Business Commons^™

An Autoregressive Conditional Filtering Process To Remove Intraday Seasonal Volatility And Its Application To Testing The Noisy Rational Expectations Model, Jang Hyung Cho

FIU Electronic Theses and Dissertations

We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive …