Volatility and duration models for financial intaday data: formulation, estimation and evaluation
This paper develops and tests empirically counting models for high frequency data: BIN (1,1) model with Poisson process, to check if this model allows to capture the clusturing phenomenon in the case of high frequence data, concening stocks intaday data. The process of estimation of the model using data generating process (DGP), then using the acutal data coming from three stocks of NYSE place (BOEING, DISNEY, and AWK), involves good results that validate model for generalisation to BIN(n,n) and for works on density forcasting. In this paper we studie the issue of adequacy of BIN models to capture the activities of financial markets about stocks intraday data (volume, quote, prices), and help to forecast the evolution of financial markets activities.
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