Forecasting volatility in commodity markets
Commodity prices have historically been
            among the most volatile of international prices.  Measured
            volatility (the standard deviation of price changes) has not
            been below 15 percent and at times has been more than 50
            percent.  Often the volatility of commodity prices has
            exceeded that of exchange rates and interest rates.   The
            large price variations are caused by disturbances in demand
            and supply.  Stockholding leads to some price smoothing, but
            when stocks are low, prices can jump sharply.  As a result,
            commodity price series are not stationary and in some
            periods they jump abruptly to high levels or fall
            precipitously to low levels relative to their long-run
            average.  Thus it is difficult to determine long-term price
            trends and the underlying distribution of prices.  The
            volatility of commodity prices makes price forecasting
            difficult.  Indeed, realized prices often deviate greatly
            from forecasted prices, which has led to the practice of
            giving forecasts probability ranges.  But assigning
            probability ranges requires forecasting future price
            volatility, which, given uncertainties about true price
            distribution, is difficult.  One potentially useful source
            of information for forecasting volatility is the volatility
            forecasts imbedded in the prices of options written on
            commodities traded in exchanges. Options give the holder the
            right to buy (call) or sell (put) a certain commodity at a
            certain date at a fixed (exercise) price.  Options prices
            depend on several variables, one of which is the expected
            volatility up to the maturity date.  Given a specific
            theoretical model, the market prices of options can be used
            to derive the market's expectations about price
            volatility and the price distribution.  The authors
            systematically analyze different methods' abilities to
            forecast commodity price volatility (for several
            commodities).  They collected the daily prices of commodity
            options and other variables for seven commodities (cocoa,
            corn, cotton, gold, silver, sugar, and wheat).  They
            extracted the volatility forecasts implicit in options
            prices using several techniques.  They compared several
            volatility forecasting methods, divided into three
            categories:  (1) forecasts using only expectations derived
            form options prices; (2) forecasts using only time-series
            modeling; (3) forecasts that combine market expectations and
            time-series modeling (a new method devised for this
            purpose).  They find that the volatility forecasts produced
            by method 3 outperform the first two as well as the naive
            forecast based on historical volatility.  This result holds
            both in and out of sample for almost all commodities considered.