Herd Behaviour, Bubbles and Crashes

Lux, Thomas

doi:10.2307/2235156

“…In contrast to the more elaborate treatment in Lux (1995Lux ( , 1997, this reasoning, which can also be found in Lux (1998, p. 149), is sufficient for an infinite population. A rigorous mathematical argument that begins with a finite population size and the intrinsic noise it implies is spelled out in Franke (2008aFranke ( or 2008b.…”

mentioning

confidence: 90%

Why a simple herding model may generate the stylized facts of daily returns: explanation and estimation

Franke

¹

,

Westerhoff

²

2014

J Econ Interact Coord

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe paper proposes an elementary agent-based asset pricing model that, invoking the two trader types of fundamentalists and chartists, comprises four features: (i) price determination by excess demand; (ii) a herding mechanism that gives rise to a macroscopic adjustment equation for the market fractions of the two groups; (iii) a rush towards fundamentalism when the price misalignment becomes too large; and (iv) a stronger noise component in the demand per chartist trader than in the demand per fundamentalist trader, which implies a structural stochastic volatility in the returns. Combining analytical and numerical methods, the interaction between these elements is studied in the phase plane of the price and a majority index. In addition, the model is estimated by the method of simulated moments, where the choice of the moments reflects the basic stylized facts of the daily returns of a stock market index. A (parametric) bootstrap procedure serves to set up an econometric test to evaluate the model's goodness-of-fit, which proves to be highly satisfactory. The bootstrap also makes sure that the estimated structural parameters are well identified.JEL classification: D84; G12; G14; G15.

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“…Again with this approach, abrupt declines in stock prices can occur without any real change in the underlying fundamentals. Lux (1995) linked the phenomena of market crashes to the process of phase transition from thermodynamics and modeled the emergence of bubbles and crashes as a result of herd behavior among heterogeneous traders in speculative markets. Finally, a strand of literature documenting precursory patterns and log-periodic signatures years before the largest crashes in the modern history suggested that crashes have an endogenous origin in 'crowd' behavior and through the interactions of many agents (Sornette and Johansen, 1998;Johansen et al, 2000;Sornette, 2002Sornette, , 2004.…”

Section: Introductionmentioning

confidence: 99%

Realizing stock market crashes: stochastic cusp catastrophe model of returns under time-varying volatility

Baruník

¹

,

Kukačka

²

2014

Quantitative Finance

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. This paper develops a two-step estimation methodology that allows us to apply catastrophe theory to stock market returns with time-varying volatility and to model stock market crashes. In the first step, we utilize high-frequency data to estimate daily realized volatility from returns. Then, we use stochastic cusp catastrophe on data normalized by the estimated volatility in the second step to study possible discontinuities in the markets. We support our methodology though simulations in which we discuss the importance of stochastic noise and volatility in a deterministic cusp catastrophe model. The methodology is empirically tested on nearly 27 years of U.S. stock market returns covering several important recessions and crisis periods. While we find that the stock markets showed signs of bifurcation in the first half of the period, catastrophe theory was not able to confirm this behavior in the second half. Translating the results, we find that the U.S. stock market's downturns were more likely to be driven by the endogenous market forces during the first half of the studies period, while during the second half of the period, the exogenous forces seem to be driving the market's instability,. The results suggest that the propose methodology provides an important shift in the application of catastrophe theory to stock markets. Terms of use: Documents in AbstractThis paper develops a two-step estimation methodology that allows us to apply catastrophe theory to stock market returns with time-varying volatility and to model stock market crashes. In the first step, we utilize high-frequency data to estimate daily realized volatility from returns. Then, we use stochastic cusp catastrophe on data normalized by the estimated volatility in the second step to study possible discontinuities in the markets. We support our methodology through simulations in which we discuss the importance of stochastic noise and volatility in a deterministic cusp catastrophe model. The methodology is empirically tested on nearly 27 years of U.S. stock market returns covering several important recessions and crisis periods. While we find that the stock markets showed signs of bifurcation in the first half of the period, catastrophe theory was not able to confirm this behavior in the second half. Translating the results, we find that the U.S. stock market's downturns were more likely to be driven by the endogenous market forces during the first half of the studied period, while during ...

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“…Hong and Stein (1999) developed a framework where the interaction between informed traders (newswatchers), and uninformed (momentum) traders, both with constant absolute risk aversion (CARA) and bounded rationality, are able to explain the commonly found initial underreaction to an information release followed by a subsequent overreaction to a series of information releases. This stream of research stimulated the development in recent years of a literature in heterogeneous agent-based models (HAM), comprising financial markets populated with agents who are neither fully-rational nor utilise the full information set (see also Brock and Hommes, 1997;1998, Lux, 19951998, Brock and LeBaron, 1996, Farmer and Joshi, 2002, and Iori, 2002. Many of these models have relied on computational methods to evaluate the impact on the price formation process of different beliefs about the future evolution of prices (see He and Li, 2008) and incorporated learning processes based on the relationship between market prices and available information.…”

Section: Introductionmentioning

confidence: 99%

“…One common element of these various models is that they attribute the learning process directly to the clients, who evaluate the performance of their investment strategies, and consequently adapt by switching from a poor performing strategy to another which on recent evidence offers more promise (see for instance, Lux, 1995;1998, andMarchesi, 1999;2000). In contrast, the paper by Berk and Green (2004) propose a model where where clients react to past managerial performance.…”

Section: Introductionmentioning

confidence: 99%

The impact on the pricing process of costly active management and performance chasing clients

Bird

¹

,

Casavecchia

²

,

Pellizzari

³

et al. 2011

J Econ Interact Coord

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One of the necessary features of markets to produce efficient pricing is competition between information-based investors who quickly impound new information into price. However, a significant proportion of funds invested in today's equity markets are in the hands of managers who pursue a style that utilises little or none of the available information. We simulate such a market where the funds are being managed using the following three investment styles: fundamental, momentum and index. We confirm that the major pricing anomalies that have been highlighted previously in the literature are a natural consequence of competition between managers utilising these three investment styles. More importantly, we show that this situation is unlikely to change as long as markets continue to be dominated by costly active managers with clients who pursue outperformance.

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Herd Behaviour, Bubbles and Crashes

Cited by 929 publications

References 16 publications

Why a simple herding model may generate the stylized facts of daily returns: explanation and estimation

Why a simple herding model may generate the stylized facts of daily returns: explanation and estimation

Realizing stock market crashes: stochastic cusp catastrophe model of returns under time-varying volatility

The impact on the pricing process of costly active management and performance chasing clients

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