In this paper, we present a new method for modeling timeevolving correlation networks, using a Mean Reversion Autoregressive Model, and apply this to stock market data. The work is motivated by the assumption that the price and return of a stock eventually regresses back towards their mean or average. This allows us to model the stock correlation time-series as an autoregressive process with a ...

Starting from a sample path of a multivariate stochastic process, we study several techniques to isolate linear combinations of the variables with a maximal amount of mean reversion, while constraining the variance of the combination to be larger than a given threshold. We show that many of the optimization problems arising in this context can be solved exactly using semidefinite programming an...

The nature of risk and long-term returns is not fully understood. There need for a measure at multiple horizons. Frequency domain digital signal processing, with an additive noise model, tests the random walk hypothesis individual firm total idiosyncratic 2-month to 4-year periods. All firms have significant 12-month risk. Monthly effects influence small firms. Large mid-cap are influenced by a...

This study investigates mean reversion of low and high stock returns for one- to ten-year periods, using 1,000 random block bootstraps. Regressions later against prior large-cap stocks indicate that generally exhibit more significant than returns. Small-cap display greater two four years five ten years. show much stronger persistent in stocks. Both large- small-cap stocks, however, provide subs...

The present paper characterizes the mean-reverting behavior of six ASEAN markets – Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam – using an autoregressive exponential GARCH-in mean model and daily data from August 2000 to May 2010. The results indicate fast speed of mean-reversion in the returns of these markets but with quite distinct patterns of return dynamics. The e...

In this paperwe testwhethermean reversion in stockmarket prices presents a different behavior in bull and bear markets. We date the US bull and bear periods using Bry and Boschan (1971) algorithm. We examine the order of integration in the S&P 500 stock market index covering a daily period from August 1929 to December 2006 in bull and bear phases. Our results indicate the existence of different...