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Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models
Affiliation:1. Institute of Financial Analysis, University of Neuchâtel, Rue A.-L. Breguet 2, 2000 Neuchâtel, Switzerland;2. Department of Finance, Insurance and Real Estate Laval University, Québec, Canada
Abstract:Using weekly data for stock and Forex market returns, a set of MS-GARCH models is estimated for a group of high-income (HI) countries and emerging market economies (EMEs) using algorithms proposed by Augustyniak (2014) and Ardia et al. (2018, 2019a,b), allowing for a variety of conditional variance and distribution specifications. The main results are: (i) the models selected using Ardia et al. (2018) have a better fit than those estimated by Augustyniak (2014), contain skewed distributions, and often require that the main coefficients be different in each regime; (ii) in Latam Forex markets, estimates of the heavy-tail parameter are smaller than in HI Forex and all stock markets; (iii) the persistence of the high-volatility regime is considerable and more evident in stock markets (especially in Latam EMEs); (iv) in (HI and Latam) stock markets, a single-regime GJR model (leverage effects) with skewed distributions is selected; but when using MS models, virtually no MS-GJR models are selected. However, this does not happen in Forex markets, where leverage effects are not found either in single-regime or MS-GARCH models.
Keywords:MS-GARCH models  GARCH models  Returns  Volatility  Latin American countries  High-income countries  Stock  Forex  C22  C52  C53
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