On collective non-gaussian dependence patterns in high frequency financial data

Abstract

The analysis of observed conditional distributions of both lagged and simultaneous intraday price increments of a basket of stocks reveals phenomena of dependence - induced volatility smile and kurtosis reduction. A model based on multivariate t-Student distribution shows that the observed effects are caused by colelctive non-gaussian dependence properties of financial time series.

misprints corrected

References in corpus (4)

• Testing the Gaussian Copula Hypothesis for Financial Assets Dependences
• Symmetry alteration of ensemble return distribution in crash and rally days of financial markets
• Ensemble properties of securities traded in the NASDAQ market
• Modeling stylized facts for financial time series

Cited by in corpus (4)

• Market Mill Dependence Pattern in the Stock Market: Distribution Geometry, Moments and Gaussization
• Market Mill Dependence Pattern in the Stock Market: Modeling of Predictability and Asymmetry via Multi-Component Conditional Distribution
• Market Mill Dependence Pattern in the Stock Market: Multiscale Conditional Dynamics
• Volatility Dynamics of Wavelet-Filtered Stock Prices