Examining Export Volatility, Structural Breaks in Price Volatility and Linkages between Domestic and Export Prices of Onion in India

Paul, Ranjit Kumar; Saxena, Raka; Chaurasia, Shikha; Zeeshan,; Rana, Simmi

doi:10.5958/0974-0279.2015.00026.9

Cited by 14 publications

(7 citation statements)

References 18 publications

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“…However, on several occasions, especially post-2008 global food crisis, the domestic prices have been higher than export prices (Figure 9(a)). Paul et al (2015) also report similar evidence.…”

Section: Exports and Export Policiessupporting

confidence: 59%

Understanding causes of volatility in onion prices in India

Birthal¹,

Negi²,

Joshi³

2019

JADEE

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Purpose Post-2008 global food crisis the prices of perishable high-value food commodities, such as vegetables and fruits, in India have risen faster and become more volatile compared to that of cereals. The welfare consequences of price shocks though are well understood yet the policy responses to manage these remain blurred because of a lack of clarity on their causes. Focusing on onions that comprise an important constituent of the Indian diet, the purpose of this paper is to explore causes of high price volatility. Design/methodology/approach Using high-frequency time series data on wholesale prices and arrivals of onions in major markets and other relevant variables, this paper analyzes causes of price volatility from several angles, that is production shocks, seasonality in production and market arrivals, internal trade, export policies and market power of intermediaries on the supply chain. Findings Despite markets being integrated and no significant climatic shocks to production there exists a strong element of uncertainty in market arrivals of onions, pointing toward the market power immediate downstream the production or alternatively anti-competition trade practices in major markets as a cause of high price volatility. The measures to manage price volatility, such as an increase in minimum export prices and bans on exports, are also not found to have an immediate cooling effect on prices. Research limitations/implications The agricultural policy should provide for a system of market intelligence to monitor anti-competitive trade practices along the supply chain, and to take proactive trade control measures to prevent frequent ups and downs in domestic prices. In addition, it should provide for incentives for developing efficient supply chains and for the cultivation of onions in the regions that have agronomic potential but it has remained underexploited due to one or the other constraint. Social implications Excessive volatility in food prices impacts farmers, consumers, processors, and traders and even political system. It may distort production and investment decisions of farmers and intermediaries on the value chains, leading to inefficient allocation of resources. The poor consumers may be forced to reduce food and non-food productive expenditures. If persists for a longer period, it may lead to political instability too. Originality/value Several studies have analyzed volatility in food prices and causes thereof. However, rarely any of these has examined volatility in prices of perishable high-value food commodities. This paper is an attempt toward filling this gap.

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Section: Exports and Export Policiessupporting

confidence: 59%

Understanding causes of volatility in onion prices in India

Birthal¹,

Negi²,

Joshi³

2019

JADEE

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“…Ajjan et al (2013) studies the market integration of the peas market in India and concluded that market has a price relationship and therefore an integrated one. Paul et al (2015) studies export volatility, structural price and the relationship betwee domestic prices and export of red onion in India, in which, co-integrated models and Granger causality tests are used. The results show that prices in all market shares are stable in the long-term.…”

Section: Literature Reviewmentioning

confidence: 99%

Market Integration of Agricultural Products

Marwa¹,

Bashir²,

Azwardi³

et al. 2017

IJEBA

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The study aims to investigate the relationship between the prices of agricultural products at the producer level with the prices of the same products at the consumer level in South Sumatera, Indonesia with a case study on the production and consumption of rice as the most important agricultural product in Indonesia. The analytical approach is linear regression modelling with Ordinary Least Squares (OLS), and the Approach of Index Market Connection model (IMC). The findings of the study indicated integration between the price of rice at the producer level and the price of rice at the consumer level. In addition, the three sample regions have IMC value less than 1, meaning that both markets have vertical integration in the short run.

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“…Some studies have used WT, co-integration tests, and Granger causality tests to identify the existence of long-term relationships [24]. Paul et al [25] examined export volatility, and identified short-and long-term relationships between onion export and domestic prices using co-integration analysis and Granger Causality. Volatility estimating is especially crucial for organizations that trade in stocks and manage portfolios [26].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

Alshammari¹,

Ismail²,

Hamadneh³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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In this study, we proposed a new model to improve the accuracy of forecasting the stock market volatility pattern. The hypothesized model was validated empirically using a data set collected from the Saudi Arabia stock Exchange (Tadawul). The data is the daily closed price index data from August 2011 to December 2019 with 2027 observations. The proposed forecasting model combines the best maximum overlapping discrete wavelet transform (MODWT) function (Bl14) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model. The results show the model's ability to analyze stock market data, highlight important events that contain the most volatile data, and improve forecast accuracy. The results were compared from a number of mathematical models, which are the non-linear spectral model, autoregressive integrated moving average (ARIMA) model and EGARCH model. The performance of the forecasting model will be evaluated based on some of error functions such as Mean absolute percentage error (MAPE), Mean absolute scaled error (MASE) and Root means squared error (RMSE).

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Examining Export Volatility, Structural Breaks in Price Volatility and Linkages between Domestic and Export Prices of Onion in India

Cited by 14 publications

References 18 publications

Understanding causes of volatility in onion prices in India

Understanding causes of volatility in onion prices in India

Market Integration of Agricultural Products

Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

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