Determinants of Food price volatility (Results from BVAR Model (Results of…
Determinants of Food price volatility
Production indicator in emerging countries.
Gilbert(2010) stresses that strong economic growth in these countries has a significant effect on price volatility.
Fluctuations in oil prices can lead directly to variations in food prices and can also influence the dynamics of food prices via bio-fuel prices (Busse et al., 2010)
Shocks to the price of financial assets, and especially to currencies, can affect the income of food commodity producers
Drought in Russia or monsoon in India can cause an increase in price through fundamental factors, and this can in turn be reinforced by financial speculation or trade restrictions
Speculation on commodity futures markets
Less prominent effect
done on coffee,cocoa,corn,soyabean,wheat (excluding rice) using SVAR & BVAR model
Rice consumption & production patterns much different. Also shocks from rice has less correlation with other grain markets. These shocks can be stabilized thru trade restrictions & stock piling issues by Asian producers.(Timmer, 2010). This type of procedure is harder to set up in other food commodity markets
Monthly data covering the period from January 2001 to March 2013 compiled by
. “Corn No.2 Yellow”, “Wheat No.2 Soft” & “Soybeans No.1 Yellow”. Brazilian coffee by
. Cocoa & sugar by
Took financial market factors, macroeconomic factors, biofuel market indicators and oil prices
Standard and Poor’s 500 stock index as
the main factor describing global financial markets, for global macroeconomic factors, they used global GDP, The Merrill Lynch Biofuels spot price index, monthly spot prices for both WTI and Brent crude oil as determinants of oil price
SD, IQR, the rolling mean
absolute deviation (MAD) & volatility indicator from a
measure of volatility
. Here, they used SD. They also checked the correlation among these values.
Whatever be the measure of food price volatility, the indicator of dispersion is always time varying. For Example, the correlation b/w SD & IQR is time-varying & that between SD & MAD is stable.
Volatility peaks are not simultaneous inside the set of food commodities and not all were more volatile during the 2007-2008 and 2010-2011 prices surges
Results from BVAR Model
The volatilities of commodities are influenced +vely by their own lags and the impact of the 1st-order lagged volatilities is significantly greater
US IPI (GDP proxy) has a -ve impact on price volatility, and for soybeans (largely produced by the US) the impact is larger after 2 months.
The effect of bio-fuel prices on corn and sugar(inputs of biofuel prod.) volatilities is -ve. +ve influence on coffee & wheat price volatility. neutral for cocoa & soybean
Corn and wheat volatilities a -vely linked to crude oil prices, but cocoa, soybean and sugar volatilities are +vely impacted by oil prices. Coffee volatility not impacted by crude oil.
Financial Mkt indicator has +ve influence on wheat volatility & -ve for cocoa. Its effect on the volatility of other foods is slightly ambiguous since the signs of the coefficients are not clearly identified.
Results of impulse reaction function using shocks
A US IPI shock(GDP) has a -ve impact on the volatility of corn and sugar. Cocoa and corn exhibit the largest reactions.The impact of the shock varies in duration across commodities.The effect of shock is absorbed within 12 months for coffee, corn ,soybeans while it is more for cocoa, sugar & wheat.
Biofuel has -ve effect on corn & wheat volatilities. Downward peak after 2-4 months, depending on the commodity & reverting to intitial value after 16 months. Corn volatility initially increases after a biofuel prices shock, peaks upwards after 4 months and then falls after 10 months. Wheat increases first and peaks after 2 months & dropping after 3 months returning to initial value after 14 months.
An oil price shock leads to an increase in cocoa, coffee, sugar and wheat volatilities (2-3 months), followed by a decrease (downwards peak after 4-6 months). Corn follows opposite & soybean volatility peaks downwards after 4 months before reverting to its initial value
A financial markets shock leads first to an increase in volatilities. In the case of sugar and wheat, volatilities fall below their initial values before reverting back to them