International Journal of Management and Economics Fundamental
40
https://theusajournals.com/index.php/ijmef
VOLUME
Vol.05 Issue 06 2025
PAGE NO.
40-50
10.37547/ijmef/Volume05Issue06-09
The Impact of Inflation on The Cash Liquidity of
Asiacell 2012-2023 Using Models
EGARCH, GJR-GARCH
Lecturer Zinah Tareq Ali
College of Administration and Economics, Tikrit University
Asst Prof Dr. Ziad Ezzeldein Taha Talib
College of Administration and Economics, Tikrit University
Received:
12 April 2025;
Accepted:
08 May 2025;
Published:
17 June 2025
Abstract:
Based on the understanding of the impact of inflation on monetary policy, the company's policy will
improve the company's financial performance by increasing the company's operating income, increasing the
amount of cash, and utilizing the short-term loans received. This study aims to analyze the impact of inflation on
the liquidity of Asiacell Telecommunications Company in Iraq from 2012 to 2023 by determining the impact of
inflation on the company's liquidity variables (operating income, current operating surplus, cash, and short-term
loans received). We used a quantitative analysis method, and the study results showed that inflation has a positive
moral impact on all the components of financial flows examined in this study. The results also showed that there is
conditional volatility and asymmetry in the response of the variables to cash liquidity, because for the variables
operating income and cash, the response to positive shocks is greater than the response to adverse shocks, while
the opposite is true for the variables current operating surplus and short-term loans received. The response to
adverse shocks is greater than the response to positive shocks. This study provides analytical insights on inflation
and liquidity that can help improve the financial stability of Asiacell and assist the company in formulating more
effective fiscal policies.
Keywords:
inflation, cash liquidity, surplus of current operations, business revenue.
Introduction:
The macroeconomic environment is a key factor
affecting a company's performance and financial
stability. Inflation is one of the most important factors
that affect economic activities, especially in developing
countries and countries with unstable economies, such
as Iraq. Unstable inflation rates directly or indirectly
affect the purchasing power of currency, operating
costs, and the prices of goods and services, affecting
companies' liquidity. High inflation rates are considered
a worrying indicator of a functioning economy because
they affect different countries differently, leading to
currency depreciation, reducing growth opportunities,
and affecting the performance of companies,
institutions, and even individuals (Murzuq, Ala, 2022).
Liquidity is one of the most important indicators of a
company's financial stability because it reflects its ability
to meet short-term obligations and daily operating
needs without facing the risk of default. Liquidity
reflects the ability of a company to deal with
unexpected expenses, repay outstanding debts, and
meet production and operational needs promptly.
Inflation will undoubtedly directly or indirectly impact
cash flow because liquidity is closely related to inflation.
Once inflation occurs, the stock market is most affected.
The company's net income determines stock prices and
depends on how much profit the company is expected
to make in the long or short term. The connection
between inflation and liquidity is well known.
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Companies pursue conservative policies during
inflationary periods, so inflation and liquidity negatively
correlate (Moosa, 2014). A company's operating cycle
starts with the purchase of raw materials, includes
inventory turnover, continuous sales days, and ends
with the collection of the company's debts. The length
of the operating cycle is affected by the industry and
many company-specific factors, such as the nature of
operations, business model, and management
efficiency. A longer operating cycle means higher
investment in current assets and reduces the company's
cash supply. (Naumoski & Juhasz, 2019)
First: The problem of the study:
Inflation is one of the most important factors affecting
the liquidity of Asiacell Telecommunications Company,
including operating revenue, operating surplus, cash
balance of the company's funds, and short-term
borrowings received. Given the current economic
situation in Iraq, questions have arisen about the impact
of inflation on the liquidity of companies, including
Asiacell. Companies operating in unstable economic
environments, such as the Iraqi financial environment,
face many additional challenges related to high
operating costs, exchange rate fluctuations, and low
purchasing power, often accompanied by high inflation
rates. In these situations, cash flow management
becomes more complex, especially when financing
sources are limited and the need to repay short-term
debts increases regularly. Therefore, the main issue of
this study is to analyze the impact of inflation on the
liquidity of Asiacell Telecommunications Company by
examining the impact of inflation on the company's
basic financial components, such as revenue, operating
surplus, available cash, and short-term borrowings. This
study is based on the following central hypothesis:
Inflation hurts a company's liquidity by reducing its
purchasing power, increasing financing costs, and
reducing the real value of cash.
Second: The importance of research:
This study aims to determine the impact of inflation on
the liquidity of Asiacell Telecommunications Company
in Iraq. The following points illustrate the importance of
this study:
1.
To assess the impact of inflation on the company's
turnover.
2.
To assess the impact of inflation on the current
operating surplus.
3.
To assess the impact of inflation on the cash in the
company's accounts.
4.
To assess the impact of inflation on short-term
loans received.
Third: Research Objectives
The study examines inflation's impact on Asiacell
Telecommunications Company's liquidity from 2012 to
2023. The following measures are being taken:
1.
Analyze the relationship between the inflation rate
and the company's cash flow to determine the
direction of price changes and the impact on cash
flow.
2.
Measure the impact of inflation on the
components of cash flow in detail, including cash
on hand, receivables, and other current assets.
3.
Investigate the impact of inflation on short-term
loans and its impact on the company's net liquidity.
4.
Determine how much liquidity responds to
inflationary changes in the Iraqi economic
environment during the study period.
5.
Provide recommendations to the management to
improve liquidity and deal with the impact of
future inflation.
6.
Research Form:
The model includes an inflation rate variable for Iraq
between 2012 and 2023. Although inflation data prior
to 2012 are available, the company's quarterly reports
were not published on the company's website or even
the Iraqi depository page until early 2012, so this period
is limited as it represents the dependent variable and
cash liquidity variable as independent variables in four
separate models (operating income, current operating
surplus, cash, short-term loans received), as shown in
the following equation:
First model:
IR
𝑡
= α
0
+ α
1
⋅ INF
𝑡
+ ε
𝑡1
(1)
Where:
IR
t
: revenue from the business.
International Journal of Management and Economics Fundamental
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INF
t
: Iraq Inflation Rate.
α
0
: Fixed-term first form.
α
1
: coefficient of the inflation variable.
ε
t1
: Random limit in the first model.
Second model:
CO
𝑡
= β
0
+ β
1
⋅ INF
𝑡
+ ε
𝑡2
(2)
Where:
CO
t
: Current operating surplus is the sum of the first
and second current operating surpluses.
INF
t
: Iraq Inflation Rate.
𝛽
0
:
: The hard limit of the second model.
𝛼
1
: Inflation variation coefficient.
ε
t2
: Random limit in the second model.
Third model:
𝐶𝑆𝐿
𝑡
= γ
0
+ γ
1
⋅ INF
𝑡
+ ε
𝑡3
(3)
Where:
CSL
t
: Ready cash.
INF
t
: Iraq Inflation Rate.
𝛾
0
:
Constant restrictions in the third model.
𝛾
1
: Inflation variation coefficient.
ε
t3
: Random limit in the third model.
𝐿𝑂𝐴𝑁𝑆
𝑡
= δ
0
+ δ
1
⋅ INF
𝑡
+ ε
𝑡4
(4)
Where:
LOANS
t
: Short-term borrowings.
INF
t
: Iraq Inflation Rate.
𝛿
0
: Fixed limit.
𝛿
1
: Variable coefficient of inflation.
ε
t3
: Random term in the fourth form.
Fourth: Research hypotheses:
Hypothesis I
: The impact of inflation on corporate
income is significant, with a significance level of 0.05.
Hypothesis II
: The impact of inflation on current
operating profit is significant, at 0.05.
Hypothesis three
: The impact of inflation on cash is
significant, at 0.05.
Fourth hypothesis
: The impact of inflation on short-
term loans is significant at 0.05.
Fifth: Research Methodology
Using quantitative analysis methods based on Asiacell's
quarterly annual reports and economic models, the data
are analyzed using two models, EGARCH and GJR-
GARCH, to estimate the impact of inflation on the direct
component of Asiacell's cash liquidity in Iraq.
The first topic: the theoretical side
1.
Inflation
Inflation is a sustained general price increase due to
excessive circulation of money, combined with price
increases that limit access to goods and services (Tabidi
& Atoli, 2024).
Inflation refers to an increase in the general price level.
The flooding of monetary channels leads to an excess of
money, while the supply of goods is not adequately
increased. Inflation does not mean an increase in the
cost of living, but is a decrease in the value or purchasing
power of money. It is important to note that inflation
does not occur in regular commodity exchanges, as in
the trade of gold and silver. In the circulation of paper
money, money is only a symbol, not real wealth, which
causes inflation. In other words, inflation is not a natural
process (if inflation is considered an economic
phenomenon), but is directly related to human activities
(McConnell et al., 2009).
2.
The concept of cash liquidity
Liquidity in the absolute sense refers to cash (money).
In contrast, liquidity in the technical sense refers to the
ability of assets to be converted into liquid funds to
meet current or short-term obligations (Abdul Hamid,
2000). In other words, liquidity refers to the ability of a
bank to meet its obligations immediately by converting
assets into cash quickly and without loss of value (Abu
Qahf, 1993). Therefore, liquidity is a relative term that
describes the relationship between cash and assets that
can be easily converted into cash (quasi-cash assets) and
outstanding liabilities that need to be met (Abdul
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International Journal of Management and Economics Fundamental (ISSN: 2771-2257)
Hamid, 2000). Liquidity represents the ability of a bank
to manage deposit withdrawals, settle outstanding
liabilities, and meet credit demands promptly (Abdul
Hamid, 2000). A company's liquidity can be measured
using liquidity ratios such as the current, quick, and cash
ratios. These ratios indicate the extent to which a
company can meet its short-term obligations using
current assets, inventory, cash, and cash equivalents
(Nindy & Arfan, 2025).
•
Business Activity Revenue
Business activities are the backbone of the global
economy. It is a commercial organization that provides
and markets services or products to generate long-term
revenue (Plutus Education).
•
Current Operations Surplus.
A company's operating surplus measures the difference
between production revenues and expenditures, i.e.,
the production surplus or deficit. It is calculated before
any interest, lease, or similar expenses are incurred on
non-productive financial or tangible assets borrowed or
leased by the business, and before any interest, lease,
or similar income is generated on the business's
property (Eurostat)
.
•
Available Cash
means, in respect of any financial quarter, all of the
current assets of the Company as of the end of such
quarter less such amount of cash reserves as the
Members may reasonably determine to be necessary or
appropriate to (a) ensure the smooth functioning of the
Company (including reserves for future capital
expenditures and anticipated future borrowing
requirements of the Company) and then to allocate to
the Real Estate Fund the cash available for distribution
to Unitholders as dividends. The value of cash available
for distribution is calculated based on the assets of the
Fund, which have fewer operating costs and current
capital expenditures (Investopedia).
•
Short-term loans
The level of "short-term debt and long-term
investment" of energy enterprises is measured by the
difference between the short-term debt ratio (short-
term debt divided by total debt) and the short-term
asset ratio (short-term assets divided by total assets).
This is a positive indicator reflecting the compatibility of
the term structure between investments (Gong &
Zhang, 2025).
The second topic: presentation and analysis of the
results: analysis of the relationship between the
variables of the study
First: Testing the stability of variables
The stability of the study variables was tested using the
Eviews.12 program, and the extended Dickie Fuller test
(ADF) was performed to determine the stability. The
results are shown in Table 1.
Table 1: Dickie-Fuller's Extended Root Test
The first difference
Level
Variable
B
A
Non
B
A
Non
-
-
-
-3.147
-3.188*
-1.937
Inflation
(INF)
-
-
-
-
-7.590*
-2.793*
Business Revenue
(IR)
-
-
-
-
-2.870
-2.774**
Surplus of Current
Operations (CO)
-
-
-
-3.839*
-1.893
-3.248*
Ready cash
(CSL)
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-
-
-
-1508.342*
-1119.67
-669.055*
Short-term loans
(LOANS)
A: represents a regression involving only one secant.
B: means regression containing a secant and a general
direction.
Non: means regression has no specific or general
direction.
*: means moral at the level of moral 5%.
The results of Table (1) show that inflation is stable at
the level without cutoff and direction, but unstable at
the level of cutoff and direction; business income is
stable at the level without cutoff and direction, but
unstable at the level of cutoff and direction; the current
account surplus variable cash is stable only when there
is no cutoff and direction; short-term loans are stable
when there is no cutoff and direction, and stable when
there is cutoff and direction, but unstable when there is
cutoff and direction. Classification, that is, contains a
unit root at the 5% significance level, so the time series
is stable at the level of taking first-order differences
below the 5% significance level.
Second: ARCH Effect Test:
Table 2: ARCH Effect Test
Model
Number
F
Prob(F)
Obs*R²
Prob (Chi²)
Total
1
5.662
0.0216
5.253
0.0219
There is an
ARCH effect
at 5%
2
5.218
0.0271
4.884
0.0271
3
38.198
0.000
21.579
0.000
4
11.022
0.0018
9.247
0.0024
The results in Table 2 show the test results for the
presence of ARCH effect using the F-statistic and Obs*R²
statistics and the corresponding p-values in the four
models examined. The results show that the ARCH
effect exists in all models, although to different extents.
In the first model, the F-value is 5.662, and the
probability is 0.0216, indicating the presence of the
ARCH effect at the 5% significance level. The results of
the second model also show an ARCH effect, with an F-
value of 5.218 and a probability of 0.0271, which is D at
the 5% level. The results of the third model are similar
to the first and second models, with an F-value of 38.198
and a probability of 0.000, which are significant at the
5% level. In the fourth model, the results show the
presence of ARCH. Here, F (11.022) and probability
(0.0018) are also significant, corresponding to a
significance of 5%. The results of the ARCH test confirm
that ARCH exists in all models. This requires using the
GARCH model or its extended form (such as EGARCH or
GJR-GARCH) in time series analysis to evaluate the
performance of cash liquidity under inflation changes.
Third. Estimation of GARCH / GJR-GARCH / EGARCH
models
The first model: The impact of inflation on operating
income
Results Table (3) shows the estimation of the impact of
inflation on corporate revenue using GJR-GARCH for
Model 1. The results of the initial model estimation
show that there is a positive and statistically significant
relationship between inflation rate and Asiacell
Telecom's business revenue, with a significance level of
(0.0131), which is below the 5% significance level, which
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means that high inflation rates are associated with
increased revenue, which may be attributed to the
impact of inflation on service pricing or demand. It also
shows that the GARCH coefficient is positive and
statistically significant, with a significance level of
(0.000) below the 5% significance level, indicating that
time series shock fluctuations play an important role in
explaining revenue changes, i.e., h. The model reflects
the existence of conditional fluctuations in the financial
statements reviewed. At the variance equation level,
the results also show an asymmetric effect, with the
negative shock coefficient (RESID(-1)^2*(RESID(-1)<0))
being negative and the function shock coefficient (-
0.603) being negative. The significance level (0.000) is
below the 5% significance level, confirming that the
variance is more affected by adverse shocks than by
positive shocks, which is consistent with the properties
of the GJR-GARCH model. The GARCH(-1) coefficient is
also statistically significant (0.000) below the 5%
significance level, indicating a longer performance in the
conditional variance process, i.e., h. Previous
fluctuations continue to affect current variances. The
goodness of the model fit indicates that the model has
good explanatory power, with a coefficient of
determination (R-squared) of about 0.54, i.e., the model
explains 54% of the turnover variation, while the
adjusted R-squared coefficient is about 0.53. The
Durbin-Watson statistic (2.052) also indicates no
autocorrelation in the rest of the model, which
increases the reliability of the first model results.
Table 3: Results of the first model estimation
Significance level
z
Standard error
Non-standard coefficient
Variable
0.000
5.156837
3.91E-10
2.02E-09
ARCH
0.0131
2.481508
947.1316
2350.315
Inflation
COMMERCIAL_ACTIVITY_REVENUE = 2.01737389231e-09*GARCH + 2350.31450269*INFLATION
Estimating variance
0.6124
-0.506664
3.67E+14
-1.86E+14
C
0.0378
2.077052
0.060246
0.125134
RESID(-1)^2
0.000
-10.05927
0.059949
-0.603045
RESID(-1)^2*(RESID(-1)<0)
0.000
18.43943
0.055608
1.025371
GARCH(-1)
GARCH = -1.86172738831e+14 + 0.125133508812*RESID(-1)^2 - 0.603045172088*RESID(-1)^2*(RESID(-1)<0) +
1.02537088658*GARCH(-1)
0.54
Coefficient of determination
0.53
Adjusted coefficient of determination
2.052
Druppen-Watson
Model Two: The impact of inflation on operating profit
Results Table (3) shows the estimation of Model 2 using
GJR-GARCH to understand the impact of inflation on the
current activities' surplus. The model was estimated,
and the results show that the model's significance level
is less than 5%. The results of the second model
estimation show that there is a positive and statistically
significant relationship between inflation and the
operating activities surplus of Asiacell Telecom, with a
significance level (0.0142) below the moral level of 5%,
which means that a high inflation rate is associated with
the company's operating activities surplus. The results
also show that the GARCH coefficient is positive and
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statistically significant with a significance level (0.000)
below the moral level of 5%, which indicates that the
volatility of shocks in the time series plays an important
role in explaining the changes in sales, i.e., h. The model
reflects the presence of conditional volatility in the
financial reports reviewed. At the variance equation
level, the results also show an asymmetric effect, with
the coefficient for adverse shocks (RESID(-1)^2*(RESID(-
1)<0)) being negative and the function (0.-612) being
negative, with a significance level (0.000) below the 5%
significance level, confirming that the variance is more
affected by adverse shocks than positive shocks, which
is consistent with the properties of the GJR-GARCH
model. In addition, the GARCH(-1) coefficient is
statistically significant (0.000) at the 5% significance
level, reflecting the long-term performance in the
conditional variance process, i.e., h. Previous
fluctuations continue to affect current variances. The
goodness of the model fit indicates that the model has
good explanatory power, with a coefficient of
determination (R-squared) of about 0.54, meaning that
the model explains 38% of the variation in remaining
farms, while the adjusted R-squared coefficient is about
0.37. The Durbin-Watson statistic (1.96) indicates no
autocorrelation in the rest of the model, which
increases the reliability of the results of the second
model.
Table 4: Results of estimating the second model
Significance level
z
Standard error
Non-standard coefficient
Variable
0.0005
3.459281
7.66E-08
2.65E-07
ARCH
0.0142
2.452332
229.0772
561.7732
Inflation
CURRENT_OPERATIONS_SURPLUS = 2.64944316386e-07*GARCH + 561.773189523*INFLATION
Estimating variance
0.000
2.45E+99
7.21E-94
1766378
C
0.000
1.70E+101
1.80E-102
0.298237
RESID(-1)^2
0.000
-4.702901
0.130135
-0.612011
RESID(-1)^2*(RESID(-1)<0)
0.000
2.30E+101
2.50E-102
0.554381
GARCH(-1)
GARCH = 1766377.51539 + 0.29823666986*RESID(-1)^2 - 0.612010988194*RESID(-1)^2*(RESID(-1)<0) +
0.554380978247*GARCH(-1)
0.39
Coefficient of determination
0.38
Adjusted coefficient of determination
1.96
Druppen-Watson
Model Three: The Impact of Inflation on Ready Cash
The results in Table 4 show the effect of inflation on cash
estimated using E-GARCH for Model 3. The model
estimation was performed, and the results showed that
the model significance level was less than 5%. The
estimation results of the third model showed that for
Asiacell Telecommunications Company, there is a
positive and statistically significant relationship
between inflation and cash with a significance level of
0.000, which is less than the significance level of 5%,
which means that high inflation rates are related to
cash. In comparison, the GARCH coefficient is positive
and significant because it reaches (2.65E-7) and reaches
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International Journal of Management and Economics Fundamental (ISSN: 2771-2257)
the significance level of (0.000) because it is less than
5%. This result shows that the volatility in the previous
period has a positive impact on the liquidity level, as the
results show that the impact of volatility is significant
and asymmetric, the absolute shock coefficient is
positive (0.5394-), while the asymmetric coefficient is
positive and has a higher value (0.670), which indicates
that positive shocks react more strongly to volatility
than adverse shocks, and the results show that the
coefficient. The quality of model fit shows that the
model has good explanatory power, with the
determination coefficient (R-squared
) of 0.30. This means that the model explains 30% of the
variation in cash holdings, and the adjusted R-squared
coefficient is about 0.28. The Durbin-Watson statistic
(1.85) indicates no autocorrelation in the rest of the
model, which increases the reliability of the third model
results.
Table 5: Results of the third model estimation
Significance
level
z
Standard
error
Non-
standard
coefficient
Variable
0.0005
6.180992
0.270342
1.670981
ARCH
0.0142
5.013646
3.59E-05
0.00018
Inflation
CASH = 1.67098132663*@SQRT(GARCH) + 0.000179787565306*4*INFLATION
Estimating variance
0.000
72.92176
0.189288
13.80318
C
0.000
-10.8325
0.049792
-0.53937
ABS(RESID(-1)/@SQRT(GARCH(-1)))
Absolute shock
0.000
12.39132
0.054094
0.670295
RESID(-1)/@SQRT(GARCH(-1))
Asymmetries
LOG(GARCH) = 13.8031782787 - 0.539368999735*ABS(RESID(-1)/@SQRT(GARCH(-1))) +
0.670295452922*RESID(-1)/@SQRT(GARCH(-1))
0.30
Coefficient of determination
0.28
Adjusted coefficient of determination
1.85
Druppen-Watson
Fourth model: the impact of inflation on short-term
loans received
The results in Table 5 show the estimation of Model 4
using E-GARCH, which is used to analyze the impact of
inflation on short-term loans. The model estimation was
performed, and the results show that the significance
level of the model is less than 5%. The results of the
third model estimation show a positive and statistically
significant relationship between inflation and short-
term loans received by Asiacell Telecom, with a
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significance level of 0.000, which is less than the 5%
significance level, which means that high inflation rates
are related to cash. At the same time, the GARCH
coefficient is positive and significant, reaching 1.4898.
The significance is (0.000) because it is less than 5%. The
result shows that the volatility in the previous period
positively impacts the liquidity level. The results show
that the impact of volatility is significant and
asymmetric, because the absolute shock coefficient is
negative (2.898-). In contrast, the asymmetric
coefficient is negative and has a higher value (2.676),
which reflects that
adverse shocks are more responsive to volatility than
positive shocks. The results show that the coefficient is.
The quality of the model fit shows that the model has
good explanatory power, as the determination
coefficient (R-squared) is about 0.40. This means the
model explains 40% of the variation in short-term loans
received, and the adjusted R-squared coefficient is
about 0.38. The Durbin-Watson statistic (1,852,094)
shows no autocorrelation in the rest of the model,
which increases the reliability of the fourth model
results.
Table 6: Fourth Model Estimation Results
Significance
level
z
Standard
error
Non-standard
coefficient
Variable
0.000
14.63526
0.101795
1.489798
ARCH
0.000
7.262145
176.5817
1282.361
Inflation
(SHORT_TERM_LOANS)^(1) = 1.48979801478*@SQRT(GARCH) + 1282.36149568*(INFLATION)
Estimating variance
0.000
1942.352
0.018679
36.28137
C
0.000
-38.6155
0.075036
-2.89754
ABS(RESID(-1)/@SQRT(GARCH(-1)))
Absolute shock
0.000
26.87946
0.099546
2.675746
RESID(-1)/@SQRT(GARCH(-1))
Asymmetries
LOG(GARCH) = 36.281366011 - 2.89754326961*ABS(RESID(-1)/@SQRT(GARCH(-1))) +
2.67574647325*RESID(-1)/@SQRT(GARCH(-1))
0.40
Coefficient of determination
0.38
Adjusted coefficient of determination
2.094
Druppen-Watson
CONCLUSIONS
1.
Inflation is impacting Asiacell Communications'
liquidity. GARCH and E-GARCH models provide
practical tools for analysing these impacts and
understanding the economic dynamics related to
cash.
2.
Inflation has a positive impact on Asiacell
Communications' revenue. Market volatility also
International Journal of Management and Economics Fundamental
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International Journal of Management and Economics Fundamental (ISSN: 2771-2257)
plays an important role in explaining changes in
sales. Adverse shocks react more strongly than
positive shocks.
3.
The results show that inflation positively impacts
Asiacell Telecom's operating surplus, and market
volatility plays an important role in explaining
changes in operating surplus. The study found that
economic shocks have a long-lasting effect. In
addition, the volatility of adverse shocks is more
pronounced than positive shocks.
4.
The results of this study show that inflation has a
positive impact on Asiacell Telecom's liquidity and a
significant indicative effect on liquidity volatility.
Asymmetric effects also reflect a stronger reaction
to positive than adverse shocks. The model is well-
equipped to explain changes in cash holdings.
5.
The results confirm that inflation positively impacts
Asiacell Telecom's short-term loans, while
economic shocks are asymmetric.
Recommendations:
This study contains some important recommendations:
1.
Apply GARCH, E-GARCH, and GJR-GARCH models to
analyze the impact of inflation on liquidity and
understand economic dynamics.
2.
Management should monitor the impact of inflation
on sales in order to optimize pricing and demand
strategies based on the impact of inflation and
market fluctuations.
3.
Management should manage market fluctuations in
current operating earnings to develop strategies to
reduce the impact of market fluctuations on current
operations.
4.
Companies must maintain stable liquidity to ensure
availability
during
inflation
and
economic
fluctuations.
5.
Asymmetric economic shocks must be managed to
establish a rapid response mechanism to adverse
shocks that affect the company's financial
performance.
6.
Company management should make long-term
shock planning and develop long-term financial
strategies to cope with the impact of inflation and
economic changes.
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