V.3:4 (142-144): Trading With ARIMA Forecasts by John F. Kepka

V.3:4 (142-144): Trading With ARIMA Forecasts by John F. Kepka
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Trading With ARIMA Forecasts by John F. Kepka

This article will attempt to present some insight into using ARIMA or Box-Jenkins forecasting techniques on the Standard & Poor (S&P) 500 index futures contract. First, I will recap ARIMA in layman's terms.

ARIMA is translated as AutoRegressive Integrated Moving Average. AutoRegressive means that the observations today depend on the previous observations. Moving Average means that the model includes errors made in the previous forecasts. Integrated takes into account the trend of the observations. ARIMA models (equations) are abbreviated as ARIMA (p,d,q) where p is the number of autoregressive terms, d is the number of differences, and q is the number of moving average terms.

Forecasting futures prices with an ARIMA model make sense because the market is composed of buyers and sellers whose equity is directly affected by each price fluctuation. Each price change represents a real gain to one group and a real loss to the other. Rising prices instill confidence in the bull's camp and provide them with extra ammunition to shoot bears. This means that successive observations in price are not completely independent, (i.e., random). Margin calls certainly are not random. The ARIMA model then is composed of a predicted value which is dependent on previous prices, a trend component, and an error term. Prices exhibit trends; there are errors in forecasting; and noise or randomness lives in the market . Hence, the ARIMA (p,d,q) model makes sense in trying to forecast the real world. In fact, an ARIMA (0,1,1) model is equivalent to exponential smoothing with which every trader is more or less familiar.

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