There's more to volatility than volume

DOI10.1080/14697680600835688zbMath1134.91547arXivphysics/0510007OpenAlexW3124711722MaRDI QIDQ3437394

Fabrizio Lillo, J. Doyne Farmer, László Gillemot

Publication date: 9 May 2007

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/physics/0510007

zbMATH Keywords

volumefinancial marketsvolatilitysubordinated processes

Mathematics Subject Classification ID

Related Items (14)

No-arbitrage semi-martingale restrictions for continuous-time volatility models subject to leverage effects, jumps and i.i.d. noise: theory and testable distributional implications ⋮ The times change: multivariate subordination. Empirical facts ⋮ Bridging stylized facts in finance and data non-stationarities ⋮ Relation between bid–ask spread, impact and volatility in order-driven markets ⋮ Order aggressiveness, pre-trade transparency, and long memory in an order-driven market ⋮ Modeling the coupled return-spread high frequency dynamics of large tick assets ⋮ The dynamic and dependence of takaful and conventional stock return behaviours: evidence from the insurance industry in Saudi Arabia ⋮ The non-random walk of stock prices: the long-term correlation between signs and sizes ⋮ The impact of heterogeneous trading rules on the limit order book and order flows ⋮ Did long-memory of liquidity signal the European sovereign debt crisis? ⋮ The market nanostructure origin of asset price time reversal asymmetry ⋮ Normally distributed high-frequency returns: a subordination approach ⋮ How efficiency shapes market impact ⋮ High-frequency trading model for a complex trading hierarchy

Uses Software

• longmemo

Cites Work

• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• The Price Variability-Volume Relationship on Speculative Markets
• A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices
• The Distribution of Realized Exchange Rate Volatility
• Modeling and Forecasting Realized Volatility

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