Asset prices have a high degree of stochastic trends inherent in the time series. In other words, price fluctuations are subject to a large degree of randomness, and therefore it is very difficult to forecast asset prices using traditional time series models such as ARIMA.

Moreover, with much of the trading done on an algorithmic basis today — prices are constantly adjusting on the basis of such forecasts — making it quite hard to exploit an advantage in the markets.

For instance, suppose I build a time series model to predict rainfall in a city for the next three months. My time series model could have a strong degree of accuracy as the forecasts will not influence rainfall levels in the future. However, if everyone uses an ARIMA model to predict asset price fluctuations for the next three months — then subsequent trading on the basis of those forecasts will directly influence previous forecasts — rendering them invalid in many cases.

Background

As a result, it is common to model projected volatility of an asset price in the financial markets — as opposed to forecasting projected price outright.

Let’s see how this can be accomplished using Python. A GARCH model is used to forecast volatility for the EUR/USD and GBP/USD currency pairs, using data from January 2017 — January 2018.

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