Duration, Volume and Volatility Impact of Trades

By Simone Manganelli. Social Science Research Network, 2005

218 citations recorded by the source corpus.

This paper develops a new econometric framework to model duration, volume and volatility simultaneously. We obtain an econometric reduced form that incorporates causal and feedback effects among these variables. We construct impulse-response functions that show how the system reacts to a perturbation of its long-run equilibrium. The methodology is applied to two groups of stocks from NYSE, classified according to their trade intensity. We document how the two groups of stocks are characterised by different dynamics: 1) volume is more persistent for frequently traded stocks than for the infrequently traded ones; 2) the well-known positive relationship between volume and price variability holds only for the frequently traded stocks at the ultra high frequency level; 3) the trade arrival process can be considered exogenous only for the not frequently traded stocks; 4) the more frequently traded the stock, the faster the market returns to its full information equilibrium after a perturbation JEL Classification: C32, G14

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/dc1cee63869b8e28d04a90334045bd2db4fd1716

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

Citations in the corpus

Liquidity Cycles and Make/Take Fees in Electronic Markets (2013, unknown): “Dividing these two equations gives, (M + (M 1)) 2 ((1 + )N 1) z = 1; ( 32 )”
Dynamics of Trade-By-Trade Price Movements: Decomposition and Models (2003, unknown): “Other work which relates to this topic includes papers by Meddahi, Renault, and Werker (1998), Ghysels and Jasiak (1998) and Manganelli (2001) who combine GARCH models for price returns with ACD style models for the times between trades.”
Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes (2010, unknown): “, Weglarczyk et al. (2005). Third, we employ the above mixture distribution to specify a so-called zero-augmented MEM (ZA-MEM) that allows for maximum likelihood estimation in the presence of zero observations.”
The role of volume in order book dynamics: a multivariate Hawkes process analysis (2016, methodology): “di;xijFi) = g(dijFi) h(xijdi;Fi) (3) A possible way to construct a model is thus to specify f or g and h as well as the distribution of the error terms. This approach has been followed for example in Manganelli (2005). In this stream of literature, the event process is described using Engle’s and Ru”
Autoregressive Conditional Duration Models in Finance: A Survey of the Theoretical and Empirical Literature (2008, methodology): “The model can easily be extended to include other variables, such as volume and volatility (Spierdijk, 2004; Manganelli, 2005).”
A Multiple Indicators Model for Volatility Using Intra-Daily Data (2003, methodology): “Such a specification was adopted in Engle and Russell (1998) for durations, Manganelli (2002) for volume transaction data and Chou (2001) for high–low range.”