Financial Application of Extended Residual Coherence
Xuze Zhang, Benjamin Kedem
TL;DR
This paper extends residual coherence to multiple time series and introduces an integrated spectrum criterion, providing new insights into financial market volatility analysis.
Contribution
It introduces an extension of residual coherence for multivariate time series and proposes an alternative selection criterion, enhancing financial data analysis.
Findings
New method for analyzing multiple time series interactions
Improved understanding of implied market volatility
Enhanced graphical tools for financial data analysis
Abstract
Residual coherence is a graphical tool for selecting potential second-order interaction terms as functions of a single time series and its lags. This paper extends the notion of residual coherence to account for interaction terms of multiple time series. Moreover, an alternative criterion, integrated spectrum, is proposed to facilitate this graphical selection. A financial market application shows that new insights can be gained regarding implied market volatility.
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Taxonomy
TopicsComplex Systems and Time Series Analysis · Stock Market Forecasting Methods · Neural Networks and Applications