Identifying common dynamic features in stock returns

This paper proposes volatility and spectral based methods for cluster analysis of stock returns. Using the information about both the estimated parameters in the threshold GARCH (or TGARCH) equation and the periodogram of the squared returns, we compute a distance matrix for the stock returns. Clusters are formed by looking to the hierarchical structure tree (or dendrogram) and the computed principal coordinates. We employ these techniques to investigate the similarities and dissimilarities between the "bluechip" stocks used to compute the Dow Jones Industrial Average (DJIA) index. Keywords: Asymmetric e¤ects; Cluster analysis; DJIA stock returns; Periodogram; Threshold GARCH model; Volatility. 1 Introduction Cluster analysis of …nancial time series plays an important role in several areas of application. In stock markets, the examination of mean and variance correlations between asset returns can be useful for portfolio diversi…cation and risk management purposes. In international equity market analysis, the identi…cation of similarities in index returns and volatilities can be useful for grouping countries. Finally, the existence of asymmetric cross-correlations and dependences in asset returns can be of interest for …nancial research. Many existing statistical methods for analysis of multiple asset returns use multivariate volatility models imposing conditions on the covariance matrix that are hard to apply. These include the multivariate generalized autoregressive conditionally heteroskedasticity (GARCH)models of Engle and Kroner (1995) and Kroner and Ng (1998). To avoid these problems, various types of multivariate statistical techniques have been