The economic and statistical significance of spread forecasts: Evidence from the London Stock Exchange

Abstract

This paper measures the economic and statistical significance of econometric forecasts of bid–ask spreads. The economic importance of these forecasts is assessed by considering the benefits of scheduling trades based on these forecasts. The unrestricted vector autoregression (VAR) model of Huang and Masulis [Rev. Financial Studies 12 (1999) 61] and the two-equation structural model of Huang and Stoll [Rev. Financial Studies 7 (1994) 179] are used to generate intraday h-step ahead forecasts of spreads for 50 stocks listed on the London Stock Exchange (LSE). The period corresponding to the minimum expected spread is then scheduled into the trading activity of the investor. The results indicate that when the unrestricted VAR model is used, the spreads incurred are around 35% lower than the spreads incurred by investors who do not schedule their trades. By contrast, spread discounts of only 5% are obtained when the two-equation structural model is used. The heterogeneity of the economic importance of the spread forecasts generated by the models is confirmed by tests of the statistical significance of the forecasts.