| Institution: | 1. Department of Finance, Deakin Business School, Deakin University, Melbourne, Victoria, Australia;2. Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, Illinois, USA;3. Department of Economics, University of Pretoria, Pretoria, South Africa
IPAG Business School, Paris, France;4. College of Business Administration, University of Nebraska at Omaha, Omaha, Nebraska, USA |
| Abstract: | This paper examines volatility linkages and forecasting for stock and foreign exchange markets from a novel perspective by utilizing a bivariate Markov-switching multifractal model that accounts for possible interactions between stock and foreign exchange markets. Examining daily data from major advanced and emerging nations, we show that generalized autoregressive conditional heteroskedasticity models generally offer superior volatility forecasts for short horizons, particularly for foreign exchange returns in advanced markets. Multifractal models, on the other hand, offer significant improvements for longer horizons, consistently across most markets. Finally, the bivariate multifractal model provides superior forecasts compared to the univariate alternative in most advanced markets and more consistently for currency returns, while its benefits are limited in the case of emerging markets. |
