Parameter estimates and price volatility drivers

We focus first on the exogenous drivers of volatility, and then examine volatility spillovers among the commodities in each group.

Selected oilseeds: For the oilseeds group we have chosen soybeans and rapeseed from the biggest producer markets, US and Europe respectively. Our monthly data period is May 1990 to July 2012.²¹

As Table 3.3 shows, we find the expected strong impacts of the own lagged volatilities, with statistically significant cross effects for both commodities, which we discuss in the next section. Among all the potential exogenous drivers tested, the volatility of the US dollar exchange rate against a basket of other important currencies proved to have a statistically significant positive impact on rapeseed price volatility, but not on soybeans. However, more of the exogenous drivers are found to be statistically significant in the soybean equation. The “La Ni˜na” effect reduces soybean volatilities, consistent with Liao, Chen, and Chen (2010). The stocks-to-use ratio (as a projection) also reduces this volatility.

21The financialisation variable is extrapolated between May 1990 and January 2006.

Table 3.3: Results group “oilseeds”

Variable Soybean Rapeseed

Soybean (US) volatility_t−1 0.84 -0.02

(0.05) (0.02)

Soybean (US) volatility_t−3 -0.02 0.05

(0.05) (0.02)

Rapeseed (EU) volatility_t−1 0.06 0.77

(0.14) (0.06)

Rapeseed (EU) volatilityt−2 0.81 0.08

(0.18) (0.08)

Rapeseed (EU) volatilityt−3 -0.65 -0.06

(0.14) (0.06)

SOI positive (La Ni˜na) -0.01 0.00

(0.00) (0.00)

Soybean US - STU -0.06 -0.02

(0.03) (0.01)

Soybean World “good News” 0.30 0.03

(0.07) (0.03)

Source: Own estimates.

The positive influence of positive changes in the projection of the end of year stock level might look surprising at a first glance. An increase in expected stocks should generally lead to lower prices, and typically also lower price volatilities. Changes in projections might bring new information on the market but with positive changes in the world stock projections, some ambiguity about the release of these stocks and their price effects might also be introduced. This could explain the positive parameter estimate.

Selected vegetable oils: Table 3.4 shows the results for the group of vegetable oils, including biodiesel, for August 2002 to July 2012.²² This group shows substantial volatility spillovers.

As implied by the GARCH estimations, own lagged price volatility plays an important role in each equation, with the exception of rapeseed oil. The dynamics of the system are rather complex and are discussed further below.

Only two exogenous drivers turned out to be statistically significant: The volatility of the strength of the US dollar has a positive impact on the price volatility of palm oil, sunflower

22The relatively late start of the analysis is due to the late availability of Biodiesel data. Again, the financialisation measure is extrapolated between August 2002 and January 2006.

Table 3.4: Results group “vegetable oils”

Soybean (US) volatility_t−1 0.55 -0.30 -0.09 0.24 1.01

(0.13) (0.29) (0.17) (0.09) (0.96)

Soybean (US) volatility_t−2 0.60 0.83 0.19 -0.29 0.42

(0.15) (0.34) (0.19) (0.10) (1.12)

Soybean (US) volatilityt−3 -0.28 -0.49 -0.21 0.11 -1.81

(0.10) (0.24) (0.14) (0.07) (0.80) Sunflower oil (Netherlands) volatilityt−1 0.11 0.50 0.04 -0.07 0.38

(0.06) (0.14) (0.08) (0.04) (0.46) Sunflower oil (Netherlands) volatility_t−2 -0.11 -0.11 0.05 0.12 0.62

(0.05) (0.12) (0.07) (0.04) (0.41) Soybean oil (Argentina) volatility_t−1 0.25 0.50 0.88 -0.07 -0.92

(0.08) (0.17) (0.10) (0.05) (0.57) Soybean oil (Argentina) volatility_t−2 -0.20 -0.38 -0.09 0.02 1.05

(0.07) (0.17) (0.10) (0.05) (0.57) Biodiesel (Germany) volatility_t−1 0.04 -0.04 0.18 0.81 1.23

(0.08) (0.18) (0.10) (0.05) (0.58)

Dollar strength volatility 0.11 0.18 0.17 0.03 -0.30

(0.05) (0.11) (0.06) (0.03) (0.35)

SOI positive (La Ni˜na) -0.00 0.01 -0.00 0.00 0.01

(0.00) (0.00) (0.00) (0.00) (0.01)

Constant -0.00 0.08 0.04 0.01 -0.02

(0.01) (0.02) (0.01) (0.01) (0.08)

Trend 0.00 0.00 0.00 0.00 -0.00

(0.00) (0.00) (0.00) (0.00) (0.00) Source: Own estimates.

oil and soybean oil. The point estimates for all commodities point into the same direction, with the exception of rapeseed oil. This difference in signs, however, is not surprising since rapeseed oil price formation is largely intra-EU (and heavily policy driven in most EU member states).

The only exogenous driver of biodiesel price volatility that is statistically significant positive (though very small) is the “La Ni˜na” effect. Since the impacts of “La Ni˜na” are dry summers in the Northern hemisphere, negative impacts on the harvest in Northern Europe and thus on the input for biodiesel are expected. We also find a (very small) negative and statistically significant trend effect in the rapeseed oil equation - possibly a consequence of the policy framework driven by the EU renewable energy directive.

Table 3.5 summarises the impact of all our potential drivers on price volatility across both commodity groups.

Table 3.5: Identified drivers

Financialisation & Speculation 7 0 7 0

Oil 7 0 7 0

Low stocks 7 1 6 0

Revision of stock projection

(“good news” / “bad news”) 7 1 6 0

Exchange rate 7 4 3 0

Increased consumption 7 0 7 0

Weather shocks 7 1 5 1

Source: Own estimates.

Our estimated price volatilities are based on the residuals of a GARCH model, where, besides the temporal dynamics of the conditional heteroscedasticity, the residuals are supposed to be white noise. It is not surprising, therefore, that the conditional standard deviations are then hard to explain by adding additional information about potential drivers. Our additional information is based on publicly available data, which might not accurately reflect the investigated driver, at least in some cases.

Nevertheless, we find that exchange rates (volatility of the strength of the US dollar) to be significant in most of the markets analysed, reflecting both a direct $ effect, and possibly also an indirect indication of more general macro-economic volatility as reflected in the $ volatility.

Weather shocks are surprisingly seldom an identifiable driver of price volatility. This might be related to the measure of weather shocks which was used here. The Southern oscillation index captures more the general, longer term tendency towards “El Ni˜no”, or “La Ni˜na”, respectively. A localized and temporally more fine-grained measure would possibly yield more significant results.

A low stock level is only once found to have a statistical significant volatility increasing effect, although we only have data for soybeans and soybean oil and not for the other commodities.

The revision of stock levels as a measure for new information on the market is significant in one market. Because the significance occurred for “good news”, i.e. an increase in the amount of the year-end stock level projection, it seems that a huge amount of new

information affects volatility regardless of the goodness of the news. Like for the low stock level we do not have enough precise measures for each commodity to make robust statements.

Consumption, as proxied by the GDP growth variable, is not statistically significant in any of our monthly volatility systems. As with weather shocks, it is difficult to construct an appropriate short-run measure of consumption changes that would better be able to explain price volatility at this temporal resolution. Even extending the period of the GDP change measure does not generate a volatility effect.²³ Oil is also not found to be a statistically significant volatility driver. We only found biodiesel to drive the volatility of soybean oil and rapeseed oil.

Finally, we do not find any hint that financialisation or speculation acts as volatility increasing factors for our monthly data horizon, as was discussed in the public over the past years. This is in line with the majority of the recent literature (Br¨ummer, Korn, Schl¨ußler, Jamali Jaghdani, and Saucedo (2013)). However, we have to be careful with the interpretation of this result. We could not use market specific measures of the variables for all commodities due to data limitations, but had to use a representative market for each group. Market specific measures would have been more appropriate to draw a robust conclusion. Given that we find rich dynamics through lagged own and cross effects, we cannot rule out that financialisation is part of the underlying mechanism of the lagged spillovers. Hence, we should not overly interpret the result that financialisation or speculation have no significant contemporaneous effects on price volatility.