Article2000

Econometric Modeling of Multivariate Irregularly-Spaced High-Frequency Data

101 citations0/0 files stored

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stochastic processes and financial applications Financial Markets and Investment Strategies Point processes and geometric inequalities

Abstract

The recent advent of high frequency data provides researchers with transaction by transaction level data. Examples include scanner data from grocery stores or financial transactions data. These new data sets allow researchers to take an unprecedented look at the underlying economic structure of the markets. Often the hypothesis of interest is in the context of multivariate time series data. Analysis of multivariate high frequency data is complicated by the fact that the multiple processes are irregularly spaced in time with different arrival rates. This has lead many investigators to work with aggregated data which can blur the market structure and contaminate the analysis. In this paper we propose a new method of working with the dissaggregated data and develop an econometric model for the arrival rates of multivariate dependent point processes. We apply the model to financial transactions data and estimate models for the bivariate point process of transaction and limit order arrival times. Since limit orders dictate the structure of the limit order book they are a direct determinant of market liquidity. To our knowledge little work has examined the dynamic structure of limit order submissions. Since transactions represent a floor trader or market order demand for liquidity the bivariate model characterizes the dynamic behavior of liquidity supply and demand. The proposed model also allows for marks, or characteristics associated with the arrival times, to influence future arrival rates. For the stock market data analyzed we find strong evidence of codependence in the two processes and both liquidity demand and supply are influenced by past volume and prevailing spreads.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 101 citations exist upstream.

References

1986Generalized autoregressive conditional heteroskedasticity 1998Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data 1987Constraints on short-selling and asset price adjustment to private information 1992Time and the Process of Security Price Adjustment 1986A transaction data study of weekly and intradaily patterns in stock returns 1988Trades, quotes, inventories, and information 1996New York Stock Exchange Systems and Trading Procedures 1968The Correlation Structure of the Output Process of Some Single Server Queueing Systems

Cited by

2006Stochastic Conditional Intensity Processes 2005Order aggressiveness and order book dynamics 2007Modelling the buy and sell intensity in a limit order book market 2018Feasible invertibility conditions and maximum likelihood estimation for observation-driven models 2007Algorithmic Trading Patterns in Xetra Orders 2016Comparison of Value-at-Risk models using the MCS approach

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.