Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Analysis of main determinants of soybean price volatility in Malawi
Dissertation (MSc (Agric))--University of Pretoria, 2018.
Saved in:
| Other Authors: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
University of Pretoria
2019
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613464642453504 |
|---|---|
| access_status_str | Open Access |
| author2 | Meyer, Ferdinand |
| author_browse | Meyer, Ferdinand |
| author_facet | Meyer, Ferdinand |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc (Agric))--University of Pretoria, 2018. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/70551 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:34.044Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/70551 Analysis of main determinants of soybean price volatility in Malawi Meyer, Ferdinand CHIMALIRO.AUBREY@YAHOO.COM Davids, Patricia (Tracey) Chimaliro, Aubrey Victor UCTD Soybean Agricultural economics Market integration Price co-integration Error correction model (ECM) Agricultural policy Price shocks Value chain Food security Export crops Malawian agriculture Southern Africa markets Exchange rates Consumer price index Price volatility Agricultural trade Economic adjustment Natural and agricultural sciences theses SDG-01 SDG-01: No poverty Natural and agricultural sciences theses SDG-02 SDG-02: Zero hunger Natural and agricultural sciences theses SDG-08 SDG-08: Decent work and economic growth Natural and agricultural sciences theses SDG-09 SDG-09: Industry, innovation and infrastructure Natural and agricultural sciences theses SDG-12 SDG-12: Responsible consumption and production Dissertation (MSc (Agric))--University of Pretoria, 2018. The study primarily focuses on analysing the extent of soybean price volatility in Malawi. The interest in the study was triggered by the findings from literature noting soybean prices in Malawi as being particularly volatile. Soybean is one of the most important oilseed crops in Malawi and has the potential to become a major export crop. It offers good export prospects to neighbouring countries in Southern Africa, but has also been prioritised for its potential domestic contribution in Malawi. It is regarded as one of the value chains that promotes better nutrition in Malawi since most diets are dominated by maize, which contributes to malnutrition in Malawi. The study empirically estimates soybean price volatility using a GARCH (1,1) model and results indicate that both the lagged squared residual and the lagged conditional variance have an effect on the conditional variance of soybean prices. GARCH terms are significant, indicating some volatility clustering in monthly returns of soybeans in Malawi, South Africa and the world. The study confirms that soybean prices in Malawi have been more volatile relative to South Africa and the USA. To evaluate the extent to which domestic soybean price volatility can be attributed to regional and global market volatility, the Engle-Granger procedure was employed to estimate long-run co-integration between soybean prices in Malawi, South Africa and the world. The prices are categorised into six pairs and testing the long-run co-integration between these pairs involve both directions. Five out of the six pairs of prices exhibit long-run co-integrating relationships. An error correction model (ECM) is also employed to estimate the speed of adjustment to the equilibrium for five co-integrated price series. South Africa is the fastest in responding to the USA price changes, taking two months. Malawi is the second fastest since it takes about four months for soybean prices to respond to shocks in South African markets. However, it takes about seven months for the USA soybean prices to respond to price shocks in the South African markets which is longer than the period that Malawi takes to respond. South Africa takes nine months to respond to the shocks that occur in the Malawian markets. USA is the lowest in terms of the speed of adjustment since it takes about sixteen months to respond to the Malawian market shocks. Therefore, this study agrees with expectation that changes in the international markets affect the domestic markets. This is so because South Africa is a small nation in the international soybean market and Malawi is even much smaller – the volumes traded in these markets are considered too small to have any meaningful impact on world market prices. Lastly, to evaluate the influence of shocks on the South African prices, as well as selected macro-economic variables in Malawi on soybean price volatility in Malawi, the study employs a vector error correction model (VECM) to evaluate the long- and short-run relationship between soybean prices and explanatory variables (South Africa soybean prices, exchange rates and consumer price index). The error correction coefficient of -0.2089 is negative and highly significant which is in line with expectations. The Johansen test points to the possibility of 1 or 2 cointegrated relationships between variables. The Wald test results show that the probability values of the joint F-statistics for South African soybean prices and Malawi exchange rate are significant at 10% and 5% levels respectively. Based on the significance of both the error correction term and the joint F-statistics of the two lagged variables in the Wald test results, the study concludes that South Africa soybean prices and Malawi exchange rate are significant drivers of volatility in Malawi soybean prices. Agricultural Economics, Extension and Rural Development MSc (Agric) Unrestricted 2019-07-08T09:46:50Z 2019-07-08T09:46:50Z 2019/04/17 2018 Dissertation Chimaliro, AV 2018, Analysis of main determinants of soybean price volatility in Malawi, MSc (Agric) Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/70551> A2019 http://hdl.handle.net/2263/70551 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Soybean Agricultural economics Market integration Price co-integration Error correction model (ECM) Agricultural policy Price shocks Value chain Food security Export crops Malawian agriculture Southern Africa markets Exchange rates Consumer price index Price volatility Agricultural trade Economic adjustment Natural and agricultural sciences theses SDG-01 SDG-01: No poverty Natural and agricultural sciences theses SDG-02 SDG-02: Zero hunger Natural and agricultural sciences theses SDG-08 SDG-08: Decent work and economic growth Natural and agricultural sciences theses SDG-09 SDG-09: Industry, innovation and infrastructure Natural and agricultural sciences theses SDG-12 SDG-12: Responsible consumption and production Analysis of main determinants of soybean price volatility in Malawi |
| title | Analysis of main determinants of soybean price volatility in Malawi |
| title_full | Analysis of main determinants of soybean price volatility in Malawi |
| title_fullStr | Analysis of main determinants of soybean price volatility in Malawi |
| title_full_unstemmed | Analysis of main determinants of soybean price volatility in Malawi |
| title_short | Analysis of main determinants of soybean price volatility in Malawi |
| title_sort | analysis of main determinants of soybean price volatility in malawi |
| topic | UCTD Soybean Agricultural economics Market integration Price co-integration Error correction model (ECM) Agricultural policy Price shocks Value chain Food security Export crops Malawian agriculture Southern Africa markets Exchange rates Consumer price index Price volatility Agricultural trade Economic adjustment Natural and agricultural sciences theses SDG-01 SDG-01: No poverty Natural and agricultural sciences theses SDG-02 SDG-02: Zero hunger Natural and agricultural sciences theses SDG-08 SDG-08: Decent work and economic growth Natural and agricultural sciences theses SDG-09 SDG-09: Industry, innovation and infrastructure Natural and agricultural sciences theses SDG-12 SDG-12: Responsible consumption and production |
| url | http://hdl.handle.net/2263/70551 |