final ranges to
evaluate the model residuals' distribution. Comprehending the statistical properties of the
residuals is crucial, as it adds to the richness of interpretation for the study results. The
research technique is a thorough and systematic approach intended to offer insightful
information about the connection between political regimes and exchange rate volatility,
illuminating the benefits and drawbacks of currency exchange in Pakistan.

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Data Collection
Table 01: Data Sources
Series
Denoted by
Measure
Source
Type of variable
Exchange Rate
(daily &
monthly)
E.R.
Concerning
U.S. $
State Bank of
Pakistan
Univariate
This study aims to identify the volatility of the exchange rate of the U.S dollar
during autocracy and democracy regimes in daily and monthly frequencies for which the
exchange rate of the U.S dollar's series has been taken from the State Bank of Pakistan,
which spans from 1981 to 2021 as this period include both democracy and autocracy eras
of Pakistan. We split this period into three political systems: autocracy (1988-1998),
autocracy (1999-2008), and post-autocracy (2009-2022). Pre and post-autocracy are the
period of democracy in Pakistan. The log value of the variable in this study has been used
to solve the heteroskedasticity problem.
Models of Study:
General equation of the GARCH model:
󰇛󰇜 
 

󰇛󰇜 
 

󰇛󰇜 


󰇛󰇜󰇛󰇜

 󰈅
󰈅


 
 

 󰇛󰇜
To explore the interplay of average returns, risk factors, and time-varying
volatility concerning U.S. dollar exchange rates across varying political systems, analyses
were conducted at both daily and monthly intervals. Empirical methodologies, specifically
GARCH models as introduced by Bollerslev in 1986, were employed for this
investigation.

120 Exchange Rate Volatility in Pakistan's Political Context
Initially, the identification of volatility clustering was pursued through the
examination of conditional variance graphs and the categorization of series into simple
graphs. This preliminary step aimed to discern patterns indicative of volatility clustering
within the exchange rate returns.
Subsequently, a crucial prerequisite for GARCH model estimation, the
heteroskedasticity test ARCH, was applied using the Ordinary Least Squares (OLS)
method. This served the purpose of detecting the Autoregressive Conditional
Heteroskedasticity (ARCH) effect within the data, essential for subsequent GARCH
modeling. Additionally, the determination of lags in the ARCH effect assisted in the
decision-making process between employing ARCH and GARCH methodologies.
Thirdly, the GARCH model estimation was employed to quantify the risk factor
and ascertain time-varying volatility associated with exchange rate returns. This step
delved into the intricacies of the financial dynamics influencing the fluctuations in the
U.S. dollar exchange rates.
The stability of the established models was rigorously assessed through the
utilization of both dynamic and static forecasting methods. This comprehensive evaluation
aimed to ensure the reliability and robustness of the models across different scenarios and
time frames.
Finally, a histogram test was applied to assess the normality of the data,
providing insights into the distributional characteristics of the exchange rate returns. This
final step contributed to the overall understanding of the statistical properties of the data,
offering a holistic perspective on the empirical findings derived from the analyses
conducted throughout the study.
Results and Discussion
Table 1 reviews the descriptive information of exchange rate returns for the 31
years 1981-2022 on a daily and monthly basis. Regarding the daily exchange rate, the
mean of the log is 69.92. The standard deviation enlightens how distant observations are
from the average. However, skewness measures the degree of asymmetry of the series. In
our case, the skewness of the exchange rate is greater than 0, which means the data is
positively skewed, while the kurtosis measures the peakedness or flatness of the sample
distribution. The kurtosis value is more than 3 in our data, meaning the series is
leptokurtic. In addition, the Jarque-Bera test is used for testing the normality of each
variable with H0 of series to be normally distributed, and Table 1 shows that the p-value
of Jarque-Bera statistics for series is less than 0.05 (95% confidence), we may reject our
Ho means the series is not normally distributed. However, for monthly exchange rate data,
the mean of the log is 73.02. The skewness of the monthly exchange rate is greater than
0, which means data is positively skewed, while the kurtosis in monthly data is less than
three, meaning the series is leptokurtic. In addition, the Jarque-Bera test is used for testing
the normality of each variable with H0 of series to be normally distributed, and Table
1 shows that the p-value of Jarque-Bera statistics for series is less than 0.05 (95%
confidence), we may reject our H0 means the series is not normally distributed.

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Table 2: Descriptive Statistics for Exchange Rate
Daily Exchange Rate
Monthly Exchange Rate
Mean
69.90
Mean
73.02
Median
60.16
Median
60.20
Maximum
168.21
Maximum
216.82
Minimum
17.79
Minimum
18.26
Std. Dev.
36.16
Std. Dev.
42.48
Skewness
0.80
Skewness
0.91
Kurtosis
3.17
Kurtosis
3.28
Jarque-Bera
920.64
Jarque-Bera
57.84
Probability
0.00
Probability
0.00
Observations
8499.00
Observations
409.00
Figure 1
The phenomenon referred to as the ARCH effect in Figure 1 is discerned through
the identification of clusters. This effect manifests when substantial additional changes
amplify already existing large changes. Conditional variance operates at various stages
within the time series model, aligning with the plotted series. This observation holds true
on both a daily and monthly basis.
The outcomes of the Augmented Dickey-Fuller (ADF) test and Phillips Perron
unit root test for assessing the stationarity of daily and monthly exchange rates are detailed
in Table 2. The ADF test's p-value, being less than 0.05, signifies that daily and monthly
exchange rate returns exhibit stationarity at a 5% confidence interval, leading to the
rejection of the existence of a unit root. The Phillips Perron unit root test corroborates
these results for both frequencies, underscoring the robustness of the conclusions drawn.
Heteroscedasticity, or the ARCH effect, is observed at 1, 3, and 6 lags in
autocracy, as well as in both pre- and post-autocracy (democracy), as indicated by the
results of the ARCH test of heteroskedasticity in Table 3. The study establishes that
significant heteroskedasticity exists with a 5% confidence level, leading to the rejection
of the null hypothesis of no heteroscedasticity. The coefficients increase when using the
ARCH model at lag six, rendering the model economically impractical. Consequently, the
GARCH (1,1) model is deemed more appropriate due to its greater economic efficiency
compared to the ARCH (6) model.
The GARCH-M model's conditional mean is contingent on its own conditional
variance, depending on whether conditional variance or volatility better captures risk. This
model examines the risk behavior of exchange rate return and volatility. The conditional
mean function of the series may exhibit conditional variance (Engle, 1993). Table 4
presents two equations reflecting the model's output. The mean equation elucidates the
constant term C's coefficient, representing the average returns associated with exchange

122 Exchange Rate Volatility in Pakistan's Political Context
rate returns. In this context, daily exchange rate average returns for all political systems
are zero, but significant p-values imply the potential for increased returns.
Significantly, lag variables R-LPREAUTOCRACY (-1), R-LAUTOCRACY (-
1), and R-LPOSTAUTOCRACY (-1) illustrate how historical exchange rate values
influence current exchange rate values. The variance equation delineates the relationship
between risk volatility and the past square residual term. Both past conditional variance
terms (GARCH (-1)) and past square residual term (RESID (-1) 2) terms are statistically
significant at 5%, indicating that the volatility of past exchange rate returns significantly
impacts present volatility, and past square residual terms significantly affect risk volatility.
Additionally, under democratic political systems, monthly exchange rates yield
zero average gains. However, a statistically negligible p-value in autocracy suggests the
possibility of an increase in returns. The present exchange rate, larger than daily exchange
rate returns but statistically significant across all political systems, is influenced by the
previous month's exchange rates, as indicated by lag terms. The variance equation reveals
that the term representing the past square residual (RESID (-1) 2) is statistically significant
at a 5% confidence level. This study suggests that past square residual terms exert a
noteworthy influence on risk volatility, with a greater impact in monthly data than in daily
data. Despite statistically significant past conditional variance terms (GARCH (-1)) at 5%,
monthly data show smaller past square residual terms during and after autocracy,
suggesting that past square residual terms significantly affect risk volatility. With a 5%
significance level, the conclusion can be drawn that H2 has a higher risk component
associated with daily exchange rate returns, time variables, and time-correlating volatility
than monthly frequency.
Table 03: GARCH M (1,1)
Mean equation
Variance Equation
Average
Returns
Impact of past
exchange rate on
present exchange rate
Impact of Past square
residual term on
present risk volatility
Impact of Past
volatility on present
volatility
Daily Exchange Rate
During Pre
Autocracy
(1988-1998)
zero but
significant
Insignificant
Positive and
significant (14.8%)
Positive and
significant (69%)
During
Autocracy
(1999-2008)
zero but
significant
Negative and
significant (6%)
Positive and
significant (157%)
Positive and
significant (51.4%)
During Post
Autocracy
(2009-2022)
zero but
significant
Positive and significant
(31.8%)
Positive and
significant (22.1%)
Positive and
significant (66%)
Monthly Exchange Rate
During Pre
Autocracy
(1988-1998)
Significant
(0.3%)
Positive and significant
(39.9%)
Positive and
significant (137.6%)
Negative and
significant (7.1%)
During
Autocracy
(1999-2008)
insignificant
Positive and significant
(48.4%)
Positive and
significant (115.2%)
Positive and
significant (49.5%)
During Post
Autocracy
(2009-2022)
Significant
(0.3%)
Positive and significant
(25.1%)
Positive and
significant (43.6%)
Positive and
significant (65.6%)

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Volume 4, Issue 1, Page 113-131, June 30, 2023
The T-GARCH model has been employed to assess the asymmetries associated
with positive and negative news, as well as the differential impact of autocracy and
democracy on news. Given the substantial repercussions of these events on assets and the
behavior of asset holders, hypothesis H3, which explores the effects of autocracy and
democracy, has been subjected to empirical testing. Nevertheless, the central aim of the
T-GARCH model is to capture asymmetries related to positive shocks (good news) and
negative shocks (bad news) (R. Rabemananjara, 1993). Analogous to other GARCH
models, the T-GARCH model comprises two equations. The mean equation from Table 5
of the T-GARCH model indicates that the risk factor/conditional variance (GARCH (-1))
associated with the Pakistani exchange rate for the United States is statistically significant.
This implies that the exchange rate of the previous day and month has an impact on the
exchange rate of the current day and month, or that the risk factor influences the mean
exchange rate returns, except for monthly exchange rate returns during the pre-autocracy
period. Despite the positive but inconsequential coefficient of autocracy in both the pre
and post-autocracy (democracy) regimes, except for the daily exchange rate during the
pre-autocracy period, it is evident that autocracy benefits investors without influencing
the exchange rate during democracy. At the 5% or 10% significance level, H3 (which
posits that autocracy affects exchange rate volatility as bad news, while democracy has an
impact as good news) has been rejected. However, the coefficient of democracy in an
autocratic government is significant and positive, signifying that democracy is considered
good news for investors. Notably, past square residual terms exert a substantial influence
on risk volatility under all conditions, as both the past conditional variance term and the
past square residual term (RESID (-1) 2) are statistically significant at 5% and 10%,
respectively. Except for monthly exchange rate returns during pre-autocracy (democracy),
GARCH (-1) reveals that past exchange rate return volatility significantly affects current
exchange rate volatility in both daily and monthly exchange rate returns during autocracy
and post-autocratic democracy. Furthermore, the monthly exchange rate returns during
the post-autocracy period exhibit an asymmetric term with a statistically insignificant
coefficient, indicating symmetry in the impact of good and bad news. Conversely, the
coefficient of RESID (-1) 2*(RESID (-1) 0) for daily and monthly exchange rate returns
is statistically significant at the 5% level, illustrating asymmetries in the impact of positive
and negative news.

124 Exchange Rate Volatility in Pakistan's Political Context
Table 04: TGARCH (1,1)
Mean equation
Variance Equation
Average
Returns
Impact of past
exchange rate on
present exchange rate
Impact of Past
square residual
term on present
risk volatility
Impact of Past
volatility on
present
volatility
The impact of
good and bad
news is
asymmetric
/symmetric
Daily Exchange Rate
During Pre-
Autocracy
(1988-1998)
Insignificant
Negative and
significant (15.5%)
Positive and
significant (7%)
Positive and
significant
(71.9%)
Asymmetric
During
Autocracy
(1999-2008)
Negative and
significant
(0.4%)
Negative and
significant (6%)
positive and
significant
(18.7%)
Positive and
significant
(50.9%)
Asymmetric
During Post
Autocracy
(2009-2022)
Insignificant
Positive and
significant (27%)
Positive and
significant
(5.4%)
Positive and
significant
(92.2%)
Asymmetric
Monthly Exchange Rate
During Pre-
Autocracy
(1988-1998)
Insignificant
Positive and
significant (39.9%)
Positive and
significant
(13.2%)
Positive and
significant
(41.4%)
Asymmetric
During
Autocracy
(1999-2008)
Negative and
significant
(0.8%)
Positive and
significant (51%)
Positive and
significant
(59.2%)
Positive and
significant
(61%)
Asymmetric
During Post
Autocracy
(2009-2022)
Insignificant
Positive and
significant (25.1%)
Positive and
significant
(24.1%)
Positive and
significant
(61.8%)
Symmetric
The E-GARCH model, serving as an asymmetric variant of the Generalized
Autoregressive Conditional Heteroskedasticity (GARCH) model, is designed to capture
conditional heteroscedasticity and account for the leverage effects stemming from
unsettling events. A detailed examination of the E-GARCH variables presented in Table
6 reveals their statistical significance in the variance equation at a 5% significance level.
Notably, the constant term C(4) attains significance at this level.
Within the model, the coefficient associated with the ARCH term, denoted as
C(5), is found to be statistically significant at the 10% significance level across all political
systems. This suggests that the nature and scale of autocracy and democracy exert a
considerable influence on exchange rate volatility. Consequently, hypotheses H5 and H6
are validated. It is crucial to note that the positive sign of the coefficient C(5) indicates a
proportional relationship: as the magnitude of autocracy and democracy increases, so does
the volatility.
Furthermore, the asymmetric term C(6) demonstrates significance at a 5% level,
with the exception of the monthly exchange rate during post-autocracy. This implies that
the impact of negative news differs from positive news, except in the specified
circumstance. The positive sign of C(6) implies that positive news has a more substantial
effect on volatility compared to negative news of the same magnitude. Conversely, the

Global Journal for Management and Administrative Sciences 125
ISSN (Print): 2788-4821 ISSN (Online): 2788-483X
Volume 4, Issue 1, Page 113-131, June 30, 2023
negative sign signifies that negative news has a more pronounced impact on volatility than
positive news. In summary, our findings suggest that, aside from the specified scenario,
positive news tends to influence volatility more than negative news in daily and monthly
exchange rates across all regimes. Consequently, hypothesis H4, positing that negative
news has a greater effect on exchange rate volatility than positive news, is rejected.
Additionally, the C(7) terms indicate that the influence of past volatility on
present volatility is statistically significant in all political regimes, except in the monthly
exchange rate during pre-autocracy and the daily exchange rate during autocracy. This
underscores the nuanced interplay of historical volatility in shaping current volatility
dynamics across different political contexts.
Table 05: EGARCH (1,1)
Mean equation
Variance Equation
Average
Returns
Impact of past
exchange rate on
present exchange
rate
Impact of Past
square residual
term on present
risk volatility
Impact of
Past
volatility on
present
volatility
Leverage
effect
Daily Exchange Rate
During Pre
Autocracy
(1988-1998)
zero but
significant
Positive and
significant (1.3%)
Negative and
significant
(32.4%)
Negative and
significant
(32.8%)
Good news has
more impact
During
Autocracy
(1999-2008)
zero but
significant
Negative and
significant (31.5
%)
Positive and
significant
(4.7%)
Negative and
significant
(1.2%)
Good news has
more impact
During Post
Autocracy
(2009-2022)
zero but
significant
Positive and
significant (6.7%)
Positive and
significant (86%)
Positive and
significant
(62.9%)
Good news has
more impact
Monthly Exchange Rate
During Pre
Autocracy
(1988-1998)
0.30%
Positive and
significant
(43.1%)
Positive and
significant
(46.3%)
Positive and
significant
(11.4%)
Good news has
more impact
During
Autocracy
(1999-2008)
insignificant
Positive and
significant
(54.5%)
Positive and
significant
(48.4%)
Positive and
significant
(92.8%)
Good news has
more impact
During Post
Autocracy
(2009-2022)
0.30%
Positive and
significant
(37.8%)
Positive and
significant
(25.1%)
Positive and
significant
(85%)
Good news and
bad news have
the same
impact
Stability Test
The table presented in the Annexure provides a comprehensive illustration of
both dynamic and static forecasting, as well as volatility stability within GARCH models.
Notably, the presence of volatility within the standard error bands is evident. The dynamic
forecasting technique assesses predictions for periods subsequent to the initial sample
period. It achieves this by utilizing prior fitted values derived from the lags of the

126 Exchange Rate Volatility in Pakistan's Political Context
dependent variable. Conversely, the static analysis relies on the actual values of the
regressand variable. This dichotomy in approach instills confidence among investors and
asset holders, fostering a sense of assurance in holding the asset, particularly in the context
of exchange rates, on a daily and monthly basis.
A significant study focused on Pakistan conducted by Shah et al. (2009)
emphasizes the prevalence of high exchange rate volatilities in developing countries. The
study underscores the pivotal role played by central banks in managing and controlling
these volatilities to stabilize the overall economic performance. Interestingly, the study
posits that continuous and persistent intervention by central banks could,
counterintuitively, contribute to an increase in exchange rate volatilities. Building upon
this insight, our own research identifies the volatility of the exchange rate against the U.S.
dollar. This volatility is attributed to a combination of managed and market-based flexible
exchange rate mechanisms.
Normality test
The results of the histogram normality test of the GARCH models shown in
Table 7 suggest that the null hypothesis of Jarque-bera is rejected and that there is no
normality because the p-value is less than 0.05. As a result, hypothesis H1 (The exchange
rate is fluctuating in Pakistan) can be accepted, implying that the dollar exchange rate is
volatile.
Table 06: Results Normality test: Histogram
(Pre-Autocracy)
(Autocracy)
( Post Autocracy)
Models
Jarque-Bera
p-value
Jarque-
Bera
p-value
Jarque-
Bera
p-value
GARCH M (1,1)
642.669
0.000
127.301
0.000
85.104
0.000
T-GARCH (1,1)
237.868
0.000
127.872
0.000
96.964
0.000
E-GARCH (1,1)
1019.878
0.000
84.189
0.000
64.944
0.000
At Confidence intervel : 5%
Comparative Analysis:
Table 07: Comparative Analysis
Daily Exchange Rate
Monthly Exchange Rate
Pre-
Autocracy
(1988-
1998)
GARCH-M
T-GARCH-
E-
GARCH
GARCH-M
T-GARCH
E-GARCH
Average
Returns
zero but
significant
Insignificant
zero but
significant
Positive and
Significant
(0.3%)
Insignificant
Positive and
Significant
(0.3%)
Impact of
Insignificant
Negative
Positive
Positive and
Positive and
Positive and

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ISSN (Print): 2788-4821 ISSN (Online): 2788-483X
Volume 4, Issue 1, Page 113-131, June 30, 2023
past
exchange
rate on
present
exchange
rate
and
significant
(15.5%)
and
significant
(1.3%)
significant
(39.9%)
significant
(39.9%)
significant
(43.1%)
Impact of
Past square
residual
term on
present risk
volatility
Positive and
significant
(14.8%)
Positive and
significant
(7%)
Negative
and
significant
(32.4%)
Negative
and
significant
(7.1%)
Positive and
significant
(13.2%)
Positive and
significant
(46.3%)
Impact of
Past
volatility on
present
volatility
Positive and
significant
(69%)
Positive and
significant
(71.9%)
Negative
and
significant
(32.8%)
Positive and
significant
(137.6%)
Positive and
significant
(41.4%)
Positive and
significant
(11.4%)
The impact
of good and
bad news is
asymmetric
/symmetric.
-
Asymmetric
-
-
Asymmetric
-
Leverage
effect
-
-
Good
news has
more
impact.
-
-
Good news
has more
impact.
GARCH-M
T-GARCH
E-
GARCH
GARCH-M
T-GARCH
E-GARCH
Autocracy
(1999-
2008)
Average
Returns
zero but
significant
Negative
and
significant
(0.4)
zero but
significant
Insignificant
Negative
and
significant
(0.8%)
Insignificant
Impact of
past
exchange
rate on
present
exchange
rate
Negative
and
significant
(6%)
Negative
and
significant
(6%)
Negative
and
significant
(31.5 %)
Positive and
significant
(48.4%)
Positive and
significant
(51%)
Positive and
significant
(54.5%)
Impact of
Past square
residual
term on
present risk
volatility
Positive and
significant
(157%)
Negative
and
significant
(18.7%)
Positive
and
significant
(4.7%)
Positive and
significant
(115.2%)
Positive and
significant
(59.2%)
Positive and
significant
(48.4%)
Impact of
Past
volatility on
present
volatility
Positive and
significant
(51.4%)
Positive and
significant
(50.9%)
Negative
and
significant
(1.2%)
Positive and
significant
(49.5%)
Positive and
significant
(61%)
Positive and
significant
(92.8%)
The impact
of good and
-
Asymmetric
-
-
Asymmetric
-

128 Exchange Rate Volatility in Pakistan's Political Context
bad news is
asymmetric
/symmetric.
Leverage
effect
-
Good
news has
more
impact.
-
-
Good news
has more
impact.
GARCH-M
T-GARCH
E-
GARCH
GARCH-M
T-GARCH
E-GARCH
Post-
Autocracy
(2008-
2022)
Average
Returns
zero but
significant
Insignificant
zero but
significant
Positive and
significant
(0.3%)
Insignificant
Positive and
significant
(0.30%)
Impact of
past
exchange
rate on
present
exchange
rate
Positive and
significant
(31.8%)
Positive and
significant
(27%)
Positive
and
significant
(6.7%)
Positive and
significant
(25.1%)
Positive and
significant
(25.1%)
Positive and
significant
(37.8%)
Impact of
Past square
residual
term on
present risk
volatility
Positive and
significant
(22.1%)
Positive and
significant
(5.4%)
Positive
and
significant
(86%)
Positive and
significant
(43.6%)
Positive and
significant
(24.1%)
Positive and
significant
(25.1%)
Impact of
Past
volatility on
present
volatility
Positive and
significant
(66%)
Positive and
significant
(92.2%)
Positive
and
significant
(62.9%)
Positive and
significant
(65.6%)
Positive and
significant
(61.8%)
Positive and
significant
(85%)
The impact
of good and
bad news is
asymmetric
/symmetric.
-
Asymmetric
-
-
Symmetric
-
Leverage
effect
-
-
Good
news has
more
impact.
-
-
Good news
and bad
news have
the same
impact.
During daily frequency, zero or insignificant average returns are associated with
pre and post-autocracy regimes. In contrast, positive and statistically significant average
returns are associated with pre and post-autocracy during monthly frequency. However,
zero or insignificant average returns are associated with autocracy regimes in daily and
monthly exchange rates.
Moreover, the impact of the past exchange rate on the present exchange rate
during pre-autocracy is ambiguous during the daily exchange rate while positive and

Global Journal for Management and Administrative Sciences 129
ISSN (Print): 2788-4821 ISSN (Online): 2788-483X
Volume 4, Issue 1, Page 113-131, June 30, 2023
statistically significant during the monthly exchange rate. However, during autocracy,
most of the GARCH models show the past exchange rate's positive and statistically
significant impact on the present exchange rate in daily and monthly frequencies. On the
other hand, during post-autocracy, all the GARCH models show the positive and
statistically significant impact of the past exchange rate on the present exchange rate in
both daily and monthly frequencies. Hence, it is concluded that the impact of the past
exchange rate on the present exchange rate during monthly data is more significant. At
the same time, if we compare political regimes, then in all regimes, a positive impact has
been identified.
Furthermore, the impact of past square residual term on present risk volatility
during pre-autocracy and autocracy is statistically significant and positive according to
the outcomes of most of the GARCH models; however, during post-autocracy, it is
statistically significant and positive according to the outcomes of all the GARCH models.
On the other hand, the impact of past volatility on present volatility is also
statistically significant and positive during all regimes and outcomes of GARCH models
are in the same pattern.
Moreover, this study found that the impact of good and bad news is asymmetric
in all regimes and frequencies except during post-autocracy in the monthly exchange rate,
which is symmetric. On the other hand, it is also found that good news has a greater impact
than bad news in all the regimes with an asymmetry in news impact. In contrast, during
post autocracy in the monthly exchange rate, the impact of good and bad news is the same
as it is symmetric.
Conclusion and Implication
In this research, symmetric and asymmetric models GARCH-M (1, 1), T-
GARCH and E-GARCH have been applied to analyze the volatility and risk associated
with daily and monthly exchange rate returns of Pakistani rupees against U.S. dollars, and
the conclusions are as follows.
According to the outcomes of GARCH-M, T-GARCH and E-GARCH, the
exchange rate is volatile in all political regimes and frequencies. There are zero or
insignificant asset returns, and fewer risks are associated with the daily exchange rate,
while small returns and higher risks are associated with the monthly exchange rate.
However, asset returns are significant during pre and post-autocracy as compared to
autocracy, and varying volatility is higher in pre and post-autocracy (democracy) as
compared to autocracy. Moreover, in past regimes of autocracy and autocracy, the impact
of good news is greater. In contrast, in the current political regime (post-autocracy), the
impact of both events is the same during the monthly exchange rate. On the other hand, it
is also concluded that the larger the magnitude of both autocracy and democracy, the larger
the volatility.
Since less risk and more returns are associated with the monthly exchange rate,

130 Exchange Rate Volatility in Pakistan's Political Context
the government, investors and traders should focus on investing, trading and policymaking
monthly. Moreover, in the current era, the impact of disturbing events is symmetric in the
monthly exchange rate, which is convenient for policy and decision-making.
The findings of this study and various other studies suggest that the exchange
rate is volatile in developing countries like Pakistan, where political and economic
instability exists. Therefore, it is important to know about exchange rate volatility as the
country faces various external and internal structural shocks. The government needs to
manage the volatility of the exchange rate to boost the trust of investors and traders.
Furthermore, future studies can determine the factors affecting the exchange rate volatility
in Pakistan.
References
Abdalla, S. Z. S. (2012). Modelling Exchange Rate Volatility Using GARCH Models:
Empirical Evidence From Arab Countries. International Journal Of Economics
And Finance, 4(3), 216–229. Https://Doi.Org/10.5539/Ijef.V4n3p216
Abdullah, S. M., Siddiqua, S., Siddiquee, M. S. H., & Hossain, N. (2017). Modelling And
Forecasting Exchange Rate Volatility In Bangladesh Using GARCH Models: A
Comparison Based On Normal And Student's T-Error Distribution. Financial
Innovation, 3(18), 1–19Https://Doi.Org/10.1186/S40854-017-0071-Z
Bearce, D. H., & Hallerberg, M. (2011). Democracy and de facto exchange rate regimes.
Economics and Politics, 23(2), 172–194. https://doi.org/10.1111/j.1468-
0343.2011.00381.x
Breusch, T. S., & Godfrey, L. G. (1981). A review of recent work on testing for
autocorrelation in dynamic simultaneous models. In Macroeconomic Analysis,
Essays in Macroeconomics and Economics (Vol. 1).
Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for Testing the Constancy of
Regression Relationships Over Time. Journal of the Royal Statistical Society:
Series B (Methodological), 37(2), 149–163. https://doi.org/10.1111/j.2517-
6161.1975.tb01532.x
Ciurean-Moldovan, P.-C. (2021). Essays on the relationships between political indicators,
and real exchange rate and inflation.
Elhussein, N. H. A., & Ahmed, A. E. I. (2019). Exchange Rate Volatility in Sudan: Does
the Exchange Rate System Matter? Journal of Finance and Bank Management,
7(2). https://doi.org/10.15640/jfbm.v7n2a1
Hays, J. C., Freeman, J. R., & Nesseth, H. (2003). Exchange rate volatility and
democratization in emerging market countries. International Studies Quarterly,
47(2), 203–228. https://doi.org/10.1111/1468-2478.4702003

Global Journal for Management and Administrative Sciences 131
ISSN (Print): 2788-4821 ISSN (Online): 2788-483X
Volume 4, Issue 1, Page 113-131, June 30, 2023
Jean Marie Viaene and Casper G.de Vries. (1992). International trade and exchange rate
volatility. European Economic Review, 36(September 1989), 1311–1321.
Kutu, A. A., Alori, D. A., & Ngalawa, H. (2021). Exchange Rate Response To Oil Price
and Political Shocks: What Can Nigeria Do? Journal of Life Economics, 8(2),
237–246. https://doi.org/10.15637/jlecon.8.2.08
Leblang, D. A. (1999). Domestic political institutions and exchange rate commitments in
the developing world. International Studies Quarterly, 43(4), 599–620.
https://doi.org/10.1111/0020-8833.00138
Leblang, D. and, & Bernhard, W. (2015). Democratic Processes, Political Risk, and
Foreign Exchange Markets. American Journal of Political Science,46(2), 316–
333.
Mohsin, M., Muneer, D. S., & Salamat, S. (2019). The Volatility of Exchange Rate Using
GARCH Type Models with Normal Distribution: Evidence from Pakistan.
Pacific Business Review, 11(12), 124–129. www.pbr.co.inwww.pbr.co.in
Quinn, D. P., & Weymouth, S. (2016). The Political Origins of Exchange Rate Valuations
*. 2017(July).
Reynolds, L. G. (1983). The Spread of Economic Growth to the Third World: 1850-1980.
Journal of Economic Literature, 21(3), 941–980.
Sánchez-Fung, J. R. (2003). Nonlinear modelling of daily exchange rate returns, volatility,
and "news" in a small developing economy. Applied Economics Letters, 10(4),
247–250. https://doi.org/10.1080/1350485032000050635
Shah, M. K. A., Hyder, Z., & Pervaiz, M. K. (2009). Central bank intervention and
exchange rate volatility in Pakistan: An analysis using GARCH-X model.
Applied Financial Economics, 19(18), 1497–1508. https://doi.org/10.1080
/09603100902967553
Sharma, C., & Pal, D. (2019). Does Exchange Rate Volatility Dampen Imports?
Commodity-Level Evidence From India. International Economic Journal, 33(4),
696–718. https://doi.org/10.1080/10168737.2019.1630467
Steinberg, D. A., & Malhotra, K. (2014). The effect of authorita rian regime type on
exchange rate policy. World Politics, 66(3), 491–529.
https://doi.org/10.1017/S0043887114000136
Y., M., M, T., & Bello, A. (2014). Forecasting of Exchange Rate Volatility between Naira
and U.S. Dollar Using GARCH Models. International Journal of Academic
Research in Business and Social Sciences, 4(7). https://doi.org/10.6007/
ijarbss/v4-i7/1029