2016
DOI: 10.1016/j.ribaf.2016.04.010
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Periodically collapsing bubbles in the South African stock market

Abstract: Abstract:This paper studies the existence and timing of bubbles in South Africa's stock market. An empirical model of bubble formation is tested against three competing models of asset price returns that rule out the existence of bubbles. The model controls for nonlinearities inherent in asset price returns by allowing for the existence of multiple regimes. The bubble model fits the data better than the competing models and suggests that the formation and existence of periodically collapsing bubbles are a real… Show more

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Cited by 33 publications
(12 citation statements)
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“…Balcilar et al . () show that there is a correlation between the probability that a bubble will burst in the JSE all share index and the relative size of the bubble. They identify 10 past episodes of possible bubbles in the JSE all share index.…”
Section: Establishing the Financial Cycle For South Africamentioning
confidence: 95%
“…Balcilar et al . () show that there is a correlation between the probability that a bubble will burst in the JSE all share index and the relative size of the bubble. They identify 10 past episodes of possible bubbles in the JSE all share index.…”
Section: Establishing the Financial Cycle For South Africamentioning
confidence: 95%
“…In a related development, (Balcilar et al, 2016;Almudhaf, 2017) among others have documented existence of explosive behavior (rational behavior) for some selected African markets.…”
Section: Literature Reviewmentioning
confidence: 97%
“…Ever since, a number of studies (Su et al, 2017;Brunnermeier, 2008;Wachira, 2013) have attempted to investigate the presence or otherwise of asset bubbles with majority employing linear base models like (Johansen and Juselius, 1992;Johansen, et al, 2000;Johansen and Juselius, 1992) with symmetric adjustment which fails to capture asymmetries property of the data generating process as it has lower power in an asymmetric adjustment process (Escobari et al, 2017); Su et al, 2017;Miao and Zhou, 2015b); these methods also fail to incorporate structural breaks into the model, implying that the power to reject a unit decreases when stationary alternative is true and structural break is ignored. Other methods includes Markovswitching model that fails to distinguish between periods likely to appear spuriously explosive resulting from high variance and periods with genuine explosive behavior (Funke et al, 1994;Phillips, 2011) PSY; (Phillips et al, 2015) (PWY) that have high chances of erroneously interpreting the presence of explosive behaviour for the presence of rational bubbles (Balcilar et al, 2016;Caspi and Graham, 2018;Ye et al, 2011). We extends extant literature by employing (Herzog, 2015) econophysics frequency domain model that allows for stochastic bubbles, not prone to model identification problem to examine the existence of bubbles in the three leading oil market price indexes.…”
Section: Introductionmentioning
confidence: 99%
“…for Botswana, 1997to 2008for Egypt, 1997to 2008for Ghana, 1997to 2008for Kenya, 1988to 2008for Nigeria, 1997to 2008for Mauritius, 1997 (Phillips, et al,2015;Balcilar, et al, 2016).…”
Section: Empirical Literaturementioning
confidence: 99%