We present the method of complementary ensemble empirical mode decomposition (CEEMD) and Hilbert-Huang transform (HHT) for analyzing nonstationary financial time series. This noise-assisted approach decomposes any time series into a number of intrinsic mode functions, along with the...

We present a multiscale analysis of the volatility of intraday prices from high-frequency data. Our multiscale framework includes a fractional Brownian motion and microstructure noise as the building blocks. The proposed noisy fractional Brownian motion model is shown to possess a variety of...

Prediction markets provide a promising approach for future event prediction. Most existing prediction markets are built upon auction mechanisms. Although powerful and flexible in some contexts to capture signals of events, these mechanisms 1) mix opinions from experts and amateurs; 2) require...