Abstract and Keywords
This article provides a comprehensive review of the core ideas and models that have proved central to the forecasting of financial time series. Forecasting the levels or, more appropriately, the changes in financial time series can be an extremely difficult exercise, particularly when using just the past history of the series itself. Forecasts other than the “no change” implied by a random walk tend to be associated with considering long forecast horizons, with taking account of the nonlinearity induced by, say, different regimes, or with incorporating wider information sets, particularly long-run, equilibrium relationships. Other features of financial time series, such as volatility and the time between price changes, are more likely to exhibit some degree of forecastability. The moral of this analysis is that one must consider very carefully what features of a financial time series are likely to be predictable and what features will be inherently unforecastable, and consequently concentrate attention on the former.
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