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Evolving Structure in Multivariate Time Series with Application to Stock Sector Data
by
Ginger Davis
University of Virginia
Coauthors: Katherine B. Ensor
Financial data lends itself to multivariate analysis due to its hierarchical structure (e.g. individual securities within sectors within markets). Many models exist for the joint analysis of several financial instruments such as securities due to the fact that they are not independent. These models often assume some type of constant behavior between the instruments over the time period of analysis. Instead of imposing this assumption, we are interested in understanding the dynamic covariance structure in our multivariate financial time series, which will provide us with an understanding of changing market conditions. In order to achieve this understanding, we first develop a multivariate model for the conditional covariance and then examine that estimate for changing structure using multivariate techniques. Specifically, we simultaneously model individual stock data that belong to one of three market sectors and examine the behavior of the market as a whole as well as the behavior of the sectors. Our aims are detecting and forecasting unusual changes in the system, such as market collapses and outliers, and understanding the issue of portfolio diversification in multivariate financial series from different industry sectors. The motivation for this research concerns portfolio diversification. The false assumption that investment in different industry sectors is uncorrelated is not made. Instead, we assume that the comovement of stocks within and between sectors changes with market conditions. Some of these market conditions include market crashes or collapses and common external influences.
We have developed a regime-switching Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) model that captures the dynamics of financial returns. This model provides us with an understanding of changing market conditions which is useful for portfolio diversification assessment. Additionally, we are able to detect unusual events in the data, often forecasting unusual changes in the system, by coupling our model with other multivariate methods for outlier detection. Our method could be used as a key tool for risk assessment in financial investment.
Date received: August 26, 2005
Copyright © 2005 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # caqt-58.