RT Journal Article
SR Electronic
T1 Dynamic Density Estimation of Market Microstructure Variables<br/>via Auxiliary Particle Filtering
JF The Journal of Trading
FD Institutional Investor Journals
SP 55
OP 64
DO 10.3905/jot.2012.7.4.055
VO 7
IS 4
A1 Daniel Nehren
A1 David Fellah
A1 Jesus Ruiz-Mata
A1 Yichen Qin
YR 2012
UL https://pm-research.com/content/7/4/55.abstract
AB In this article, we introduce a new density estimation approach to sequentially model the distribution of market microstructure variables on a continuous basis. We employ a nonlinear state space model as our basic framework, where the market microstructure variables follow a distribution with unknown parameters that are identified as the states in the model. Instead of specifying a parametric family for the posterior distribution of the states given the observed microstructure variables, we use a discrete approximation approach. Combining an (auxiliary) particle filtering approach with an efficient change point detection methodology, we efficiently update the posterior distribution of the “states” with newly arrived observations of the relevant microstructure variables, which are detected to indicate a departure from the previous distributional regime. The methodology offers a potential solution to the challenge of updating the distribution of market variables of interest using only relevant data arriving at high frequencies while filtering out noise that is unlikely to indicate departures from previously estimated regimes. The method is validated using simulated data and real bid–ask spread data, and excellent performance is found.TOPICS: Statistical methods, exchanges/markets/clearinghouses