Abstract
The EU dairy industry faces an unprecedented level of change. The anticipated removal of milk quotas and the move to a less restricted global trade environment will provide the industry with both opportunities and challenges. The primary challenge will be the need for the industry to deal with more volatile prices. Active management of the risks associated with these more volatile prices will help to place the industry in a more competitive position. By quantifying the increases in EU butter and Skim Milk Powder (SMP) price volatility, this chapter demonstrates one of the consequences of the more recent reforms of EU dairy policy. Comparison with comparable world prices also provides an indication of how this volatility might evolve. This analysis employs a number of techniques to quantify the increased volatility from the simple and intuitive to more complex time series models (GARCH). In all cases increased volatility in EU dairy commodity prices is clearly evident suggesting that the challenges associated with high levels of price volatility need to be addressed as a priority.
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Notes
- 1.
The choice of these commodities may be explained by the fact that these joint products provide a means of long-term storage for milk fat and milk protein, the two most valuable components of raw milk. It should also be noted that casein, whole milk powder, liquid milk and certain varieties of cheese have to a lesser degree also been regulated by the CAP.
- 2.
Intervention buying of products by government agencies is generally referred to as “intervention”. The use of this term can confuse as it refers to only one form of government intervention. Henceforth, intervention will refer specifically to intervention buying, while government intervention in the market will be referred to as policy intervention. The intervention system when available places an effective floor price for the market and thus eliminates the more extreme negative price fluctuations.
- 3.
For Italy, the 5% increase will be introduced immediately in 2009–2010.
- 4.
The USDA publishes a monthly high and low quotation and the series considered in this analysis is the mid point of these quotations.
- 5.
The butter series are reported in “Milk Product” while the SMP series are reported in “Preserved Milk” (Agra Europe).
- 6.
It may be noted that the target price for milk under the “old” CAP was approximately 10% higher than the intervention milk price equivalent.
- 7.
A similar pattern is observed for SMP.
- 8.
Note the scale is identical in all panels of this chart thus highlighting the greater volatility in the world prices.
- 9.
These and all subsequent estimations were undertaken using PcGive software.
- 10.
Furthermore, the α i and γ i must be non-negative.
- 11.
Note that in the following analysis data from February 1990 to February 2009 is considered.
- 12.
These results are available from the authors on request.
- 13.
Note the scale of the graph in this appendix is different in each instance.
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Appendices
Appendix 1: Price Series Growth Rates
Appendix 2: GARCH Specifications and Volatility Charts
Modelling WSMP by restricted GARCH(0,1)
The estimation sample is: 1990 (7) to 2009 (2)
| Â | Coefficient | Std. Error | Robust SE | t-Value | t-Prob |
|---|---|---|---|---|---|
WSMP_1 | Y 0.306072 | 0.06608 | 0.1077 | 2.84 | 0.005 |
WSMP_3 | Y 0.208133 | 0.05396 | 0.07222 | 2.88 | 0.004 |
WSMP_5 | Y –0.109334 | 0.05414 | 0.04732 | –2.31 | 0.022 |
alpha_0 | H 0.00147904 | 0.0001985 | 0.0002729 | 5.42 | 0.000 |
alpha_1 | H 0.422738 | 0.1245 | 0.1249 | 3.39 | 0.001 |
Modelling WBUT by restricted GARCH(0,1)
The estimation sample is: 1990 (7) to 2009 (2)
| Â | Coefficient | Std. Error | Robust SE | t-Value | t-Prob |
|---|---|---|---|---|---|
WBUT_1 | Y 0.322282 | 0.07283 | 0.09257 | 3.48 | 0.001 |
WBUT_3 | Y 0.0860846 | 0.05478 | 0.04244 | 2.03 | 0.044 |
WBUT_5 | Y –0.197504 | 0.05109 | 0.09525 | –2.07 | 0.039 |
alpha_0 | H 0.00179056 | 0.0002351 | 0.0003257 | 5.50 | 0.000 |
alpha_1 | H 0.422387 | 0.1381 | 0.2064 | 2.05 | 0.042 |
Modelling EUSMP by restricted GARCH(1,1)
The estimation sample is: 1990 (7) to 2009 (2)
| Â | Coefficient | Std. Error | Robust SE | t-Value | t-Prob |
|---|---|---|---|---|---|
EUSMP_1 | Y 0.573138 | 0.07903 | 0.08233 | 6.96 | 0.000 |
EUSMP_2 | Y –0.223628 | 0.07303 | 0.08499 | –2.63 | 0.009 |
EUSMP_5 | Y –0.151037 | 0.05120 | 0.06055 | –2.49 | 0.013 |
alpha_0 | H 7.29770e–005 | 2.624e–005 | 3.502e–005 | 2.08 | 0.038 |
alpha_1 | H 0.454464 | 0.1228 | 0.1782 | 2.55 | 0.011 |
beta_1 | H 0.495433 | 0.09554 | 0.1323 | 3.75 | 0.000 |
Modelling EUBUT by restricted GARCH(1,1)
The estimation sample is: 1990 (7) to 2009 (2)
| Â | Coefficient | Std. Error | r Robust SE | t-Value | t-Prob |
|---|---|---|---|---|---|
EUBUT_1 | Y 0.767340 | 0.07746 | 0.06576 | 11.7 | 0.000 |
EUBUT_2 | Y –0.230737 | 0.07680 | 0.06758 | –3.41 | 0.001 |
alpha_0 | H 1.84080e–005 | 6.902e–006 | 7.288e–006 | 2.53 | 0.012 |
alpha_1 | H 0.409981 | 0.1035 | 0.1853 | 2.21 | 0.028 |
beta_1 | H 0.589816 | 0.07456 | 0.1100 | 5.36 | 0.000 |
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O’Connor, D., Keane, M. (2011). Empirical Issues Relating to Dairy Commodity Price Volatility. In: Piot-Lepetit, I., M'Barek, R. (eds) Methods to Analyse Agricultural Commodity Price Volatility. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7634-5_5
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