Modeling financial durations using penalized estimating functions
Nalini Ravishanker and
Computational Statistics & Data Analysis, 2019, vol. 131, issue C, 145-158
Accurate modeling under least restrictive assumptions of patterns in inter-event durations is of considerable interest in the analysis of high-frequency financial data which show liquidity induced patterns for different stocks and often exhibit diurnal patterns in addition to temporal dependence. For analyzing durations between user-defined events in transaction-by-transaction stock prices from the Trade and Quotes (TAQ) database at Wharton Research Data Services (WRDS), a fast and accurate distribution-free modeling methodology is described and implemented using penalized martingale estimating functions on logarithmic autoregressive conditional duration (Log ACD) models. Three approaches are implemented for parameter estimation: solution of nonlinear estimating equations, recursive formulas for the vector-valued parameter estimates, and iterated component-wise scalar recursions, each using effective starting values from an approximating time series model to increase the accuracy of the final estimates. The analyses provide very good fits and predictions that can assist in trading decisions. The approach can be easily extended to other models for financial durations as well as to a large class of linear and nonlinear time series models.
Keywords: Godambe information; High-frequency data; Inter-event durations; Log ACD models; Online recursions; Penalized estimating function (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:131:y:2019:i:c:p:145-158
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