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I have data for 3 yrs of 5 min prices of various equities. I can construct a linear regression model and try to see the in-sample model performance. But I was wondering whether fitting a state-space model might improve my model bias (performance). I have been using R and constructed an AR(2) process (where the coefficients are estimated by MLE) using the package DLM and a Random-walk-plus-noise model but both of these do not give significant bias (model bias is typically < 50 %). Also I am rolling the model as each new data comes in and re-evaluating the model parameters at each step. I do not know if holding the model for some time and forecasting multi-step ahead is gonna make my model more robust. I tried to search online but among the various papers I got, nothing pinpoints to using a state space model for forecasting equity returns. Does someone have any knowledge of this or can point me to a paper which deals with this? Thanks in Advance !!

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Can you go more into detail, maybe use some latex/equations?What is the regression model (dependent/independent variable), how does your state-space model look. Where does AR(2) enter the game? You can do regression with AR errors, or you can formulate a regression like model using State Space models ... what is it that you want to do? –  Richard Sep 30 '13 at 9:12
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