COVID-19 and prices of pulses in Major markets of India: Impact of nationwide lockdown

Ranjit Kumar Paul; Md Yeasin

doi:10.1371/journal.pone.0272999

Abstract

The COVID-19 pandemic has impacted almost all the sectors including agriculture in the country. The present paper investigates the impact of COVID-19 induced lockdown on both wholesale and retail prices of major pulses in India. The daily wholesale and retail price data on five major pulses namely Lentil, Moong, Arhar, Urad and Gram are collected for five major markets in India namely Delhi, Mumbai, Kolkata, Chennai and Hyderabad during the period January, 2019 to September, 2020 from Ministry of Consumer Affairs, Food & Public Distribution, Government of India. The Government of India declared nationwide lockdown since March, 24, to May, 31, 2020 in different phases in order to restrict the spread of the infection due to COVID-19. To see the impact of lockdown on price and price volatility, time series model namely Autoregressive integrated moving average (ARIMA) model with error following Generalized autoregressive conditional heteroscedastic (GARCH) model incorporating exogenous variable as lockdown dummy in both mean as well variance equations. It is observed that in almost all the markets, lockdown has significant impact on price of the pulses whereas in few cases, it has significant impact on price volatility.

Citation: Paul RK, Yeasin M (2022) COVID-19 and prices of pulses in Major markets of India: Impact of nationwide lockdown. PLoS ONE 17(8): e0272999. https://doi.org/10.1371/journal.pone.0272999

Editor: Ricky Chee Jiun Chia, Universiti Malaysia Sabah, MALAYSIA

Received: January 10, 2022; Accepted: July 31, 2022; Published: August 25, 2022

Copyright: © 2022 Paul, Yeasin. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data is collected from public database of Government of India (https://consumeraffairs.nic.in). The relevant computer program can be accessed in https://github.com/ranjitstat/Covid-pulse.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

COVID-19 and prices of pulses in major markets of India: Impact of nationwide lockdown

1. Introduction

The outbreak of covid-19 caused a disruptive damage to India and other nations in the world. In January 2020, The World Health Organization (WHO) declared COVID-19 as a pandemic disease. International Monetary Fund (IMF) predicted that the global economy is going to downsize by more than 3% in 2020. According to International Labour Organisation (ILO) the COVID-19 pandemic, along with lockdowns, mobility limitations and social-distancing norms has not only surge unemployment and income loss but also effected price inflation and spending pattern of the consumers. India, one of the most populated country, imposed a complete lockdown from 24^th March 2020. Unfortunately, lockdown was enforced shortly before the Rabi (Winter) season which is crucial time to harvest some of the major crops. Although the government of India relaxed the lockdown norms for agriculture related operations, still it affected demand and supply chains of agriculture commodities due to restrictions in transportation and labour mobility [1]. Consumers all across the world have reacted to the crisis and has caused to typical shopping behaviour. India leads the way, with 69–78% of customers expressing a desire to modify their purchasing habits in lockdown periods [2]. The lockdown resulted in a 23.9 percent drop in GDP in the first quarter (April to June) of 2020–21. Only the agriculture sector had positive growth (3.4 percent), although it is lesser than its previous quarter growth (5.9 percent). So, due to COVID-19 pandemic, Indian economy witnessed decline of agriculture growth by 2.5% [3]. Price fluctuations in agricultural commodities has a significant impact on poverty reduction and food security across the country. The supply of agricultural goods was particularly impacted, posing a major threat to the food and nutritional security of the most vulnerable section of society [4]. Bairagi et al. reported that price of basic food items such as wheat flour and rice in India increased significantly during the pandemic period [5]. Ruan et al. studied the impact of COVID-19 and nationwide lockdowns on vegetable prices in China [6]. They reported that the lockdown policy caused a large and immediate surge in price and price dispersion of Chinese cabbage. Akter examined the impact of ‘stay-at-home’ restrictions during of COVID-19 on food prices in 31 European countries [7]. It was found that the most significant surges in prices were meat, fish & seafood, and vegetables. Hobbs J assessed the implications of the COVID-19 pandemic for food supply chains in US-Canada border [8]. Yu et al. studied the impact of COVID-19 on food prices in China and reported no significant impact on rice and wheat flour prices, but significantly positive impact on cabbages prices [9]. Laborde et al. reported that COVID-19-related lockdown measures explain most of the fall in output, whereas declines in savings soften the adverse impacts on food consumption [10]. Ahmed et al. investigated the Impact of COVID-19 on agricultural production and distribution in South Asia and found that Livestock, vegetable, fruit, and fishing sector were more affected than the crop sector due to the pandemic [11]. Vyas et al. assessed the early impacts of the COVID-19 pandemic on the food supply chain and farm distress in India [12]. They showed wide-scale impacts across different components of the food supply chain. Kumari et al. reported that buyer and supplier uncertainty created by the COVID-19 lockdowns encouraged stakeholders to engage in relational governance initiatives [13]. Alina et al. studied the Impact of Covid-19 on the agri-food sector in Romania [14]. Janssen et al. found that it was more difficult for people to eat healthily during a COVID-19 confinement in terms of fresh fruit and vegetables [15]. The lockdown’s impact on the Indian agricultural sectors has been complicated and diversified across several sectors. The impact of the lockout on perishable food (milk, vegetables and fruits) products was worrisome, but there was also a substantial impact on the food grains, pulses, and oilseeds industry. Paul and Birthal investigated the impact of lockdown on wholesale and retail prices of tomato, onion and potato in major markets of India [16]. They reported surge in price volatility in all the studied commodities.

India is the world’s leading producer (25% of the world production), consumer (27% of the world consumption), and importer (14% of world imports) of pulses [17]. Approximately 20% area of total food grains cultivation is under the pulse’s cultivation and pulses accounts for 7% of the total food grains production in India [18]. Gram is the most dominant pulse with a share of around 40% in the total production followed by Arhar at 15 to 20% and Urad and Moong at around 8–10% each. Pulses are one of the important sources of protein in Indian diets. Depending on the species and variety, pulses have a protein content ranging between 17 to 32 percent. Pulses contain good amount of protein, carbohydrates, and dietary fibre. A good description of nutritional values of different pulses is given by Venkidasamy et al [19]. Urad is a rich source of protein, fat, carbohydrates, and Vitamin B. Fiber, protein, and folic acid are all abundant in arhar dal. Gram contains high-protein, zinc, and calcium. Lentil is a high-protein, vitamin B₁, potassium, and amino acid-rich pulse. In Moong, Phosphorus, magnesium, potassium, and other essential nutrients may be found. Therefore, one pulse may substitute other with respect to nutritional value.

Price of pulses in retail as well as wholesale markets has been fluctuated during and after lockdown due to disrupt supply chains and change in consuming pattern of consumers. A significant change in the consumption habits of consumers was observed during the lockdown phase and the consumers were spending mostly on essentials commodities. In this study we investigated the effect of lockdown on the prices of major pulses in India’s leading wholesales and retails markets. These issues are more prominent in the context of a developing country where the food supply chain is long and fragile [20]. Generally, pulses exhibit high price volatility [21]. Pulse price volatility is one of the key concerns for policymakers. Volatility is the sudden unexpected rise or fall in the series. A large number of models have been developed to capture volatility since development of Autoregressive conditional heteroscedastic (ARCH) model by Engle (1982) followed by Generalized ARCH (GARCH) model by Bollerslev (1986) [22,23]. There are various ways of measuring price volatility. Among them, GARCH model is most popular model due to its wide applicability into various field [24]. Various researchers already proved supremacy of GARCH model in volatility estimation in agriculture and allied sectors [25]. Paul et al. demonstrated that GARCH model is superior than usual ARIMA model for forecasting the spices export from India [26]. Ghosh et al. reported efficiency of nonlinear time series model e.g. GARCH in modelling conditional volatility; Paul et al. studied application of ARIMAX-GARCH model for forecasting monsoon rainfall in India [27,28]. Paul applied GARCH model by introducing exogenous variable in mean model for forecasting wheat yield [29]. Rakhsit et al. demonstrated the different extension of GARCH model to capture the asymmetricity present in onion price volatility [30]. When volatility is influenced by exogeneous variables, the simple GARCH model is not sufficient to model volatility. GARCH with exogenous variables (GARCH-X) has the potential to capture the volatility within the study series and also evaluate the influence of environmental influences [31]. In order to improve the forecast accuracy of GARCH model, the external demand influencing factors (X) needs to be incorporated in the forecasting model. If these external factors are excluded, the GARCH model may encounter biased estimates of volatility. Various researchers have already studied GARCH model with covariates; such as Brenner et al. and Patton used interest rate levels as exogeneous variables; Apergis used certain macroeconomic fundamentals as covariates to estimate stock prices; Yeasin et al. modelled domestic price index by using two external variables [31–34].

In the present investigation, we formulated our study to analyse the volatility of wholesale and retail prices of major pulses in account for the influence of lockdown. We have considered five pulses namely gram, arhar, urad, moong and lentils in five metro cities, i.e. Delhi, Mumbai, Kolkata, Hyderabad and Chennai. The primary objective of this research is to assess the impact of lockdown restrictions on prices of pulses in the Indian market. The exogenous variable may affect both the mean and variance model of GARCH collectively. To understand effect of lockdown on prices of pulses, we employed GARCH-X covariate (lockdown dummy) in both the mean and variance equations of GARCH model. The paper is organized as follows: Section 2 describes the methodology involved in this study. Section 3 deals with the results and discussion. Section 4 draws conclusion.

2. Methodology

The Box-Jenkin’s Autoregressive Integrative Moving Average (ARIMA) model gained enormous popularity in class of linear time series model. But when the series is nonlinear and the conditional variance of error term is heteroscedastic in nature, the linear ARIMA model fails to describe behavior of study variable. An Autoregressive Conditional Heteroscedastic-Lagrange Multiplier (ARCH-LM) test is used to detect the nonlinearity and conditional heteroscedasticity in ARIMA residuals [22]. The ARCH-LM test is conducted as follows:

Assuming that ε_t be the residual series, the Lagrange Multiplier (LM) test for is used to check for presence of conditional heteroscedasticity. The null hypothesis of the test is specified as H₀: a_i = 0, i = 1, 2, …, q in the following regression equation: (1) where e_t denotes the error term, q is the positive integer, and T is the number of observations.

The test statistics is (2) where SSR₀ = , is sample mean of {}, and SSR₁ = , e_t is the least square residual.

The test statistic F asymptotically follows Chi square distribution with q degrees of freedom.

The presence of volatility makes it challenging for linear ARIMA model to adequately describe the study variable. If ARCH-LM test indicates the presence of ARCH effect, an appropriate nonlinear time series model may be implemented to capture the complex behavior of data. Generalized autoregressive conditional heteroscedastic (GARCH) model is one of popular models in the class of nonlinear time series models [22]. To include the effect of exogenous variable, an improvement of GARCH model, GARCH-X model, is developed. A brief description of the GARCH and GARCH-X models along with the estimation procedures are given below:

GARCH model

GARCH model is a weighted average of past squared residuals and also includes declining weights for the residuals that never reach zero. GARCH process also models the lagged conditional variances. If y_t, y_t−1, y_t−2,…, y₂, y₁ are the observation of a volatile time series data, then GARCH (p, q) model is defined as (3) where μ>0, α_i≥0, i = 1,2,…,q for all i and β_j≥0, j = 1,2,…,p and ε is distributed i.i.d. with zero mean and unit variance. We can add η_t = ∈²_t−σ²_t to both side of the equation to express the model in the form of ARMA (mean model) model.

(4)

Mean equation (ARIMA) of the GARCH model can be represented by the following equation (5) where, φ(B) = 1−φ₁B−φ₂B²−⋯−φ_pB^p = the autoregressive operator of order p;

φ₁, φ₂,…,φ_p = the corresponding autoregressive parameters; θ(B) = 1−θ₁B−θ₂B²−⋯−θ_qB^q = the moving average operator of order q; θ₁, θ₂,…,θ_q = the associated non-seasonal moving average parameters; (1−B)^d = the differencing operator of order d to produce stationarity of the d^th differenced data. In Eq (3) B is used as a backshift operator on Y_t and is defined as Bⁱ(Y_t) = Bⁱ(Y_t−1).

GARCH-X model

In GARCH-X model, the effect of external covariates is taken into consideration. This improvement of GARCH model has the ability to capture the real situation more efficiently. The algorithms of the GARCH-X model can be summarised into four steps:

Model identification: In this step, we select the appropriate GARCH (p, q) order. Tsay (1987) stated that GARCH (1, 1) model provides the best fit than its higher order as higher order of GARCH model involved greater complication and also yield no significant enhancements in goodness of fit. So, we used GARCH (1, 1) as predecessor model of the GARCH-X formulations [35].
Inclusion of exogenous variables: This step is regarded as one of the most essential steps in GARCH-X procedure. Here, we decide how many and what kinds of exogenous variables can be included in the model. There are no particular rules for determining the suitable numbers and kind of exogenous variables. This solely depends on the objective of the study. In this study we incorporate a covariate viz. lockdown as a dummy. For lockdown period we define lockdown dummy as:
Now the GARCH-X model is developed by including the covariate in both the mean and variance equation of GARCH model.
(6) (7)
Similar to the GARCH, conditions for stationary of the GARCH-X model are μ>0, α_i≥0 for all i; β_j≥0 and The parameter γ₁ and γ₂ represent the impact of the covariates on the mean and conditional variance of series respectively.
Parameters estimation: Similar to the GARCH model, parameters of the GARCH-X model are estimated by Quasi-maximum likelihood estimation (QMLE) technique.
Diagnosis the fitness of the model: In this final step of the model, we diagnosed GARCH-X model with various selection criteria (Akaike, Bayes, Shibata and Hannan-Quinn information criteria) and residuals testing. Residual diagnostic is an important tool validate a model. The assumptions of the error in the model can be tested using residual analysis. Ljung-Box test have been performed to check adequacy of the fitted GARCH and GARCH-X model [36]. To test the structural breaks and ARCH effect in residuals, we have implemented Nyblom Stability and ARCH-LM test respectively [22,37]. All the above tests ensure that the models build up for the data under study are correctly specified and adequate.

3. Result and discussions

Data description

Daily wholesale and retail price data during the period 1^st January, 2019 to 30^th September, 2020 for five major pulses (gram, arhar, urad, moong and lentils) in five metro cities of India namely Delhi, Mumbai, Kolkata, Chennai and Hyderabad have been collected from Department of Consumer Affairs, Ministry of Consumer Affairs, Food & Public Distribution, Government of India. The study locations are displayed in Fig 1. India implemented four nationwide lockdowns consecutively, beginning from March 25, 2020 to May 31, 2020. The first lockdown was of 21 days duration (March 25–April 14); the second of 19 days (April 15–May 3); the third of 14 days (May 4–17), and the fourth of14 days (May 18–31).

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Fig 1. The study locations.

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Fig 2 presents the average wholesale and retail prices of different pulses in different markets in India during the entire lockdown period (March 25-May 31, 2020), the corresponding periods of 2019 and also the full sample period i.e. during January, 2019 to September, 2020. A perusal of Fig 2 indicates that the prices (both retail and wholesale) of all the five pulses in all the studied markets increased in 2020 during the lockdown period as compared to previous year. Also, when considering full sample of data, the wholesale and retail prices of Lentil, Moong and Gram are maximum in Mumbai, whereas Chennai experienced highest prices of Urad and Arhar.

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Fig 2. Average price and % change in price of pulses in different markets of India.

Note: % change indicates the percentage change in prices during lockdown period of 2020 compared to the same period in 2019.

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Moreover, percentage changes in average prices of pulses in all the studied markets during lockdown period i.e. March-May, 2020 with the same period in 2019 have been computed and displayed in Fig 2. Overall, Mumbai has the greatest increase in retail and wholesale prices, followed by Chennai. Delhi had the lowest price increases for both retail and wholesale, followed by Kolkata. In terms of pulses, gram is the least affected, followed by lentil while urad is the most affected, followed by moong. When we examine the change in price for wholesale and retail prices of pulses, we have seen a mixed picture. In the Hyderabad and Kolkata markets, changes in retail price are less than changes in wholesale price for all five pulses, but in Chennai, changes in retail price are more than changes in wholesale price for four out of five pulses. Mumbai and Delhi displayed a varied pattern.

The price behaviour during the studied period can also be seen in Fig 3. The red coloured vertical lines indicate the duration of lockdown. Looking at the price trend, it was evident that prices of pulses in both wholesale and retail began to rise in chosen five Indian markets following the implementation of the lockdown. In the instance of Gram, the wholesale price volatility was highest in Kolkata, whereas the retail price variation was highest in Mumbai and lowest in Hyderabad. The Kolkata market showed stable price, whereas the Chennai market fluctuated the most in both retail and wholesale Arhar prices. For Urad wholesale prices, the largest volatility was noted in the Chennai and Hyderabad markets, and the lowest in Kolkata and Delhi, but for retail prices, the highest fluctuation was recorded in Chennai and Mumbai, and Kolkata had consistent price. The wholesale and retail prices of Moong varied greatly in the Mumbai and Kolkata markets. For Lentil, wholesale prices, the largest variance was seen in the Chennai market and the lowest variation was recorded in the Delhi market, but for lentil retail prices, the highest fluctuation was observed in Mumbai and lowest in Hyderabad. It is to be noted that higher price variation has been observed in retail markets as compared to the wholesale markets for all the pulses.

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Fig 3. Pattern of wholesale and retail prices of pulses.

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Application of GARCH-X model

From the above discussion, it is clear that price fluctuations in the both markets are not only caused by usual inflation in the market, but also by the influence of the lockdown restrictions. The estimate of kernel density of the wholesale and retail prices of pulses in different markets is reported in Fig 4. A perusal of Fig 4 confirms the significant departure from normality of all the price series. Therefore, underlying error distribution is considered to be generalized error distribution as the data does not follow normality. To assess the statistical impact of the lockdown, we model the daily return price of pulses using an ARIMA-GARCH-X model with the lockdown dummy as a covariate. First, the ARIMA models have been implemented and residuals of the models were tested using ARCH-LM test. The test results evidenced the presence of ARCH effect in data sets. Hence, the GARCH-X model is more suitable to evaluate the impact of the lockdown on pulse prices. To check the model adequacy Ljung-Box, ARCH-LM and Nyblom Stability test have been implemented and test results showed that all the GARCH-X models have been correctly specified and the residuals of the models fulfilled all its assumptions. The parameter estimates of the fitted models are reported in Tables 1–5 for different pulses. The parameters μ, φ₁, θ₁, γ₁, ω, α₁, β₁, γ₂ respectively denotes the constant in mean model, AR coefficient, MA coefficient, impact of lockdown on mean price return, constant of variance equation, ARCH effect, GARCH effect and impact of lockdown on price volatility.

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Fig 4. Kernel density estimate of wholesale and retail prices of pulses in different markets.

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Table 1. Parameters estimate of GARCH-X model for gram.

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Table 2. Parameters estimate of GARCH-X model for arhar.

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Table 3. Parameters estimate of GARCH-X model for urad.

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Table 4. Parameters estimate of GARCH-X model for moong.

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Table 5. Parameters estimate of GARCH-X model for lentil.

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A perusal of the parameters estimates reported above indicate that, in the case of gram wholesale price, effect of lockdown was significant in the mean model for Mumbai, Chennai, and Hyderabad markets and in the variance model for Chennai market, whereas in case of retail price, effect of lockdown was significant in the mean model for Chennai market and in the variance model for Mumbai and Kolkata markets (Table 1). In case of arhar wholesale price, significance in lockdown was seen only for the Hyderabad market in the variance model, but in case of retail price, significance was detected only for the Hyderabad market in the mean model and the Mumbai and Delhi markets in the variance model (Table 2). In case of urad wholesale price, lockdwon was significant for all five markets in the mean model and only for the Chennai market in the variance model, whereas in retail price, lockdown was significant for the Chennai and Hyderabad markets in the mean model and for the Delhi, Mumbai, and Chennai markets in the variance model (Table 3). In case of moong, significant effect of lockdown was found for the markets of Delhi, Mumbai, Chennai, and Hyderabad in the mean model and Delhi in the variance model, while in retail price, effect of lockdown was significant for the markets of Delhi, Mumbai, Chennai, and Hyderabad in the mean model and Delhi, Mumbai, and Chennai in the variance model (Table 4). In case of lentil wholesale price, lockdown effect was significant in the mean model for Mumbai, Kolkata, Chennai, and Hyderabad markets and in the variance model for Delhi market, whereas in the case of retail price, it was significant in the mean model for Delhi, Kolkata, Chennai, and Hyderabad markets and in the variance model for Mumbai market (Table 5). The total of the Alpha and Beta parameters for all instances was quite high (near to 1), indicating that volatility was well captured. Based on Tables 1–5, we can conclude that the effect of lockdown was observed in nearly all pulse prices, although arhar has a comparatively modest effect of lockdown in comparison to other pulses.

4. Conclusion

The Indian government implemented a nationwide lockdown starting March 24, 2020, with some exceptions for agricultural operations. The lockdown has little effect on pulse production, but it has an impact on the demand and supply chain owing to poor transit. COVID-19 has revealed weaknesses and imbalances in the Indian agricultural demand-supply system. In the present investigation, the impact of lockdown on wholesale and retail prices of pulses in major Indian markets have been evaluated. It revealed that, the lockdown has a significant impact on both the wholesale and retail prices of pulses. Among the studied markets, Mumbai has seen the highest increase in retail and wholesale prices, followed by Chennai during the lockdown period. Delhi had the lowest price increases for both retail and wholesale, followed by Kolkata. In case of pulses, due to lockdown, gram is the least impacted, followed by lentil, and urad is the most affected, followed by moong. For arhar, the lockdown has a comparatively modest effect. The imposition of lockdown not only impacted the price of pulses but also its volatility significantly. Multiple national lockdowns have resulted irregular flow of raw materials and finished goods, disrupting the supply chain. The inclusion of lockdown as exogenous variable in both mean and variance equation of fitted model could effectively capture the variation in prices across the different markets. In future study, the other variants of GARCH model including machine learning techniques may be applied to model the volatility in agricultural commodity prices.

Acknowledgments

Authors are grateful to the anonymous reviewers for providing suggestions which helped in improving the quality of the manuscript.

References

1. Ramakumar R. Agriculture and the Covid-19 Pandemic: An Analysis with special reference to India. Review of Agrarian Studies. 2020;10.
- View Article
- Google Scholar
2. Arora N, Robinson K, Charm T, Ortega M, Staack Y, Whitehead S, et al. Consumer sentiment and behavior continue to reflect the uncertainty of the COVID-19 crisis. McKinsey & Company, July 2020.
3. Cariappa AGA, Acharya KK, Adhav CA, Sendhil R, Ramasundaram P. Impact of COVID-19 on the Indian agricultural system: A 10-point strategy for post-pandemic recovery. Outlook on Agriculture. 2021;50: 26–33.
- View Article
- Google Scholar
4. Harris J, Depenbusch L, Pal AA, Nair RM, Ramasamy S. Food system disruption: initial livelihood and dietary effects of COVID-19 on vegetable producers in India. Food Security. 2020;12: 841–851. pmid:32837650
- View Article
- PubMed/NCBI
- Google Scholar
5. Bairagi S, Mishra AK, Mottaleb KA. Impacts of the COVID-19 pandemic on food prices: Evidence from storable and perishable commodities in India. PLoS One. 2022;17: e0264355. pmid:35239679
- View Article
- PubMed/NCBI
- Google Scholar
6. Ruan J, Cai Q, Jin S. Impact of COVID-19 and Nationwide Lockdowns on Vegetable Prices: Evidence from Wholesale Markets in China. American Journal of Agricultural Economics. 2021;103: 1574–1594. pmid:33821009
- View Article
- PubMed/NCBI
- Google Scholar
7. Akter S. The impact of COVID-19 related ‘stay-at-home’ restrictions on food prices in Europe: findings from a preliminary analysis. Food Security. 2020;12: 719–725. pmid:32837638
- View Article
- PubMed/NCBI
- Google Scholar
8. Hobbs JE. Food supply chains during the COVID-19 pandemic. Canadian Journal of Agricultural Economics/Revue canadienne d’agroeconomie. 2020;68: 171–176.
- View Article
- Google Scholar
9. Yu X, Liu C, Wang H, Feil JH. The impact of COVID-19 on food prices in China: evidence of four major food products from Beijing, Shandong and Hubei Provinces. China Agricultural Economic Review. 2020;12: 445–458.
- View Article
- Google Scholar
10. Laborde D, Martin W, Vos R. Impacts of COVID-19 on global poverty, food security, and diets: Insights from global model scenario analysis. Agric Econ. 2021;52: 375–390. pmid:34230728
- View Article
- PubMed/NCBI
- Google Scholar
11. Uddin J, Saifun A|, Kausar A|, Majumder A. Impact of COVID-19 on agricultural production and distribution in South Asia. World Food Policy. 2021;7: 168–182.
- View Article
- Google Scholar
12. Vyas S, Chanana N, Chanana M, Aggarwal PK. From Farm to Fork: Early Impacts of COVID-19 on Food Supply Chain. Frontiers in Sustainable Food Systems. 2021;5: 424.
- View Article
- Google Scholar
13. Kumari S, Venkatesh VG, Deakins E, Mani V, Kamble S. Agriculture value chain sustainability during COVID-19: an emerging economy perspective. International Journal of Logistics Management. 2021;
- View Article
- Google Scholar
14. Alina DL, Daniela M, Iulia O, Maria G, Nicolae S. The Impact of Covid-19 on Food Prices in Romania. Springer Proceedings in Business and Economics. 2022; 121–136.
- View Article
- Google Scholar
15. Janssen M, Chang BPI, Hristov H, Pravst I, Profeta A, Millard J. Changes in Food Consumption During the COVID-19 Pandemic: Analysis of Consumer Survey Data From the First Lockdown Period in Denmark, Germany, and Slovenia. Front Nutr. 2021;8: 635859. pmid:33763443
- View Article
- PubMed/NCBI
- Google Scholar
16. Paul RK, Birthal PS. The prices of perishable food commodities in India: the impact of the lockdown. Agricultural Economics Research Review. 2021;34: 151–164.
- View Article
- Google Scholar
17. India at a glance | FAO in India | Food and Agriculture Organization of the United Nations. [cited 10 Jan 2022]. Available: https://www.fao.org/india/fao-in-india/india-at-a-glance/en/.
18. Govt. of India D of PD. Pulses: Status and Contribution to Food Security.
19. Venkidasamy B, Selvaraj D, Nile AS, Ramalingam S, Kai G, Nile SH. Indian pulses: A review on nutritional, functional and biochemical properties with future perspectives. Trends in Food Science & Technology. 2019;88: 228–242.
- View Article
- Google Scholar
20. Reardon T, Mishra A, Nuthalapati CSR, Bellemare MF, Zilberman D. COVID-19’s disruption of India’s transformed food supply chains. Economic and Political Weekly. 2020;55: 18–22.
- View Article
- Google Scholar
21. Verma P, Praharaj CS, Kanaujia VK. Wholesale price variability of raw and processed pulses-A case study. Legume Research-An International Journal. 2013;36: 429–435.
- View Article
- Google Scholar
22. Engle RF. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society. 1982; 987–1007.
- View Article
- Google Scholar
23. Bollerslev T. Generalized autoregressive conditional heteroskedasticity. J Econom. 1986;31: 307–327.
- View Article
- Google Scholar
24. Engle R. GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of economic perspectives. 2001;15: 157–168.
- View Article
- Google Scholar
25. Paul RK, Saxena R, Bhat SA. How price signals in pulses are transmitted across regions and value chain? Examining horizontal and vertical market price integration for major pulses in India. 2016; 29 (Conference Number): 75–86.
- View Article
- Google Scholar
26. Paul RK, Himadri G. GARCH nonlinear time series analysis for modelling and forecasting of India’s volatile spices export data. Journal of the Indian Society of Agricultural Statistics. 2009;63: 123–131.
- View Article
- Google Scholar
27. Ghosh H, Paul RK. Nonlinear Time Series Modeling and Forecasting for Periodic and ARCH Effects. Journal of Statistical Theory and Practice. 2010;4: 27–44.
- View Article
- Google Scholar
28. Paul RK, Prajneshu, Himadri G. Development of out-of-sample forecasts formulae for ARIMAX-GARCH model and their application. Journal of the Indian Society of Agricultural Statistics. 2014;68: 85–92.
- View Article
- Google Scholar
29. Paul RK. ARIMAX-GARCH-WAVELET model for forecasting volatile data. Model Assisted Statistics and Applications. 2015;10: 243–252.
- View Article
- Google Scholar
30. Rakshit D, Kumar Paul R, Panwar S. Asymmetric Price Volatility of Onion in India. Indian Journal of Agricultural Economics. 76(2), 245–260.
- View Article
- Google Scholar
31. Yeasin M, Narain Singh K, Lama A, Kumar Paul R. Modelling Volatility Influenced by Exogenous Factors using an Improved GARCH-X Model. Journal of the Indian Society of Agricultural Statistics. 2020;74: 209–216. Available: https://www.researchgate.net/publication/348163301.
- View Article
- Google Scholar
32. Brenner RJ, Harjes RH, Kroner KF. Another look at models of the short-term interest rate. Journal of financial and quantitative analysis. 1996;31: 85–107.
- View Article
- Google Scholar
33. Apergis N, Rezitis A. Food price volatility and macroeconomic factors: Evidence from GARCH and GARCH-X estimates. Journal of Agricultural and Applied Economics. 2011;43: 95–110.
- View Article
- Google Scholar
34. Patton E. What good is a volatility model. Quantitative Finance. 2001;1: 237–245.
- View Article
- Google Scholar
35. Tsay RS. Conditional heteroscedastic time series models. Journal of the American Statistical association. 1987;82: 590–604.
- View Article
- Google Scholar
36. Ljung GM, Box GEP. On a measure of lack of fit in time series models. Biometrika. 1978;65: 297–303.
- View Article
- Google Scholar
37. Nyblom J. Testing for the constancy of parameters over time. J Am Stat Assoc. 1989;84: 223–230.
- View Article
- Google Scholar

[ref1] 1. Ramakumar R. Agriculture and the Covid-19 Pandemic: An Analysis with special reference to India. Review of Agrarian Studies. 2020;10.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Arora N, Robinson K, Charm T, Ortega M, Staack Y, Whitehead S, et al. Consumer sentiment and behavior continue to reflect the uncertainty of the COVID-19 crisis. McKinsey & Company, July 2020.

[ref3] 3. Cariappa AGA, Acharya KK, Adhav CA, Sendhil R, Ramasundaram P. Impact of COVID-19 on the Indian agricultural system: A 10-point strategy for post-pandemic recovery. Outlook on Agriculture. 2021;50: 26–33.
View Article
Google Scholar

[6] View Article

[7] Google Scholar

[ref4] 4. Harris J, Depenbusch L, Pal AA, Nair RM, Ramasamy S. Food system disruption: initial livelihood and dietary effects of COVID-19 on vegetable producers in India. Food Security. 2020;12: 841–851. pmid:32837650
View Article
PubMed/NCBI
Google Scholar

[9] View Article

[10] PubMed/NCBI

[11] Google Scholar

[ref5] 5. Bairagi S, Mishra AK, Mottaleb KA. Impacts of the COVID-19 pandemic on food prices: Evidence from storable and perishable commodities in India. PLoS One. 2022;17: e0264355. pmid:35239679
View Article
PubMed/NCBI
Google Scholar

[13] View Article

[14] PubMed/NCBI

[15] Google Scholar

[ref6] 6. Ruan J, Cai Q, Jin S. Impact of COVID-19 and Nationwide Lockdowns on Vegetable Prices: Evidence from Wholesale Markets in China. American Journal of Agricultural Economics. 2021;103: 1574–1594. pmid:33821009
View Article
PubMed/NCBI
Google Scholar

[17] View Article

[18] PubMed/NCBI

[19] Google Scholar

[ref7] 7. Akter S. The impact of COVID-19 related ‘stay-at-home’ restrictions on food prices in Europe: findings from a preliminary analysis. Food Security. 2020;12: 719–725. pmid:32837638
View Article
PubMed/NCBI
Google Scholar

[21] View Article

[22] PubMed/NCBI

[23] Google Scholar

[ref8] 8. Hobbs JE. Food supply chains during the COVID-19 pandemic. Canadian Journal of Agricultural Economics/Revue canadienne d’agroeconomie. 2020;68: 171–176.
View Article
Google Scholar

[25] View Article

[26] Google Scholar

[ref9] 9. Yu X, Liu C, Wang H, Feil JH. The impact of COVID-19 on food prices in China: evidence of four major food products from Beijing, Shandong and Hubei Provinces. China Agricultural Economic Review. 2020;12: 445–458.
View Article
Google Scholar

[28] View Article

[29] Google Scholar

[ref10] 10. Laborde D, Martin W, Vos R. Impacts of COVID-19 on global poverty, food security, and diets: Insights from global model scenario analysis. Agric Econ. 2021;52: 375–390. pmid:34230728
View Article
PubMed/NCBI
Google Scholar

[31] View Article

[32] PubMed/NCBI

[33] Google Scholar

[ref11] 11. Uddin J, Saifun A|, Kausar A|, Majumder A. Impact of COVID-19 on agricultural production and distribution in South Asia. World Food Policy. 2021;7: 168–182.
View Article
Google Scholar

[35] View Article

[36] Google Scholar

[ref12] 12. Vyas S, Chanana N, Chanana M, Aggarwal PK. From Farm to Fork: Early Impacts of COVID-19 on Food Supply Chain. Frontiers in Sustainable Food Systems. 2021;5: 424.
View Article
Google Scholar

[38] View Article

[39] Google Scholar

[ref13] 13. Kumari S, Venkatesh VG, Deakins E, Mani V, Kamble S. Agriculture value chain sustainability during COVID-19: an emerging economy perspective. International Journal of Logistics Management. 2021;
View Article
Google Scholar

[41] View Article

[42] Google Scholar

[ref14] 14. Alina DL, Daniela M, Iulia O, Maria G, Nicolae S. The Impact of Covid-19 on Food Prices in Romania. Springer Proceedings in Business and Economics. 2022; 121–136.
View Article
Google Scholar

[44] View Article

[45] Google Scholar

[ref15] 15. Janssen M, Chang BPI, Hristov H, Pravst I, Profeta A, Millard J. Changes in Food Consumption During the COVID-19 Pandemic: Analysis of Consumer Survey Data From the First Lockdown Period in Denmark, Germany, and Slovenia. Front Nutr. 2021;8: 635859. pmid:33763443
View Article
PubMed/NCBI
Google Scholar

[47] View Article

[48] PubMed/NCBI

[49] Google Scholar

[ref16] 16. Paul RK, Birthal PS. The prices of perishable food commodities in India: the impact of the lockdown. Agricultural Economics Research Review. 2021;34: 151–164.
View Article
Google Scholar

[51] View Article

[52] Google Scholar

[ref17] 17. India at a glance | FAO in India | Food and Agriculture Organization of the United Nations. [cited 10 Jan 2022]. Available: https://www.fao.org/india/fao-in-india/india-at-a-glance/en/.

[ref18] 18. Govt. of India D of PD. Pulses: Status and Contribution to Food Security.

[ref19] 19. Venkidasamy B, Selvaraj D, Nile AS, Ramalingam S, Kai G, Nile SH. Indian pulses: A review on nutritional, functional and biochemical properties with future perspectives. Trends in Food Science & Technology. 2019;88: 228–242.
View Article
Google Scholar

[56] View Article

[57] Google Scholar

[ref20] 20. Reardon T, Mishra A, Nuthalapati CSR, Bellemare MF, Zilberman D. COVID-19’s disruption of India’s transformed food supply chains. Economic and Political Weekly. 2020;55: 18–22.
View Article
Google Scholar

[59] View Article

[60] Google Scholar

[ref21] 21. Verma P, Praharaj CS, Kanaujia VK. Wholesale price variability of raw and processed pulses-A case study. Legume Research-An International Journal. 2013;36: 429–435.
View Article
Google Scholar

[62] View Article

[63] Google Scholar

[ref22] 22. Engle RF. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society. 1982; 987–1007.
View Article
Google Scholar

[65] View Article

[66] Google Scholar

[ref23] 23. Bollerslev T. Generalized autoregressive conditional heteroskedasticity. J Econom. 1986;31: 307–327.
View Article
Google Scholar

[68] View Article

[69] Google Scholar

[ref24] 24. Engle R. GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of economic perspectives. 2001;15: 157–168.
View Article
Google Scholar

[71] View Article

[72] Google Scholar

[ref25] 25. Paul RK, Saxena R, Bhat SA. How price signals in pulses are transmitted across regions and value chain? Examining horizontal and vertical market price integration for major pulses in India. 2016; 29 (Conference Number): 75–86.
View Article
Google Scholar

[74] View Article

[75] Google Scholar

[ref26] 26. Paul RK, Himadri G. GARCH nonlinear time series analysis for modelling and forecasting of India’s volatile spices export data. Journal of the Indian Society of Agricultural Statistics. 2009;63: 123–131.
View Article
Google Scholar

[77] View Article

[78] Google Scholar

[ref27] 27. Ghosh H, Paul RK. Nonlinear Time Series Modeling and Forecasting for Periodic and ARCH Effects. Journal of Statistical Theory and Practice. 2010;4: 27–44.
View Article
Google Scholar

[80] View Article

[81] Google Scholar

[ref28] 28. Paul RK, Prajneshu, Himadri G. Development of out-of-sample forecasts formulae for ARIMAX-GARCH model and their application. Journal of the Indian Society of Agricultural Statistics. 2014;68: 85–92.
View Article
Google Scholar

[83] View Article

[84] Google Scholar

[ref29] 29. Paul RK. ARIMAX-GARCH-WAVELET model for forecasting volatile data. Model Assisted Statistics and Applications. 2015;10: 243–252.
View Article
Google Scholar

[86] View Article

[87] Google Scholar

[ref30] 30. Rakshit D, Kumar Paul R, Panwar S. Asymmetric Price Volatility of Onion in India. Indian Journal of Agricultural Economics. 76(2), 245–260.
View Article
Google Scholar

[89] View Article

[90] Google Scholar

[ref31] 31. Yeasin M, Narain Singh K, Lama A, Kumar Paul R. Modelling Volatility Influenced by Exogenous Factors using an Improved GARCH-X Model. Journal of the Indian Society of Agricultural Statistics. 2020;74: 209–216. Available: https://www.researchgate.net/publication/348163301.
View Article
Google Scholar

[92] View Article

[93] Google Scholar

[ref32] 32. Brenner RJ, Harjes RH, Kroner KF. Another look at models of the short-term interest rate. Journal of financial and quantitative analysis. 1996;31: 85–107.
View Article
Google Scholar

[95] View Article

[96] Google Scholar

[ref33] 33. Apergis N, Rezitis A. Food price volatility and macroeconomic factors: Evidence from GARCH and GARCH-X estimates. Journal of Agricultural and Applied Economics. 2011;43: 95–110.
View Article
Google Scholar

[98] View Article

[99] Google Scholar

[ref34] 34. Patton E. What good is a volatility model. Quantitative Finance. 2001;1: 237–245.
View Article
Google Scholar

[101] View Article

[102] Google Scholar

[ref35] 35. Tsay RS. Conditional heteroscedastic time series models. Journal of the American Statistical association. 1987;82: 590–604.
View Article
Google Scholar

[104] View Article

[105] Google Scholar

[ref36] 36. Ljung GM, Box GEP. On a measure of lack of fit in time series models. Biometrika. 1978;65: 297–303.
View Article
Google Scholar

[107] View Article

[108] Google Scholar

[ref37] 37. Nyblom J. Testing for the constancy of parameters over time. J Am Stat Assoc. 1989;84: 223–230.
View Article
Google Scholar

[110] View Article

[111] Google Scholar

Figures

Abstract

1. Introduction

2. Methodology

GARCH model

GARCH-X model

3. Result and discussions

Data description

Application of GARCH-X model

4. Conclusion

Acknowledgments

References