Abstract
We empirically quantify the relation between trading activity-measured by the number of transactions N-and the price change G(t) for a given stock, over a time interval [t,t+Δt]. We relate the time-dependent standard deviation of price changes-volatility-to two microscopic quantities: the number of transactions N(t) in Δt and the variance W2(t) of the price changes for all transactions in Δt. We find that the long-ranged volatility correlations are largely due to those of N. We then argue that the tail-exponent of the distribution of N is insufficient to account for the tail-exponent of P{G > x}. Since N and W display only weak inter-dependency, our results show that the fat tails of the distribution P{G > x} arises from W, which has a distribution with power-law tail exponent consistent with our estimates for G.
Original language | English (US) |
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Pages (from-to) | 137-143 |
Number of pages | 7 |
Journal | Physica A: Statistical Mechanics and its Applications |
Volume | 299 |
Issue number | 1-2 |
DOIs | |
State | Published - Oct 1 2001 |
Event | Application of Physics in Economic Modelling (NATO ARW) - Prague, Czech Republic Duration: Feb 8 2001 → Feb 10 2001 |
Keywords
- Anomalous diffusion
- Econophysics
- Stochastic volatility
- Subordinate processes
ASJC Scopus subject areas
- Statistical and Nonlinear Physics
- Statistics and Probability