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<p>In this article the dynamic connectedness between the five agricultural commodities is examined by implementing the Diebold and Yılmaz (VAR based) and Time--Varying Parameter Vector Autoregressions (TVP-VAR) measures for understanding the time-varying variance-covariance mechanism using daily data for the period of 2005 to 2019. The findings reveal that at an overall level all the commodity prices are less susceptible to significant volatility shocks from other commodities specifically before the introduction of the pan-India electronic trading portal (eNAM). Cotton prices do not show any variation due to spillover from others for the entire study period. The volatility spillover is visible post eNAM period particularly for the commodity stock prices. Whereas at an overall level the total directional connectedness has gone down in the post eNAM era. The network analysis suggests that the commodity stock prices show a stronger association as compared to market prices. Generally commodity prices show volatility connectedness but with respect to their own market which means strong spillover is missing among both the markets.</p>
</abstract>

In this article the dynamic connectedness between the five agricultural commodities is examined by implementing the Diebold and Yılmaz (VAR based) and Time--Varying Parameter Vector Autoregressions (TVP-VAR) measures for understanding the time-varying variance-covariance mechanism using daily data for the period of 2005 to 2019. The findings reveal that at an overall level all the commodity prices are less susceptible to significant volatility shocks from other commodities specifically before the introduction of the pan-India electronic trading portal (eNAM). Cotton prices do not show any variation due to spillover from others for the entire study period. The volatility spillover is visible post eNAM period particularly for the commodity stock prices. Whereas at an overall level the total directional connectedness has gone down in the post eNAM era. The network analysis suggests that the commodity stock prices show a stronger association as compared to market prices. Generally commodity prices show volatility connectedness but with respect to their own market which means strong spillover is missing among both the markets.

Agricultural commodities: An integrated approach to assess the volatility spillover and dynamic connectedness

Department of Food Business Management and Entrepreneurship

Economics and Business Review

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this article the dynamic connectedness between the five agricultural commodities is examined by implementing the Diebold and Yılmaz (VAR based) and...

Agricultural commodities: An integrated approach to assess the volatility spillover and dynamic connectedness

Publié en ligne: 30 déc. 2021

Pages: 28 - 53

Reçu: 24 juil. 2021

Accepté: 25 oct. 2021

DOI: https://doi.org/10.18559/ebr.2021.4.3

Mots clés
dynamic connectedness, TVP-VAR, price volatility, volatility spillover, agricultural commodities, network diagrams

© 2020 Arunendra Mishra et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Agricultural commodities: An integrated approach to assess the volatility spillover and dynamic connectedness

Publié en ligne: 30 déc. 2021

Pages: 28 - 53

Reçu: 24 juil. 2021

Accepté: 25 oct. 2021

DOI: https://doi.org/10.18559/ebr.2021.4.3

Mots clésdynamic connectedness, TVP-VAR, price volatility, volatility spillover, agricultural commodities, network diagrams

© 2020 Arunendra Mishra et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
dynamic connectedness, TVP-VAR, price volatility, volatility spillover, agricultural commodities, network diagrams