Time-Varying Regression Model with Unknown Time-Volatility for Nonstationary Signal Analysis

M. Markov (USA), O. Krasotkina, V. Mottl (Russia), and I. Muchnik (USA)


Time-varying regression, leave-one-out principle, Kalman filter, style analysis of investment portfolios.


The problem of estimating time-varying regression is studied via a mathematical formulation of a class of nonsta tionary signal analysis problems. This problem inevitably concerns the necessity to choose the appropriate level of model volatility – ranging from the full stationarity of in stant regression models to their absolute independence of each other. A modification of the Kalman-Bucy filter and smoother is considered to allow for easy computing of the leave-one-out error of the signal model as a quality indicator of each specific volatility level. On the basis of the proposed technique we develop a new approach to the problem of detecting the hidden dynamics of an investment portfolio in respect to certain market or economic factors.

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