If prices of assets are not aligned to their net present value, a trading strategy may be implemented when actual prices revert to fundamentals. This hypothesis is informally tested in real-time using a trading strategy which consists of identifying whether the equity index is over or under-...

We propose a parametric state space model with accompanying estimation and forecasting framework that combines long memory and level shifts by decomposing the underlying process into a simple mixture model and ARFIMA dynamics. The Kalman filter is used to construct the likelihood function after...

If prices of assets are not aligned to their net present value, a trading strategy may be implemented when actual prices revert to fundamentals. A real-time trading strategy is introduced based on the assumption that reversion occurs in later periods. The fundamental price is constructed in real...

We introduce a non-Gaussian dynamic mixture model for macroeconomic forecasting. The Locally Adaptive Signal Extraction and Regression (LASER) model is designed to capture relatively persistent AR processes (signal) contaminated by high frequency noise. The distribution of the innovations in...