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The tools presented in this section provide a visual representation of historical periods of excessive global price volatility from 2000-present, as well as a daily volatility status. This status can alert policymakers when world markets are experiencing a period of excessive food price volatility; this information can then be used to determine appropriate country-level food security responses, such as the release of physical food stocks.
A time period of excessive price volatility: A period of time characterized by extreme price variation (volatility) is a period of time in which we observe alarge number of extreme positive returns. An extreme positive return is defined to be a return that exceeds a certain preestablished threshold. This threshold is normally taken to be a high order (95 or 99%) conditional quantile, (i.e. a value of return that is exceeded with low probability: 5 or 1%). In this model, we are using the 95% quantile and look at time in 60-day increments.
Our lighting system:
The probability that we will observe k days of extreme price returns (returns above the 95% quantile as explained in the definition of excessive price volatility) in a period of D consecutive days is defined as:
To define our lighting system, we implement a two-sided test based on a normal approximation for the binomial distribution. Using a period of 60 consecutive days that precede any date (i.e. D=60), we compare the probability value obtained from our stochastic model of returns with the chosen 5% probability of observing extreme return.
The decision rule imbedded in the color system is as follows:
Days in volatility reflects the number of continuous days in the current level of volatility. For example, 20 days of low volatility means that since the last instance of moderate or high volatility, there have been 20 days of low volatility.
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Watch Maximo Torero, Director of IFPRI's Markets, Trade, and Institutions Division, and IFPRI Researcher Carlos Martins-Filho describe the model behind the IFPRI's "Excessive Food Price Variation Early Warning System".