The financial crisis has fueled interest in alternatives to traditional asset classes that might be less affected by large market gyrations and, thus, provide for a less volatile development of a portfolio. One attempt at selecting stocks that are less prone to extreme risks, is obeyance of...

The financial crisis has fueled interest in alternatives to traditional asset classes that might be less a ected by large market gyrations and, thus, provide for a less volatile development of a portfolio. One attempt at selecting stocks that are less prone to extreme risks, is obeyance of...

Long memory (long-term dependence) of volatility counts as one of the ubiquitous stylized facts of financial data. Inspired by the long memory property, multifractal processes have recently been introduced as a new tool for modeling financial time series. In this paper, we propose a parsimonious...

This chapter provides an overview over the recently developed so called multifractal (MF) approach for modeling and forecasting volatility. We outline the genesis of this approach from similar models of turbulent flows in statistical physics and provide details on different specifications of...

Nonlinear, non-Gaussian state space models have found wide applications in many areas. Since such models usually do not allow for an analytical representation of their likelihood function, sequential Monte Carlo or particle filter methods are mostly applied to estimate their parameters. Since...

In the current era of strong worldwide market couplings the global financial village became highly prone to systemic collapses, events that can rapidly sweep through out the entire village. Here we present a new methodology to assess and quantify inter-market relations. The approach is based on...