High-frequency trading model for a complex trading hierarchy
AbstractFinancial markets exhibit a complex hierarchy among different processes, e.g. a trading time marks the initiation of a trade, and a trade triggers a price change. High-frequency trading data arrive at random times. By combining stochastic and agent-based approaches, we develop a model for trading time, trading volume, and price changes. We generate intertrade time (time between successive trades) Δ t i , and the number of shares traded q (Δ t i ) as two independent but power-law autocorrelated processes, where Δ t i is subordinated to q (Δ t i ), and Δ t i is more strongly correlated than q (Δ t i ). These two power-law autocorrelated processes are responsible for the emergence of strong power-law correlations in (a) the total number of shares traded N (Δ T) and (b) the share volume Q Δ T calculated as the sum of the number of shares q i traded in a fixed time interval Δ T. We find that even though q (Δ t i ) is weakly power-law correlated, due to strong power-law correlations in Δ t i , the (integrated) share volume exhibits strong long-range power-law correlations. We propose that intertrade times and bid--ask price changes share the same volatility mechanism, yielding the power-law autocorrelations in absolute values of price change and power-law tails in the distribution of price changes. The model generates the log-linear functional relationship between the average bid--ask spread ⟨ S⟩ Δ T and the number of trade occurrences N Δ T , and between ⟨ S⟩ Δ T and Q Δ T . We find that both results agree with empirical findings.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Quantitative Finance.
Volume (Year): 12 (2012)
Issue (Month): 4 (October)
Contact details of provider:
Web page: http://www.tandfonline.com/RQUF20
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Schinckus, C., 2013. "Between complexity of modelling and modelling of complexity: An essay on econophysics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3654-3665.
- Pyrlik, Vladimir, 2013. "Autoregressive conditional duration as a model for financial market crashes prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6041-6051.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty).
If references are entirely missing, you can add them using this form.