An Autoregressive Conditional Filtering Process to remove Intraday Seasonal Volatility and its Application to Testing the Noisy Rational Expectations Model

Authors

Jang Hyung Cho, Florida International UniversityFollow

Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Finance

First Advisor's Name

Robert T. Daigler

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Ali M. Parhizgari

Third Advisor's Name

Brice Dupoyet

Fourth Advisor's Name

Krishnamurthy Surysekar

Fourth Advisor's Committee Title

Committee Member

Fifth Advisor's Name

Xiaoquan Jiang

Fifth Advisor's Committee Title

Committee Member

Keywords

Information, Noise, Noisy rational expectations, Variance decomposition, Filtering, Seasonality

Date of Defense

7-15-2008

Abstract

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 conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.

Identifier

FI08121903

Recommended Citation

Cho, Jang Hyung, "An Autoregressive Conditional Filtering Process to remove Intraday Seasonal Volatility and its Application to Testing the Noisy Rational Expectations Model" (2008). FIU Electronic Theses and Dissertations. 60.
https://digitalcommons.fiu.edu/etd/60