Edoardo Otranto

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The interest toward the classification of time series has recently received a lot of contributions (see Piccolo, 2007, for a review). Most of these studies are devoted to capture the structure of the mean of the process hypothesized as generator of the data, whereas little attention has been devoted to the variance. When dealing with heteroskedastic time(More)
The transmission mechanisms of volatility between markets can be characterized within a new Markov Switching bivariate model where the state of one variable feeds into the transition probability of the state of the other. A number of model restrictions and hypotheses can be tested to stress the role of one market relative to another (spillover,(More)
In the field of financial time series analysis it is widely accepted that the returns (price variations) are unpredictable in the long period [1]; nevertheless, this unappealing constraint could be somehow relaxed if sufficiently short time intervals are considered. In this paper this alternative scenario is investigated with a novel methodology, aimed at(More)
This article includes a unique data set of a balanced daily (Monday, Tuesday and Wednesday) for oil and natural gas volatility and the oil rich economies' stock markets for Saudi Arabia, Qatar, Kuwait, Abu Dhabi, Dubai, Bahrain and Oman, using daily data over the period spanning Oct. 18, 2006-July 30, 2015. Additionally, we have included unique GAUSS codes(More)
a r t i c l e i n f o Unlike previous studies, this paper uses the Multi-Chain Markov Switching model (MCMS) to examine portfolio management strategies based on volatility transmission between six domestic stock markets of Gulf Arab states (GCC) and global markets (i.e., the U.S. S&P 500 index and oil prices) and compares the results with those of the VAR(More)