Incorporating financial market volatility to improve forecasts of directional changes in Australian share market returns
Nada Kulendran and
Riccardo Natoli ()
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Riza Erdugan: Victoria University
Nada Kulendran: Victoria University
Riccardo Natoli: Victoria University
Financial Markets and Portfolio Management, 2019, vol. 33, issue 4, No 2, 417-445
Abstract This study examines whether incorporating volatility improves the forecast of directional changes in the returns of Australia’s banking, industrial and resource sectors. This study first estimates a benchmark non-volatility logit regression model and assesses it against four estimated volatility logit models measured by mean absolute deviation, standard deviation, return squared (U2) and range. An out-of-sample prediction performance, assessed by Brier’s QPS statistic and hit ratio, confirms that volatility improves the prediction of directional changes of returns. A simple trading strategy is utilized to provide practical improvement in investors’ market timing decisions.
Keywords: Binary regression model; Volatility estimates; Marginal probability; Forecast comparison (search for similar items in EconPapers)
JEL-codes: G17 (search for similar items in EconPapers)
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