We give an alternative and unified derivation of the general framework developed in the last
few years for analyzing nonstationary time series. A different approach for handling the …

In the Derivatives financial markets, Futures portfolios are perceived to be instruments of
high risk, despite their flexibility of being used for portfolio protection (hedging) or for …

Many problems in complex dynamical systems involve metastable regimes despite nearly
Gaussian statistics with underlying dynamics that is very different from the more familiar …

Data-driven stochastic parameterization methods use observational data to support and
improve existing prediction systems. Specifically in atmospheric sciences, uncertainty in …

A numerical framework for clustering of time series via a Markov chain Monte Carlo (MCMC)
method is presented. It combines concepts from recently introduced variational time series …

Trading with futures in the Derivatives financial markets, is fraught with risks. To mitigate the
risks, a multipronged approach such as diversification in different asset classes across …

Large-scale stochastic models are relevant in many different fields such as computational
biology, finance, social sciences, communication, and traffic networks. In order to both …

Trading with futures is complex and abounds with risks, to mitigate which a multi-pronged
approach such as diversification in different asset classes across dissimilar markets or …

In this thesis we consider the problem of choosing financial assets from the equity markets
for portfolio construction purposes. We adapt various measures to model the dependence …

Given the explosion of new sensing technologies, data storage has become significantly
cheaper and consequently, people increasingly rely on wearable devices to create personal …