Abstract
Volatility clustering and fat tails are prominently observed in financial markets. Here, we analyze the underlying mechanisms of three agent-based models explaining these stylized facts in terms of market instabilities and compare them on empirical grounds. To this end, we first develop a general framework for detecting tail events in stock markets. In particular, we introduce Hawkes processes to automatically identify and date onsets of market turmoils which result in increased volatility. Second, we introduce three different indicators to predict those onsets. Each of the three indicators is derived from and tailored to one of the models, namely quantifying information content, critical slowing down or market risk perception. Finally, we apply our indicators to simulated and real market data. We find that all indicators reliably predict market events on simulated data and clearly distinguish the different models. In contrast, a systematic comparison on the stocks of the Forbes 500 companies shows a markedly lower performance. Overall, predicting the onset of market turmoils appears difficult, yet, over very short time horizons high or rising volatility exhibits some predictive power.
Highlights
Financial market prices are vastly fluctuating with prices occasionally rallying upwards and suddenly collapsing back down
Econophysics has since developed and proposed models which explain these and other stylized facts of volatility in terms of endogenous dynamics arising from the collective action of many heterogeneous traders (Aymanns and Farmer 2015; Franke and Westerhoff 2016; Lux and Marchesi 1999; Patzelt and Pawelzik 2013; Samanidou et al 2007)
In order to rigorously evaluate and compare all indicators, we have proposed a novel method for dating the onset of volatility clusters, i.e., market events
Summary
Financial market prices are vastly fluctuating with prices occasionally rallying upwards and suddenly collapsing back down. Econophysics has since developed and proposed models which explain these and other stylized facts of volatility in terms of endogenous dynamics arising from the collective action of many heterogeneous traders (Aymanns and Farmer 2015; Franke and Westerhoff 2016; Lux and Marchesi 1999; Patzelt and Pawelzik 2013; Samanidou et al 2007) According to this view, market fluctuations are amplified by feedbacks inherent in the dynamics of traders, e.g., arising from chartist strategies (Lux and Marchesi 1999), herding (Franke and Westerhoff 2016) or leverage targeting (Aymanns and Farmer 2015). Market fluctuations are amplified by feedbacks inherent in the dynamics of traders, e.g., arising from chartist strategies (Lux and Marchesi 1999), herding (Franke and Westerhoff 2016) or leverage targeting (Aymanns and Farmer 2015) While these models can replicate several stylized facts, their empirical estimation and analysis remains challenging. Thereby being in the realm of classical statistics without influences from the science of complex systems
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