We tightly analyze the sample complexity of CCA, provide a learning algorithm that achieves optimal statistical performance in time linear in the required number of samples (up to log factors), as well as a streaming algorithm with similar guarantees.

Asset prices are typically modeled with the geometric Brownian motion (GBM). Correlation between the assets is exogenously modeled and then ad-hoc assigned to the asset prices. This is conceptually and mathematically unsatisfying. We create a new, simple approach, which simultaneously models stochastic volatility and stochastic correlation. This approach replicates the realworld volatility – co...

Time-series analysis, a relatively uncommon technique in ecological studies, has been applied to annual tree growth-ring series. In agreement with earlier North American work, ARMA(1,1) models were found to be the predominant form for expressing stochastic growth processes, occurring in 58% of the 36 Nothofagus menziesii and N. solandri time-series examined. The remaining 42% conformed to an AR...

Stochastic gravitational waves (SGW) can be detected by measuring a cross-correlation of two or more gravitational wave (GW) detectors. In this paper we describe an optimal SGW search technique in the wavelet domain. It uses a sign correlation test, which allows calculation of the cross-correlation significance for non-Gaussian data. We also address the problem of correlated noise for the GW de...

This paper is concerned with the study of quadratic hedging of contingent claims with basis risk. We extend existing results by allowing the correlation between the hedging instrument and the underlying of the contingent claim to be random itself. We assume that the correlation process ρ evolves according to a stochastic differential equation with values between the boundaries −1 and 1. We keep...