Gaussian process volatility model

The prediction of time-changing variances is an important task in the modeling of financial data. Standard econometric models are often limited as they assume rigid functional relationships for the evolution of the variance. Moreover, functional parameters are usually learned by maximum likelihood, which can lead to over-fitting. To address these problems we introduce GP-Vol, a novel non-parametric model for time-changing variances based on Gaussian Processes. This new model can capture highly flexible functional relationships for the variances. Furthermore, we introduce a new online algorithm for fast inference in GP-Vol. This method is much faster than current offline inference procedures and it avoids overfitting problems by following a fully Bayesian approach. Experiments with financial data show that GP-Vol performs significantly better than current standard alternatives.

Novel passive launch scheme for ultimate bandwidth improvement of graded-index multimode fibers

We propose a new practical multimode fiber optical launch scheme, providing near single mode group excitation for >5 times transmission bandwidth improvement. Equalization-free transmission of a 10-Gb/s signal over 220-m fiber is achieved in experimental demonstrations. © 2010 Optical Society of America.