A Three‐Regime Model of Speculative Behaviour: Modelling the Evolution of the S&amp;P 500 Composite Index

Brooks, Chris; Katsaris, Apostolos

doi:10.1111/j.1468-0297.2005.01019.x

Cited by 93 publications

(68 citation statements)

References 51 publications

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“…The literature on testing for the presence of bubbles in stock markets alone is extensive (see, for example, Anderson et al, 2010;Brooks and Katsaris, 2003, 2005a, 2005bDezhbakhsh and Demirguc-Kunt;1990;Donaldson and Kamstra, 1996, to name but a few), and the number of papers documenting bubble existence is considerably greater than those finding against. Pastor and Veronesi (2006), on the other hand, argue that the growth of technology stock prices in the 1990s was not necessarily caused by a bubble.…”

Section: Introductionmentioning

confidence: 99%

Speculative bubbles and the cross-sectional variation in stock returns

Anderson

Brooks

2014

International Review of Financial Analysis

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Evidence suggests that rational, periodically collapsing speculative bubbles may be pervasive in stock markets globally, but there is no research that considers them at the individual stock level. In this study we develop and test an empirical asset pricing model that allows for speculative bubbles to affect stock returns. We show that stocks incorporating larger bubbles yield higher returns. The bubble deviation, at the stock level as opposed to the industry or market level, is a priced source of risk that is separate from the standard market risk, size and value factors. We demonstrate that much of the common variation in stock returns that can be attributable to market risk is due to the co-movement of bubbles rather than being driven fundamentals. EFM classification codes: 310, 330, 350 Acknowledgements: We are grateful to Rajesh Tharyan for providing a list of SEDOL codes matched with those from Datastream, and to Apostolos Katsaris, discussions with whom helped to shape the ideas for this paper.1

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Section: Introductionmentioning

confidence: 99%

Speculative bubbles and the cross-sectional variation in stock returns

Anderson

Brooks

2014

International Review of Financial Analysis

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“…One can also divide models according to whether they are intrinsic or extrinsic. Extrinsic models posit that the actual price is equal to a fundamental price plus a bubble component which is not a function of dividends (such as Blanchard and Watson, 1982;Evans, 1991;Van Norden and Schaller, 1999;Brooks and Katsaris, 2005). Intrinsic models either specify the bubble component but make it a function of dividends (such as in Froot and Obstfeld, 1991) or posit that the bubble is itself within the fundamental price (such as .…”

Section: Theorymentioning

confidence: 99%

Testing for bubbles in agriculture commodity markets

Balcombe

Rapsomanikis

2016

EARN

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ABSTRACT:We apply the recent generalized sup augmented Dickey-Fuller (GSADF) test for explosive bubbles (Phillips et al., 2012) to monthly time-series for food, beverages, agricultural raw material, cereals, dairy, meat, oils and sugar indices and a total of 28 agricultural commodities between 1980-2012. We found price bubbles occurred for 6 out of the 10 indices studied and for 6 out of the 28 commodities within food markets. Results from the tests can help implementing policies aimed at mitigating effects of future price bubbles to targeted food commodity markets that may require special attention.

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“…Evans (1991), van Norden and Schaller (1999) and Brooks and Katsaris (2005) are examples of the use of regime switching models to study asset price bubbles.…”

Section: Model Designmentioning

confidence: 99%

“…We do not require that bubble growth is constant over time. Instead, we allow a stochastic growth rate which is strictly positive in expectation as in Brooks and Katsaris (2005).…”

Section: Model Designmentioning

confidence: 99%

Bubbles and Investment Horizons

Guenster

Kole

2009

SSRN Journal

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The current theoretical literature makes contradicting predictions regarding the impact of an investor's horizon on his optimal trading strategy in the presence of bubbles. We analyze this relation empirically using a Regime Switching Model to identify bubbles and crashes. We base our analysis on industry returns and find high positive returns after bubbles at the one-month horizon. At intermediate horizons of 2-4 months our findings are mixed, but thereafter, for horizons up to five years, returns following a bubble are again more positive than returns in the absence of a bubble. We compare a mean-variance as well as a downside-risk averse investor's portfolio allocation in the presence and absence of a bubble. The weight allocated to the bubbly asset is higher for horizons up to 5 years. These findings suggest that even for a rather unsophisticated trader who does not follow daily market news, riding bubbles is a more profitable strategy than refraining from investing in the bubbly asset. Given the broad range of horizons during which riding bubbles is the optimal strategy, our results question the idea that bubbles are zero-sum games.

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A Three‐Regime Model of Speculative Behaviour: Modelling the Evolution of the S&P 500 Composite Index

Cited by 93 publications

References 51 publications

Speculative bubbles and the cross-sectional variation in stock returns

Speculative bubbles and the cross-sectional variation in stock returns

Testing for bubbles in agriculture commodity markets

Bubbles and Investment Horizons

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