Power law scaling and "Dragon-Kings" in distributions of intraday financial drawdowns
arXiv:1407.5037 · doi:10.1016/j.chaos.2014.12.002
Abstract
We investigate the distributions of epsilon-drawdowns and epsilon-drawups of the most liquid futures financial contracts of the world at time scales of 30 seconds. The epsilon-drawdowns (resp. epsilon- drawups) generalise the notion of runs of negative (resp. positive) returns so as to capture the risks to which investors are arguably the most concerned with. Similarly to the distribution of returns, we find that the distributions of epsilon-drawdowns and epsilon-drawups exhibit power law tails, albeit with exponents significantly larger than those for the return distributions. This paradoxical result can be attributed to (i) the existence of significant transient dependence between returns and (ii) the presence of large outliers (dragon-kings) characterizing the extreme tail of the drawdown/drawup distributions deviating from the power law. The study of the tail dependence between the sizes, speeds and durations of drawdown/drawup indicates a clear relationship between size and speed but none between size and duration. This implies that the most extreme drawdown/drawup tend to occur fast and are dominated by a few very large returns. We discuss both the endogenous and exogenous origins of these extreme events.
References in corpus (13)
- Power-law distributions in empirical data
- Power laws, Pareto distributions and Zipf's law
- Inverse Cubic Law for the Probability Distribution of Stock Price Variations
- Dragon-kings: mechanisms, statistical methods and empirical evidence
- Quantifying reflexivity in financial markets: towards a prediction of flash crashes
- Causal cascade in the stock market from the ``infrared'' to the ``ultraviolet''
- Rank-Ordering Statistics of Extreme Events: Application to the Distribution of Large Earthquakes
- Stock market crashes are outliers
- Robust Statistical Tests of Dragon-Kings beyond Power Law Distributions
- Inverse Statistics in Economics : The gain-loss asymmetry
- Runaway Events Dominate the Heavy Tail of Citation Distributions
- Self-Excited Multifractal Dynamics
- A simple microstructure return model explaining microstructure noise and Epps effects