Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models

Modeling carbon spot and futures price returns with GARCH and Markov switching GARCH models

This paper thoroughly investigates the price dynamics of carbon spot and futures returns for the first commitment period ranging from 2008 to 2012 with the aim to develop an adequate spot-returns-model. We apply a broad spectrum of various GARCH models including different distributions for model innovations. Both time series, spot and futures returns, exhibit asymmetric behavior in their varian...

Markov Switching GARCH models for Bayesian Hedging on Energy Futures Markets

A new Bayesian multi-chain Markov Switching GARCHmodel for dynamic hedging in energy futures markets is developed: a system of simultaneous equations for return dynamics on the hedged portfolio and futures is introduced. More specifically, both the mean and variance of the hedged portfolio are assumed to be governed by two unobserved discrete state processes, while the futures dynamics is drive...

Long memory with Markov-Switching GARCH

A Bayesian MCMC Algorithm for Markov Switching GARCH models

Markov switching GARCH models have been developed in order to address the statistical regularity observed in financial time series such as strong persistence of conditional variance. However, Maximum Likelihood Estimation faces a implementation problem since the conditional variance depends on all the past history of state. This paper shows that this problem can be handled easily in Bayesian in...

Regime Switching Garch Models

We develop univariate regime-switching GARCH (RS-GARCH) models wherein the conditional variance switches in time from one GARCH process to another. The switching is governed by a time-varying probability, specified as a function of past information. We provide sufficient conditions for geometric ergodicity and existence of moments. Because of path dependence, maximum likelihood estimation is no...