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Price Behaviour and Volatility Dynamics in Major Onion Markets of India: Evidence from Selected APMC Markets in Maharashtra

R
R.A. Thombare1,*
https://orcid.org/0009-0008-2326-6385
R
R.V. Chavan2
S
S.S. More2
S
S.V. Bharti2
T
T.B. Munde2
G
G.M. Bodkhe3
P
P.K. Rath2
1Vaikunth Mehta National Institute of Cooperative Management, Pune-411 007, Maharashtra, India.
2Department of Agricultural Economics, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani-431 402, Maharashtra, India.
3Department of Agricultural Economics, Sau. K.S.K. (Kaku) College of Agriculture, Beed-431 153, Maharashtra, India.

ABSTRACT

Background: Onion prices in India often fluctuate sharply due to seasonal production, perishability, irregular arrivals and supply-side disturbances. Maharashtra, the leading onion-producing state, plays a vital role in domestic price determination and export supply. Therefore, understanding price behaviour and volatility across major APMC markets is essential for improving market efficiency and ensuring price stability.

Methods: The study used monthly time-series data on onion prices and arrivals from 2005-06 to 2024-25 for seven major APMC markets in Maharashtra: Lasalgaon, Pune, Satara, Solapur, Jalgaon, Ahilyanagar and Vaijapur. Coefficient of variation (CV) and seasonal indices were used to examine variability and seasonality, while the augmented dickey-fuller (ADF) test assessed stationarity and ARCH-GARCH (1,1) models estimated volatility dynamics.

Result: The results indicated substantial seasonal fluctuations and persistent price volatility across all selected markets. Price variability reached 86.78 per cent in Lasalgaon during December and 81.46 per cent in Solapur during August. Seasonal indices showed prices declining to 48-61 per cent of the annual average during April and rising to 154.91 per cent in November in Lasalgaon. GARCH estimates confirmed highly persistent volatility across the markets.

INTRODUCTION

Onion (Allium cepa L.) is an important commercial vegetable crop with major roles in food security, nutrition and agricultural trade (NHRDF, 2023). India is among the leading onion-producing countries and Maharashtra is the principal producing state. Recent estimates indicate national production of about 30.77 million tonnes in 2024-25 (FAO, 2024; Ministry of Agriculture and Farmers Welfare, 2025).
       
Onion production and marketing are characterized by seasonal variability and price instability because of irregular arrivals, perishability and supply-demand imbalances (Rede et al., 2026; Saha et al., 2020). The crop is grown in kharif, late kharif and rabi seasons, with rabi contributing a major share of production because of its better storage quality (Sharma and Chauhan, 2024; Sharma and Dogra, 2017). Recent ARCC literature also documents substantial growth and instability in vegetable production and emphasizes the importance of understanding production risk (Gupta et al., 2025). Weather variability, particularly erratic rainfall, can further affect onion output and market supply (Matthan, 2022). National onion production declined from 302.08 lakh tonnes in 2022-23 to 242.12 lakh tonnes in 2023-24 (Ministry of Consumer Affairs, Food and Public Distribution, 2024). Maharashtra has a dominant position in onion production (Sharma et al., 2025; Jadhav et al., 2020), while APMC markets and institutions such as NAFED undertake marketing, procurement and buffer-stock operations (Kakuste, 2020; Saxena et al., 2017).
       
Price volatility in onions is associated with seasonal arrivals, storage constraints and policy interventions such as export restrictions and minimum export price (MEP) measures (Birthal et al., 2019). Post-harvest losses and inadequate storage can further intensify supply fluctuations (Anbukkarasi and Banumathi, 2013; Goyal et al., 2024; NABCONS, 2017). Recent ARCC review evidence likewise highlights the relationship between vegetable arrivals and prices and the importance of seasonality in price behaviour (Sethi et al., 2025). In general, abundant arrivals during peak harvest periods are associated with lower prices, whereas lean-season arrivals are associated with higher prices (Saha et al., 2020; Roy, 2021; Sharma et al., 2021).
       
Understanding price fluctuations and seasonal patterns is important for improving market efficiency and designing appropriate interventions (Kumar et al., 2021; Paul et al., 2016; Reddy et al., 2017; Areef et al., 2019). Previous studies have also examined growth and market performance of onion markets (Kumbhar et al., 2019).

MATERIALS AND METHODS

Selection of study area and markets
 
Maharashtra was purposively selected due to its leading contribution in onion production, area and market arrivals in India (Sidhu et al., 2019; Kumar and Ahmed, 2018). Major onion- producing districts contributing nearly 90 per cent of production (Shinde and Burark, 2012) were selected and representative APMC markets Lasalgaon, Pune, Satara, Solapur, Ahilyanagar, Jalgaon and Vaijapur were chosen based on arrivals, price leadership and regional representation.
 
Data sources and period
 
Monthly secondary data on onion prices and arrivals for 2005-06 to 2024-25 were obtained from selected APMC markets, AGMARKNET, FAO and other official sources. The research was carried out in the Department of Agricultural Economics, Vasantrao Naik Marathwada Krishi Vidyapeeth (VNMKV), Parbhani, Maharashtra, India.
 
Analytical framework
 
Measurement of instability
 
The instability in onion prices and arrivals was measured using the coefficient of variation (CV) (Cuddy and Della Valle, 1978).
 
 
CDVI = CV × √(1 - R²)
 
 
Where,
σ= Standard deviation.
μ= Mean.
R2= Coefficient of determination.
       
The instability in onion prices and arrivals was estimated using the cuddy-della valle instability index (CDVI), which adjusts the coefficient of variation for the trend effect in the time series (Gummagolmath et al., 2011; Jain and Pal, 2013; Patil and Kerur, 2016).
 
Seasonal analysis
 
The ratio-to-moving average method was used to estimate seasonal fluctuations in onion prices and arrivals by removing trend and cyclical components (Sailaja, 2019).
 
 
 
Where,
SI= Seasonal index.
Yt= Actual value at time t.
MAt= Centered moving average.
       
The indices were adjusted using a correction factor:
 
 
Stationarity test
 
The stationarity of onion price and market arrival series was tested using the augmented dickey-fuller test (ADF) (Dickey and Fuller, 1979).
 
 
 
Where,
Yt= Price series.
Δ= First difference.
εt= Error term.
 
Volatility modelling
 
To capture volatility clustering and conditional variance, an ARCH-GARCH approach was used. A GARCH (1,1) model was specified to estimate the persistence and magnitude of onion price volatility (Baishya and Kumar, 2021; Rakshit, 2021; Ghosh et al., 2020; Bollerslev, 1986).
 
Mean equation
 
  
 
Variance equation
 
 
Where,
σt2 = Conditional variance.
ω= Constant.
α= ARCH parameter short-run shock effect.
β= GARCH parameter volatility persistence.
If α + β ≈ 1, it indicates high volatility persistence.
 
Diagnostic test
 
The adequacy of the fitted volatility model was examined using the box-ljung test (Box and Ljung, 1978).
 
Where,
n= Sample size.
= Autocorrelation at lag k.
Q= Box-ljung test statistic.
m= Number of lags.
rk= Autocorrelation at lag k.

RESULTS AND DISCUSSION

Table 1 shows substantial seasonal instability in onion prices and arrivals across the selected markets. Price variability was generally higher during August-December and lower during March-April. Lasalgaon recorded the highest monthly price CV in December (86.78%), while Solapur recorded 81.46% in August. Arrival variability exceeded price variability in all markets, indicating strong seasonal concentration of supply. Jalgaon showed the greatest arrival instability, whereas Pune had comparatively lower variability. These patterns are consistent with earlier evidence on onion price and arrival behaviour (Saha et al., 2020; Ghosh et al., 2020; Baishya and Kumar, 2021).

Table 1: Month-wise coefficient of variation (CV%) for prices and arrivals of onion in different (APMC) markets of Maharashtraa (2005-06 to 2024-25).


       
Table 2 and 3 show that greater seasonal concentration of arrivals was associated with higher price instability. Lasalgaon recorded the highest average arrivals (415,221 tonnes) and mean price (₹1,334.36/q), with moderate arrival instability (CDVI 48.82%) but relatively high price instability (CDVI 69.47%). This indicates that prices remained sensitive to supply-demand conditions and policy-related shocks despite relatively stable inflows (Birthal et al., 2019; Rakshit, 2021). Low R2 values suggest that short-term fluctuations explain a substantial part of the observed variation. Similar evidence on instability in vegetable production and prices has been reported in recent ARCC research (Gupta et al., 2025; Sethi et al., 2025).

Table 2: Growth and instability for onion arrivals in different (APMC) markets in Maharashtraa (2005-06 to 2024-25).



Table 3: Growth and instability of onion prices in (APMC) markets in Maharashtraa (2005-06 to 2024-25).


       
Table 4 shows a consistent seasonal pattern in onion prices. Prices remained below the annual average during March-April and increased thereafter, reaching their highest levels mainly during October-November. April recorded the lowest seasonal indices across the markets, reflecting abundant rabi arrivals. Prices recovered from June to September as stored stocks declined. Similar seasonal movements have been reported for onion and other agricultural commodities (Roy, 2021; Saha et al., 2020; Vasanthi et al., 2024; Jadeja et al., 2025).

Table 4: Seasonal indices for monthly prices of onion in selected markets in Maharashtraa (2005-06 to 2024-25).


       
Fig 1 confirms a U-shaped seasonal price pattern, with lower prices during harvest months and higher prices during lean supply periods.

Fig 1: Seasonal indices for monthly prices of onion in selected markets of Maharashtra.


       
Table 5 shows that onion arrivals were above the annual average mainly during January-March, with a secondary increase in December due to late kharif arrivals and release of stored stocks. Similar seasonal concentration of arrivals has been reported in previous studies (Roy, 2021; Saha et al., 2020; Vasanthi et al., 2024).

Table 5: Seasonal indices for monthly arrivals of onion in selected markets in Maharashtraa (2005-06 to 2024-25).


       
January and March recorded the highest arrival indices in several markets. January ranged from 119.90 in Ahilyanagar to 224.63 in Solapur, while March reached 161.44 in Satara and 146.22 in Solapur. The concentration reflects bulk marketing of rabi onion, which contributes a major share of Maharashtra’s annual production (Roy, 2021).
       
During the monsoon, arrival indices declined below the annual average in several markets, including Solapur (45.56 in June), Jalgaon (49.46 in September) and Vaijapur (54.04 in October). This decline is consistent with depletion of stored rabi stocks, storage losses and limited kharif supply (Saha et al., 2020; Chand and Jumrani, 2013). A secondary rise occurred in December, particularly in Solapur (179.72) and Jalgaon (182.01), reflecting late kharif arrivals and release of stored produce.
       
Fig 2 confirms strong seasonal concentration of arrivals, with heavy inflows during January-March, a decline during the monsoon and partial recovery in December.

​

Fig 2: Seasonal indices for monthly arrivals of onion in selected markets of Maharashtraa.


       
Table 6 reports the ADF results. All statistics are negative and statistically significant at the 5% level, with p-values below 0.05. Thus, the null hypothesis of a unit root is rejected for all selected markets (Dickey and Fuller, 1979).

Table 6: Results of the augmented Dickey-Fuller (ADF) stationarity test for onion prices across selected markets.


       
Table 7 reports the Box-Ljung test results. All test statistics are statistically significant, with very low p-values, indicating serial dependence in the price series. The results support the presence of volatility clustering and motivate the use of ARCH-GARCH modelling (Box and Ljung, 1978; Engle, 1982).

Table 7: Results of the box-ljung test for onion prices across selected markets.


       
Table 8 presents the GARCH (1,1) estimates. The mean price parameter (ì) is positive and statistically significant at the 1% level in all markets.

Table 8: Comparative GARCH (1,1) model parameter estimates for onion price volatility across selected markets.


       
The ARCH coefficients (α1) are positive and highly significant in all markets. Moderate values occur in Lasalgaon (0.429), Pune (0.510), Satara (0.488) and Vaijapur (0.3641), indicating a strong short-run response to recent shocks. Very high values occur in Solapur (0.999), Jalgaon (0.998) and Ahilyanagar (0.999), indicating a particularly strong immediate response of volatility to recent shocks.
       
The GARCH coefficients (β1) vary across markets. They are significant in Lasalgaon (0.569), Pune (0.489), Satara (0.511) and Vaijapur (0.6341), indicating persistence of past volatility. In Solapur (0.00002), Jalgaon (0.0002) and Ahilyanagar (0.0006), the coefficients are very small, indicating a limited direct contribution of lagged conditional variance relative to recent shocks.
       
The variance constant (ω) also differs across markets. It is moderate and insignificant in Lasalgaon (1118.68), Pune (763.79), Satara (874.38) and Vaijapur (759.46), but significantly higher in Solapur (38,244.0), Jalgaon (39,042.0) and Ahilyanagar (58,555.0), indicating higher baseline variance in these markets.
       
The sum α1 + β1 ranges from 0.998 to 0.999 across markets, indicating highly persistent volatility. This persistence supports the use of GARCH-type models and is consistent with earlier evidence on agricultural commodity price behaviour (Engle, 1982; Ghosh et al., 2020; Rakshit, 2021; Baishya and Kumar, 2021).
       
Fig 3-9 show clear volatility clustering across the selected markets, supporting the GARCH estimates. Major volatility spikes occurred during 2011-12, 2013-14, 2014-15, 2016-17, 2019-20 and 2022-24. Lasalgaon, Satara, Pune and Jalgaon showed more frequent clusters, while Vaijapur displayed comparatively smoother movements. These patterns confirm the recurrent nature of onion price volatility across Maharashtra markets.

Fig 3: Estimated conditional volatility of monthly onion prices in lasalgaon market.



Fig 4: Estimated conditional volatility of monthly onion prices in Pune market.



Fig 5: Estimated conditional volatility of monthly onion prices in Satara market.



Fig 6: Estimated conditional volatility of monthly onion prices in Solapur market.



Fig 7: Estimated conditional volatility of monthly onion prices in Jalgaon market.



Fig 8: Estimated conditional volatility of monthly onion prices in Ahilyanagar market.



Fig 9: Estimated conditional volatility of monthly onion prices in Vaijapur market.

CONCLUSION

The study revealed strong seasonality, substantial instability and persistent volatility in onion prices across the selected Maharashtra markets. Prices were generally lower during peak rabi arrivals and higher during lean supply periods. Arrival variability exceeded price variability, while GARCH estimates indicated highly persistent volatility. The findings underscore the importance of improved storage, reliable market information and predictable trade policies for reducing supply-related price instability.

ACKNOWLEDGEMENT

The authors sincerely acknowledge the support and assistance provided by the Agricultural Produce Market Committees (APMCs), APEDA, AGMARKNET, and other government agencies for providing the necessary data. The authors are also grateful to the Department of Agricultural Economics, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani, for providing valuable guidance and infrastructure to facilitate the research. The corresponding author also sincerely acknowledges the support, encouragement, and institutional guidance provided by the Vaikunth Mehta National Institute of Cooperative Management (VAMNICOM), Pune, in facilitating and supporting research activities.
 
Disclaimers
 
The views and conclusions expressed in this article are solely those of the authors and do not necessarily reflect the views of their affiliated institutions.
 
Policy implications
 
Strengthen buffer stock operations
 
Enhance strategic procurement and buffer stock management through NAFED using timely market information.
Develop scientific storage infrastructure
 
Expand modern storage facilities to reduce post-harvest losses and stabilize market supplies.
 
Establish a real-time market intelligence system
 
Strengthen digital platforms such as e-NAM for transparent price discovery and early volatility forecasting.
 
Strengthen the price stabilization fund (PSF)
 
Improve PSF responsiveness to facilitate timely market interventions during price fluctuations.
 
Promote farmer producer organizations (FPOs)
 
Support collective marketing and storage to improve farmers’ market access and bargaining power.
 
Ensure a stable trade policy
 
Adopt predictable export and trade policies aligned with domestic production and price conditions.

CONFLICT OF INTEREST

The authors declare that there is no conflict of interest regarding the publication of this research paper. The research was conducted independently without any financial or commercial influence that could affect the findings of the study.

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    Full Research Article

    Price Behaviour and Volatility Dynamics in Major Onion Markets of India: Evidence from Selected APMC Markets in Maharashtra

    R
    R.A. Thombare1,*
    https://orcid.org/0009-0008-2326-6385
    R
    R.V. Chavan2
    S
    S.S. More2
    S
    S.V. Bharti2
    T
    T.B. Munde2
    G
    G.M. Bodkhe3
    P
    P.K. Rath2
    1Vaikunth Mehta National Institute of Cooperative Management, Pune-411 007, Maharashtra, India.
    2Department of Agricultural Economics, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani-431 402, Maharashtra, India.
    3Department of Agricultural Economics, Sau. K.S.K. (Kaku) College of Agriculture, Beed-431 153, Maharashtra, India.

    ABSTRACT

    Background: Onion prices in India often fluctuate sharply due to seasonal production, perishability, irregular arrivals and supply-side disturbances. Maharashtra, the leading onion-producing state, plays a vital role in domestic price determination and export supply. Therefore, understanding price behaviour and volatility across major APMC markets is essential for improving market efficiency and ensuring price stability.

    Methods: The study used monthly time-series data on onion prices and arrivals from 2005-06 to 2024-25 for seven major APMC markets in Maharashtra: Lasalgaon, Pune, Satara, Solapur, Jalgaon, Ahilyanagar and Vaijapur. Coefficient of variation (CV) and seasonal indices were used to examine variability and seasonality, while the augmented dickey-fuller (ADF) test assessed stationarity and ARCH-GARCH (1,1) models estimated volatility dynamics.

    Result: The results indicated substantial seasonal fluctuations and persistent price volatility across all selected markets. Price variability reached 86.78 per cent in Lasalgaon during December and 81.46 per cent in Solapur during August. Seasonal indices showed prices declining to 48-61 per cent of the annual average during April and rising to 154.91 per cent in November in Lasalgaon. GARCH estimates confirmed highly persistent volatility across the markets.

    INTRODUCTION

    Onion (Allium cepa L.) is an important commercial vegetable crop with major roles in food security, nutrition and agricultural trade (NHRDF, 2023). India is among the leading onion-producing countries and Maharashtra is the principal producing state. Recent estimates indicate national production of about 30.77 million tonnes in 2024-25 (FAO, 2024; Ministry of Agriculture and Farmers Welfare, 2025).
           
    Onion production and marketing are characterized by seasonal variability and price instability because of irregular arrivals, perishability and supply-demand imbalances (Rede et al., 2026; Saha et al., 2020). The crop is grown in kharif, late kharif and rabi seasons, with rabi contributing a major share of production because of its better storage quality (Sharma and Chauhan, 2024; Sharma and Dogra, 2017). Recent ARCC literature also documents substantial growth and instability in vegetable production and emphasizes the importance of understanding production risk (Gupta et al., 2025). Weather variability, particularly erratic rainfall, can further affect onion output and market supply (Matthan, 2022). National onion production declined from 302.08 lakh tonnes in 2022-23 to 242.12 lakh tonnes in 2023-24 (Ministry of Consumer Affairs, Food and Public Distribution, 2024). Maharashtra has a dominant position in onion production (Sharma et al., 2025; Jadhav et al., 2020), while APMC markets and institutions such as NAFED undertake marketing, procurement and buffer-stock operations (Kakuste, 2020; Saxena et al., 2017).
           
    Price volatility in onions is associated with seasonal arrivals, storage constraints and policy interventions such as export restrictions and minimum export price (MEP) measures (Birthal et al., 2019). Post-harvest losses and inadequate storage can further intensify supply fluctuations (Anbukkarasi and Banumathi, 2013; Goyal et al., 2024; NABCONS, 2017). Recent ARCC review evidence likewise highlights the relationship between vegetable arrivals and prices and the importance of seasonality in price behaviour (Sethi et al., 2025). In general, abundant arrivals during peak harvest periods are associated with lower prices, whereas lean-season arrivals are associated with higher prices (Saha et al., 2020; Roy, 2021; Sharma et al., 2021).
           
    Understanding price fluctuations and seasonal patterns is important for improving market efficiency and designing appropriate interventions (Kumar et al., 2021; Paul et al., 2016; Reddy et al., 2017; Areef et al., 2019). Previous studies have also examined growth and market performance of onion markets (Kumbhar et al., 2019).

    MATERIALS AND METHODS

    Selection of study area and markets
     
    Maharashtra was purposively selected due to its leading contribution in onion production, area and market arrivals in India (Sidhu et al., 2019; Kumar and Ahmed, 2018). Major onion- producing districts contributing nearly 90 per cent of production (Shinde and Burark, 2012) were selected and representative APMC markets Lasalgaon, Pune, Satara, Solapur, Ahilyanagar, Jalgaon and Vaijapur were chosen based on arrivals, price leadership and regional representation.
     
    Data sources and period
     
    Monthly secondary data on onion prices and arrivals for 2005-06 to 2024-25 were obtained from selected APMC markets, AGMARKNET, FAO and other official sources. The research was carried out in the Department of Agricultural Economics, Vasantrao Naik Marathwada Krishi Vidyapeeth (VNMKV), Parbhani, Maharashtra, India.
     
    Analytical framework
     
    Measurement of instability
     
    The instability in onion prices and arrivals was measured using the coefficient of variation (CV) (Cuddy and Della Valle, 1978).
     
     
    CDVI = CV × √(1 - R²)
     
     
    Where,
    σ= Standard deviation.
    μ= Mean.
    R2= Coefficient of determination.
           
    The instability in onion prices and arrivals was estimated using the cuddy-della valle instability index (CDVI), which adjusts the coefficient of variation for the trend effect in the time series (Gummagolmath et al., 2011; Jain and Pal, 2013; Patil and Kerur, 2016).
     
    Seasonal analysis
     
    The ratio-to-moving average method was used to estimate seasonal fluctuations in onion prices and arrivals by removing trend and cyclical components (Sailaja, 2019).
     
     
     
    Where,
    SI= Seasonal index.
    Yt= Actual value at time t.
    MAt= Centered moving average.
           
    The indices were adjusted using a correction factor:
     
     
    Stationarity test
     
    The stationarity of onion price and market arrival series was tested using the augmented dickey-fuller test (ADF) (Dickey and Fuller, 1979).
     
     
     
    Where,
    Yt= Price series.
    Δ= First difference.
    εt= Error term.
     
    Volatility modelling
     
    To capture volatility clustering and conditional variance, an ARCH-GARCH approach was used. A GARCH (1,1) model was specified to estimate the persistence and magnitude of onion price volatility (Baishya and Kumar, 2021; Rakshit, 2021; Ghosh et al., 2020; Bollerslev, 1986).
     
    Mean equation
     
      
     
    Variance equation
     
     
    Where,
    σt2 = Conditional variance.
    ω= Constant.
    α= ARCH parameter short-run shock effect.
    β= GARCH parameter volatility persistence.
    If α + β ≈ 1, it indicates high volatility persistence.
     
    Diagnostic test
     
    The adequacy of the fitted volatility model was examined using the box-ljung test (Box and Ljung, 1978).
     
    Where,
    n= Sample size.
    = Autocorrelation at lag k.
    Q= Box-ljung test statistic.
    m= Number of lags.
    rk= Autocorrelation at lag k.

    RESULTS AND DISCUSSION

    Table 1 shows substantial seasonal instability in onion prices and arrivals across the selected markets. Price variability was generally higher during August-December and lower during March-April. Lasalgaon recorded the highest monthly price CV in December (86.78%), while Solapur recorded 81.46% in August. Arrival variability exceeded price variability in all markets, indicating strong seasonal concentration of supply. Jalgaon showed the greatest arrival instability, whereas Pune had comparatively lower variability. These patterns are consistent with earlier evidence on onion price and arrival behaviour (Saha et al., 2020; Ghosh et al., 2020; Baishya and Kumar, 2021).

    Table 1: Month-wise coefficient of variation (CV%) for prices and arrivals of onion in different (APMC) markets of Maharashtraa (2005-06 to 2024-25).


           
    Table 2 and 3 show that greater seasonal concentration of arrivals was associated with higher price instability. Lasalgaon recorded the highest average arrivals (415,221 tonnes) and mean price (₹1,334.36/q), with moderate arrival instability (CDVI 48.82%) but relatively high price instability (CDVI 69.47%). This indicates that prices remained sensitive to supply-demand conditions and policy-related shocks despite relatively stable inflows (Birthal et al., 2019; Rakshit, 2021). Low R2 values suggest that short-term fluctuations explain a substantial part of the observed variation. Similar evidence on instability in vegetable production and prices has been reported in recent ARCC research (Gupta et al., 2025; Sethi et al., 2025).

    Table 2: Growth and instability for onion arrivals in different (APMC) markets in Maharashtraa (2005-06 to 2024-25).



    Table 3: Growth and instability of onion prices in (APMC) markets in Maharashtraa (2005-06 to 2024-25).


           
    Table 4 shows a consistent seasonal pattern in onion prices. Prices remained below the annual average during March-April and increased thereafter, reaching their highest levels mainly during October-November. April recorded the lowest seasonal indices across the markets, reflecting abundant rabi arrivals. Prices recovered from June to September as stored stocks declined. Similar seasonal movements have been reported for onion and other agricultural commodities (Roy, 2021; Saha et al., 2020; Vasanthi et al., 2024; Jadeja et al., 2025).

    Table 4: Seasonal indices for monthly prices of onion in selected markets in Maharashtraa (2005-06 to 2024-25).


           
    Fig 1 confirms a U-shaped seasonal price pattern, with lower prices during harvest months and higher prices during lean supply periods.

    Fig 1: Seasonal indices for monthly prices of onion in selected markets of Maharashtra.


           
    Table 5 shows that onion arrivals were above the annual average mainly during January-March, with a secondary increase in December due to late kharif arrivals and release of stored stocks. Similar seasonal concentration of arrivals has been reported in previous studies (Roy, 2021; Saha et al., 2020; Vasanthi et al., 2024).

    Table 5: Seasonal indices for monthly arrivals of onion in selected markets in Maharashtraa (2005-06 to 2024-25).


           
    January and March recorded the highest arrival indices in several markets. January ranged from 119.90 in Ahilyanagar to 224.63 in Solapur, while March reached 161.44 in Satara and 146.22 in Solapur. The concentration reflects bulk marketing of rabi onion, which contributes a major share of Maharashtra’s annual production (Roy, 2021).
           
    During the monsoon, arrival indices declined below the annual average in several markets, including Solapur (45.56 in June), Jalgaon (49.46 in September) and Vaijapur (54.04 in October). This decline is consistent with depletion of stored rabi stocks, storage losses and limited kharif supply (Saha et al., 2020; Chand and Jumrani, 2013). A secondary rise occurred in December, particularly in Solapur (179.72) and Jalgaon (182.01), reflecting late kharif arrivals and release of stored produce.
           
    Fig 2 confirms strong seasonal concentration of arrivals, with heavy inflows during January-March, a decline during the monsoon and partial recovery in December.

    ​

    Fig 2: Seasonal indices for monthly arrivals of onion in selected markets of Maharashtraa.


           
    Table 6 reports the ADF results. All statistics are negative and statistically significant at the 5% level, with p-values below 0.05. Thus, the null hypothesis of a unit root is rejected for all selected markets (Dickey and Fuller, 1979).

    Table 6: Results of the augmented Dickey-Fuller (ADF) stationarity test for onion prices across selected markets.


           
    Table 7 reports the Box-Ljung test results. All test statistics are statistically significant, with very low p-values, indicating serial dependence in the price series. The results support the presence of volatility clustering and motivate the use of ARCH-GARCH modelling (Box and Ljung, 1978; Engle, 1982).

    Table 7: Results of the box-ljung test for onion prices across selected markets.


           
    Table 8 presents the GARCH (1,1) estimates. The mean price parameter (ì) is positive and statistically significant at the 1% level in all markets.

    Table 8: Comparative GARCH (1,1) model parameter estimates for onion price volatility across selected markets.


           
    The ARCH coefficients (α1) are positive and highly significant in all markets. Moderate values occur in Lasalgaon (0.429), Pune (0.510), Satara (0.488) and Vaijapur (0.3641), indicating a strong short-run response to recent shocks. Very high values occur in Solapur (0.999), Jalgaon (0.998) and Ahilyanagar (0.999), indicating a particularly strong immediate response of volatility to recent shocks.
           
    The GARCH coefficients (β1) vary across markets. They are significant in Lasalgaon (0.569), Pune (0.489), Satara (0.511) and Vaijapur (0.6341), indicating persistence of past volatility. In Solapur (0.00002), Jalgaon (0.0002) and Ahilyanagar (0.0006), the coefficients are very small, indicating a limited direct contribution of lagged conditional variance relative to recent shocks.
           
    The variance constant (ω) also differs across markets. It is moderate and insignificant in Lasalgaon (1118.68), Pune (763.79), Satara (874.38) and Vaijapur (759.46), but significantly higher in Solapur (38,244.0), Jalgaon (39,042.0) and Ahilyanagar (58,555.0), indicating higher baseline variance in these markets.
           
    The sum α1 + β1 ranges from 0.998 to 0.999 across markets, indicating highly persistent volatility. This persistence supports the use of GARCH-type models and is consistent with earlier evidence on agricultural commodity price behaviour (Engle, 1982; Ghosh et al., 2020; Rakshit, 2021; Baishya and Kumar, 2021).
           
    Fig 3-9 show clear volatility clustering across the selected markets, supporting the GARCH estimates. Major volatility spikes occurred during 2011-12, 2013-14, 2014-15, 2016-17, 2019-20 and 2022-24. Lasalgaon, Satara, Pune and Jalgaon showed more frequent clusters, while Vaijapur displayed comparatively smoother movements. These patterns confirm the recurrent nature of onion price volatility across Maharashtra markets.

    Fig 3: Estimated conditional volatility of monthly onion prices in lasalgaon market.



    Fig 4: Estimated conditional volatility of monthly onion prices in Pune market.



    Fig 5: Estimated conditional volatility of monthly onion prices in Satara market.



    Fig 6: Estimated conditional volatility of monthly onion prices in Solapur market.



    Fig 7: Estimated conditional volatility of monthly onion prices in Jalgaon market.



    Fig 8: Estimated conditional volatility of monthly onion prices in Ahilyanagar market.



    Fig 9: Estimated conditional volatility of monthly onion prices in Vaijapur market.

    CONCLUSION

    The study revealed strong seasonality, substantial instability and persistent volatility in onion prices across the selected Maharashtra markets. Prices were generally lower during peak rabi arrivals and higher during lean supply periods. Arrival variability exceeded price variability, while GARCH estimates indicated highly persistent volatility. The findings underscore the importance of improved storage, reliable market information and predictable trade policies for reducing supply-related price instability.

    ACKNOWLEDGEMENT

    The authors sincerely acknowledge the support and assistance provided by the Agricultural Produce Market Committees (APMCs), APEDA, AGMARKNET, and other government agencies for providing the necessary data. The authors are also grateful to the Department of Agricultural Economics, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani, for providing valuable guidance and infrastructure to facilitate the research. The corresponding author also sincerely acknowledges the support, encouragement, and institutional guidance provided by the Vaikunth Mehta National Institute of Cooperative Management (VAMNICOM), Pune, in facilitating and supporting research activities.
     
    Disclaimers
     
    The views and conclusions expressed in this article are solely those of the authors and do not necessarily reflect the views of their affiliated institutions.
     
    Policy implications
     
    Strengthen buffer stock operations
     
    Enhance strategic procurement and buffer stock management through NAFED using timely market information.
    Develop scientific storage infrastructure
     
    Expand modern storage facilities to reduce post-harvest losses and stabilize market supplies.
     
    Establish a real-time market intelligence system
     
    Strengthen digital platforms such as e-NAM for transparent price discovery and early volatility forecasting.
     
    Strengthen the price stabilization fund (PSF)
     
    Improve PSF responsiveness to facilitate timely market interventions during price fluctuations.
     
    Promote farmer producer organizations (FPOs)
     
    Support collective marketing and storage to improve farmers’ market access and bargaining power.
     
    Ensure a stable trade policy
     
    Adopt predictable export and trade policies aligned with domestic production and price conditions.

    CONFLICT OF INTEREST

    The authors declare that there is no conflict of interest regarding the publication of this research paper. The research was conducted independently without any financial or commercial influence that could affect the findings of the study.

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