Generalized exponential moving average (EMA) model with particle filtering and anomaly detection

摘要

This paper proposes a generalized exponential moving average (EMA) model, a new stochastic volatility model with time-varying expected return in financial markets. In particular, we effectively apply a particle filter (PF) to sequential estimation of states and parameters in a state space framework. Moreover, we develop three types of anomaly detectors, which are implemented easily in the PF algorithm to be used for investment decision. As a result, a simple investment strategy with our scheme is superior to the one based on the standard EMA and well-known traditional strategies such as equally-weighted, minimum-variance and.risk parity portfolios.