In what aspects GVAR (Global Vectorautoregressive model) is superior to M-GARCH and other GARCH family models when it comes modelling volatility? Can GVAR be considered as a superior methodology to GARCH family models? Any evidence from tourism demand volatility in regard to GVAR?
why considering GARCH model, is that the stock exchange volatility is the as tourism volatility? is that tourism demand look like more volatile? as fa as in my study, tourism become dominated by people income and destination attractive object of one country. Is that study of volatile more relevant rather than traditional econometric forecast.? However, i am in position not to answer directly to your question, but may be the problems is compared between econometric methods and VAR methods, the two are slightly differents.
Thank for the reply. In addition to demand modelling, I am interested in modelling volatility and the dependencies and interdependencies between countries. That's the main reason for me to consider GVAR modelling. Other econometric methods or VAR models will not be very useful in this case.
Kind regards
MGARCH-DCC shows how volatilities and correlations between assets return change over time including their directions(positive or negative) and size (stronger or weaker). The major advantage of MGARCH-DCC in relation to other time-varying estimating methods is that it can consider both mean and variances simultaneously of time series. On the other hand, GVAR considers both time series, panel data and factor analysis to address wide set of macroeconomic and financial issues ranging from policy analysis to risk management.
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