Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 12, No. 1, 2023 

 

82 

Study	on	the	Influence	of	Cotton	Option	Launch	on	The	
Price	Fluctuation	of	Underlying	Futures	
Wenmei Liu* 

School of Business, Hunan University of Science and Technology, Xiangtan 411201, China 
*Corresponding author email: ytbz69_69@163.com 

 

Abstract: This paper takes cotton options as the research object, and empirically tests the impact of cotton options on the price 
fluctuation of the underlying futures in the short and medium term by establishing the ARMA-GARCH model with the 
introduction of two dummy variables. The study found that the launch of cotton options increased the volatility of the underlying 
futures price in the short term, and increased the volatility in the medium and long term. After the launch of cotton options, the 
supply of national policies against the epidemic played a certain stabilizing role in the volatility of futures prices, but the policy 
effect has a time lag. Based on this, institutional investors should be encouraged to use cotton options and futures risk 
management, build a risk prevention and control mechanism for sudden public crisis events, popularize the knowledge of options 
and futures, and improve the transparency of cotton trading. 

Keywords: Cotton options, Futures prices, Risk management, Financial supervision. 
 

1. Introduction 
Cotton is a national strategic bulk agricultural product, 

which is related to agricultural economy and high quality 
development of agriculture. Among them, whether the price 
of cotton is stable is an important standard for the healthy 
development of its industry. Its abnormal fluctuations will 
increase the uncertainty of producers' market expectations, 
which will increase the risks faced by the cotton industry. In 
order to make the steady and healthy development of cotton 
industry, the launch of cotton financial derivatives has 
become an inevitable trend. Derivative financial products are 
an important tool to stabilize the financial market, which can 
prevent and resolve financial risks well. However, investors 
are prone to some irrational behaviors such as speculation 
under the influence of the external environment, which may 
deviate from the original intention of derivative financial 
instrument innovation and aggravate the volatility of the 
financial market. Cotton options as a derivative financial 
instrument was officially listed on the Zhengzhou 
Commodity Exchange on January 28, 2019. Will its listing 
bring certain fluctuations to cotton futures prices? How big is 
the volatility? The discussion of these issues can provide a 
useful policy reference for the healthy development of cotton 
industry and further maintain the smooth operation of cotton 
derivative financial market. 

2. Literature Review 
As for the study of futures price volatility, scholars mainly 

start from two aspects: the impact of futures listing on spot 
market volatility and the impact of sudden external factors on 
futures price volatility. In terms of the impact of futures listing 
on spot price volatility, Gu Haifeng and Yayong Zhou(2019) 
found by means of wavelet analysis that the listing of CSI 500 
stock index futures had a significant boost effect on spot price 
in the short term [1]. Based on the VECM model, Junlin Li et 
al. (2023) concluded that there is a good linkage between the 
domestic and foreign soybean futures markets and the 
domestic spot prices, and the soybean futures of Dalian 

Exchange have a greater impact on the domestic spot price 
discovery [2]. In terms of the impact of sudden external 
factors on the volatility of futures prices, Borgards et al. (2021) 
compared and analyzed the price behavior of 20 futures 
before and after the COVID-19 epidemic, and confirmed that 
different futures prices showed varying degrees of excessive 
volatility during the epidemic [3]. Xinyue Li (2023), with the 
help of the ACARR-X model, concluded that external shocks 
such as Sino-US trade friction and the novel coronavirus 
epidemic intensified the volatility of futures prices [4]. 

The research on the impact of option launch on futures 
price fluctuation mainly includes three viewpoints: expanding 
futures price fluctuation, reducing futures price fluctuation 
and no significant impact on futures price fluctuation. Luyao 
Li (2019) took soybean meal options as the research object 
and found that after the listing of soybean meal options, 
soybean meal futures price fluctuations decreased [5]. 
Robbani and Bhuyan(2005) studied the Jones Industrial index 
options and concluded that the increased volatility of futures 
prices was caused by the increase in irrational transactions of 
investors [6]. Mazouz (2004) pointed out that the listing of 
stock index options had no significant impact on the volatility 
of futures prices [7]. 

In summary, the existing literature mainly focuses on the 
impact of futures price fluctuations, index options and 
individual commodity options on the price fluctuations of 
underlying futures. There are few studies on the relationship 
between cotton option launch and the price fluctuation of the 
underlying futures, and few analyses on the short and medium 
term effects of cotton option launch on the price fluctuation 
of the underlying futures. In view of this, this paper constructs 
an ARMA-GARCH model introducing single and double 
dummy variables to explore the impact of cotton option 
launch on the price fluctuation of the underlying futures, 
which provides a certain reference for maintaining the smooth 
operation of the cotton derivative financial market and 
preventing and resolving financial risks. 



 

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3. Research Design 

3.1.  Model construction 
The ARMA-GARCH model can more accurately describe 

the fluctuation process of financial time series rate of return. 
The general expressions of the ARMA-GARCH model 
introducing single and double virtual variables are as follows: 

 

t

n

1j jtjit

m

1i i0t
εεθRR              (1) 

 

ttt uσε                         (2) 
 

zcgjγyearγσβεαασ 21
2
jt

q

1j j
2
it

p

1i i0
2
t    (3) 
 
Formula (1) is the ARMA model, which is an 

autoregressive moving average process. In equation (2) u  
are random variables that obey the standard normal 
distribution. Formula (3) is the GARCH model with two 
virtual variables. α  represents the degree of impact brought 
by external shocks; β  indicates the degree of influence of the 
previous period on the current volatility. Taking the 
introduction of cotton options as the cut-off point, a time 
dummy variable year is introduced. Considering the impact 
of national policy supply on the volatility of futures prices 
after the outbreak of the novel coronavirus pneumonia 
epidemic, a second dummy variable zcgj is introduced on the 
basis of the time dummy variable year. The time dummy 
variable year said that in the case of national policy supply 
after the outbreak of the new coronavirus pneumonia 
epidemic, the introduction of cotton options on the impact of 
the underlying futures price fluctuations.If 𝛾  is greater than 
0 and significant, it indicates that the introduction of cotton 
options has expanded the volatility of futures prices in the 
case of national policy supply after the outbreak of the novel 
coronavirus pneumonia epidemic; If 𝛾   is less than 0 
significant, it indicates that the introduction of cotton options 
has reduced the price volatility of the underlying futures 
under the condition of national policy supply after the 
outbreak of the novel coronavirus pneumonia epidemic; If 𝛾  
is not significant, it is impossible to determine the impact of 

the introduction of cotton options on the price fluctuation of 
the underlying futures under the condition of national policy 
supply after the outbreak of the novel coronavirus pneumonia 
epidemic. The dummy variable zcgj said that after the 
introduction of cotton options, the impact of the policy supply 
to fight the epidemic on futures prices. If 𝛾  is greater than 0 
and significant, it indicates that after the introduction of 
cotton options, the policy supply to combat the epidemic has 
expanded the volatility of futures prices; If 𝛾  is less than 0 
and significant, it indicates that after the introduction of 
cotton options, the policy supply to fight against the epidemic 
has reduced the volatility of futures prices; If 𝛾   is not 
significant, it is impossible to determine the impact of the 
policy supply against the epidemic on the volatility of futures 
prices after the introduction of cotton options. 

3.2.  Data specification 
This paper selects 1771 daily closing prices of major cotton 

futures contracts from January 28, 2016 to May 15, 2023 as 
sample data. All data is from the Wind database. The sample 
data is divided into two analysis intervals. The first interval 
sample data is from January 29, 2018 to February 3, 2020, 
indicating the short-term effect of the introduction of cotton 
options on the volatility of the underlying futures price. The 
second interval sample data is from January 28, 2016 to May 
15, 2023, indicating the medium and long term effect of the 
introduction of cotton options on the volatility of the 
underlying futures price. The first-order difference of the 
sample data is used to obtain the futures daily rate of return, 
which helps to enhance the stationarity of the series. The 
formula for the first-order difference is: 

 

1ttt lnPlnPR                 (4) 
 
Where, 𝑅   represents the futures yield on the t day, 𝑃  

represents the closing price on the t day. The results in Table 
1 show that the mean and standard deviation of the futures 
yield series are smaller in the short period of the launch of 
cotton options, which preliminarily indicates that the launch 
of cotton options has the effect of stabilizing futures prices to 
a certain extent. 

 
Table 1. Descriptive statistics 

Sample Mean Std.Dev. Skewness Kurtosis Jarque-Bera 
Short term -0.0004 0.0122 -0.2446 7.9343 495.8419 

Medium and long term 0.0002 0.0141 -0.1798 7.2136 1318.9260 

 

4. Empirical Results and Analysis 

4.1.  Stationarity test 
In order to avoid the phenomenon of pseudo-regression, the 

ADF test is carried out on the return series respectively. The 
results in Table 2 show that the T-statistic values are all less 

than the corresponding critical values at the significance level 
of 1%, and the unit root null hypothesis can be rejected, which 
indicates that the return rate series have stationarity. 
Considering the minimum information criterion and 
parameter significance comprehensively, ARMA(1,1) is used 
as the mean regression equation for all return series. 

 
Table 2. Stationarity test results 

Sample T-statistic 
1% critical 

value 
5% critical 

value 
10% critical 

value 
P-value 

Short term -21.1782 -3.4436 -2.8673 -2.5699 0.0000 
Medium and long term -42.7879 -3.4338 -2.8630 -2.5676 0.0000 

 
ARCH LM test is performed on the residual of the return series. The results in Table 3 show that the residual square 



 

84 

sequences are not random and all have ARCH effect. 
Therefore, it is necessary to build GARCH model for further 

fitting. 

 
Table 3. ARCH LM test results 

Sample F-statistic Prob.F Obs*R-squared Prob.Chi-Square 
Short term 3.1312 0.0774 3.1239 0.0772 

Medium and long term 83.7862 0.0000 80.0837 0.0000 
 

4.2. Result analysis 
In order to study the short and medium term effects of 

cotton option launch on the price fluctuation of the underlying 
futures, this paper first establishes the ARMA-GARCH 
model with dummy variables of cotton option launch time for 
two series respectively. The dummy variable year is: before 
the launch of cotton options (January 28, 2016 - January 27, 
2019) 𝑦𝑒𝑎𝑟 0  and after the launch of cotton options 
(January 28, 2019 - May 15, 2023) 𝑦𝑒𝑎𝑟 1. According to 
the white paper "China's Actions against the Novel 
Coronavirus Epidemic" released by The State Council 
Information Office, this paper has identified January 20, 2020 
as the time for intensive provision of anti-epidemic policies. 
Considering that the policy supply of the country after the 
outbreak of the novel coronavirus pneumonia epidemic may 
have an impact on the volatility of futures prices, this paper 
introduces the policy supply dummy variable zcgj on the basis 
of the dummy variable of the launch time of cotton options. 
The dummy variable zcgj is: before the outbreak of COVID-
19 and the intensive supply of national policies (January 28, 
2016 - January 19, 2020) 𝑧𝑐𝑔𝑗 0  and after the national 
policy supply (January 20, 2020 - May 15, 2023) 𝑧𝑐𝑔𝑗 1. 
Considering the minimum information criterion and 
parameter significance comprehensively, the ARMA(1,1)-
GARCH(1,1) model is established to determine the final 
return series. 

Table 4 shows that the sum of α   and β   is close to 1, 
indicating that the fitting process is relatively stable. All the 
P-values in the ARCH LM test results were significant at the 
significance level of 1%, which indicated that the ARCH 

effect had been eliminated after fitting, and the model was 
effective as a whole. 𝛾  is greater than 0 and significant, and 
the launch of cotton options is small in the short term, which 
indicates that under the condition of national policy supply 
after the outbreak of the novel coronavirus pneumonia 
epidemic, the launch of cotton options has expanded the 
volatility of the underlying futures price in the short term, and 
the volatility has intensified in the medium and long term. The 
possible reason is that the listing time of cotton options is 
short, the market cognition is not very high, and there are 
many retail investors in China, more speculative, the risk 
management function of cotton options has not been 
effectively played. In the short term of the launch of cotton 
options, 𝛾   is not significant, and it is impossible to 
determine the impact of the supply of anti-epidemic policies 
on the volatility of futures prices after the launch of cotton 
options. It may be because the anti-epidemic policy in the 
short term of the introduction of cotton options has just been 
released, and the effect of the policy has a time lag, so it can 
not be accurately measured. In the medium and long term 
after the launch of cotton options, 𝛾   is less than 0 and 
significant, indicating that after the launch of cotton options, 
the policy supply to fight against the epidemic has played a 
certain stabilizing role in the futures price. The reason may be 
that after the outbreak of the novel coronavirus, the country 
has adopted a series of policies such as issuing special loans 
and increasing rediscount support to deal with the impact of 
sudden public health events such as the epidemic, which has 
stabilized the volatility of futures prices to a certain extent. 

 
Table 4. The results of introducing two dummy variables 

Variable 
Short-term effect Medium and long term effect 

Coefficient Coefficient 

𝜑  
0.4963 

(0.6335) 
-0.2800 
(0.1899) 

𝜃  
-0.5142 
(0.6231) 

0.3641** 
(0.1852) 

𝛼  
5.64E-06*** 
(1.78E-06) 

7.86E-05*** 
(2.97E-06) 

𝛼  
0.0304*** 
(0.0084) 

0.3060*** 
(0.0139) 

𝛽  
0.9225*** 
(0.0220) 

0.6234*** 
(0.0116) 

𝛾  
2.11E-06* 
(1.18E-06) 

1.61E-05*** 
(3.48E-06) 

𝛾  
0.0002 

(0.0002) 
-1.39E-05*** 

(2.89E-06) 
F-statistic 0.0838 0.2054 

Prob.F 0.7723 0.6505 
Obs*R-squared 0.0842 0.2056 
Prob.Chi-Square 0.7717 0.6502 

Note: ***, ** and * are significance levels of 1%, 5% and 10% respectively, with standard error values in brackets. The 
same below. 

 



 

85 

 

4.3.  Robustness test 
In order to ensure the credibility and validity of the research 

conclusions, this paper constructs a GARCH-M model to test 
the robustness of the above fitting results. Considering the 
minimum information criterion and parameter significance 
comprehensively, the GARCH-M (1,1) model is established 
for both yield series. The GARCH-M (1,1) expressions 
introducing two virtual variables are as follows: 

 

t
2
t11t1t εlnσρRR                 (5) 

 

zcgjγyearγlnσβεααlnσ 21
2
1t1

2
1t10

2
t   (6) 

 

Equation (5) is the mean value equation. Equation (6) is the 
variance equation. Where ln𝜎  can be replaced by 𝜎  or 𝜎 ; 
𝜌   represents the reward for taking risks. Robustness test 
results Table 5 shows that the symbols of 𝛾  and 𝛾  in the 
introduction of double dummy variables GARCH-M (1,1) are 
the same as those in the introduction of double dummy 
variables ARMA(1,1)-GARCH(1,1), with the same 
significance, no matter in the short or medium term of the 
introduction of cotton option. On the whole, the regression 
results of this paper are relatively robust, which strengthens 
the conclusion that the introduction of cotton options has an 
impact on the price fluctuation of the underlying futures in the 
short and medium term. 

 
Table 5. Robustness test of cotton option launch effect 

Variable 
Short-term effect Medium and long term effect 

Coefficient Coefficient 

𝜑  
-0.0137 
(0.0508) 

-0.0205 
(0.0256) 

𝜌  
1.64E-05 

(6.36E-05) 
-6.05E-05* 
(3.46E-05) 

𝛼  
5.84E-06*** 
(1.93E-06) 

1.76E-05*** 
(1.77E-06) 

𝛼  
0.0304*** 
(0.0082) 

0.1227*** 
(0.0102) 

𝛽  
0.9207*** 
(0.0230) 

0.7979*** 
(0.0153) 

𝛾  
2.19E-06* 
(1.22E-06) 

6.93E-06*** 
(1.90E-06) 

𝛾  
0.0002 

(0.0002) 
-3.97E-06** 
(1.88E-06) 

F-statistic 0.0627 0.3686 
Prob.F 0.8024 0.5439 

Obs*R-squared 0.0629 0.3689 
Prob.Chi-Square 0.8019 0.5436 

 

5. Conclusions and Recommendations 
In this paper, the ARMA-GARCH model with two dummy 

variables is established to empirically test the impact of cotton 
options on the price fluctuation of the underlying futures in 
the short and medium term. The main conclusions are as 
follows: (1) The launch of cotton options intensified the price 
volatility of the underlying futures in the short term, and the 
volatility intensified in the medium and long term. (2) After 
the launch of cotton options, the supply of national policies to 
fight against the epidemic played a certain stabilizing role in 
the volatility of futures prices, but the policy effect has a time 
lag. 

Based on the above research conclusions, this paper puts 
forward the following suggestions. (1) Encourage 
institutional investors to use cotton options and futures to 
manage risk. Through preferential policies or institutional 
incentives, gradually optimize the structure of investors in the 
futures market, improve the professional level of investors, 
and reduce the occurrence of irrational behaviors such as 
speculative trading. Actively use cotton options and cotton 
futures hedging tools to reduce the exposure of entities to the 
wind direction. (2) Establish a risk prevention and control 
mechanism for unexpected public crises. Focus on and 
monitor the risk impact of sudden public crisis events on the 

cotton futures market, transform post-prevention to pre-
prevention, improve the management information system 
equipment, and accurately and effectively regulate the risk 
management of cotton options and cotton futures market. (3) 
Popularize the knowledge of options and futures and improve 
the transparency of cotton trading. Knowledge related to 
cotton options and futures was vigorously publicized through 
Weibo and public accounts, and special lectures were held for 
key groups such as state-owned enterprises and listed 
companies. Prudently operate the information release and 
disclosure of the futures market, improve the management 
system of market participants, and improve the transparency 
of cotton trading information. 

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