What Proportion of Time is a Particular Market Inefficient? … A Method for Analysing the Frequency of Market Efficiency when Equity Prices Follow Threshold Autoregressions

Ahmed Muhammad Farid () and Satchell Stephen
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Ahmed Muhammad Farid: Faculty of Economics, Selwyn College, University of Cambridge, Sidgwick Avenue Cambridge, Cambridge, CB3 9DD, UK
Satchell Stephen: University of Sydney, Camperdown, NSW 2006, Australia

Journal of Time Series Econometrics, 2018, vol. 10, issue 2, 22

Abstract: We assume that equity returns follow multi-state threshold autoregressions and generalize existing results for threshold autoregressive models presented in Knight and Satchell 2011. “Some new results for threshold AR(1) models,” Journal of Time Series Econometrics 3(2011):1–42 and Knight, Satchell, and Srivastava (2014) for the existence of a stationary process and the conditions necessary for the existence of a mean and a variance; we also present formulae for these moments. Using a simulation study, we explore what these results entail with respect to the impact they can have on tests for detecting bubbles or market efficiency. We find that bubbles are easier to detect in processes where a stationary distribution does not exist. Furthermore, we explore how threshold autoregressive models with i.i.d trigger variables may enable us to identify how often asset markets are inefficient. We find, unsurprisingly, that the fraction of time spent in an efficient state depends upon the full specification of the model; the notion of how efficient a market is, in this context at least, a model-dependent concept. However, our methodology allows us to compare efficiency across different asset markets.

Keywords: market efficiency; bubbles; threshold auto-regressions (search for similar items in EconPapers)
JEL-codes: C24 G14 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1515/jtse-2016-0021

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