Liu, RuipengDemirer, RizaGupta, RanganWohar, Mark E.2020-06-092020-03Liu R, Demirer R, Gupta R, Wohar M. Volatility forecasting with bivariate multifractal models. Journal of Forecasting. 2020;39:155–167. https://doi.org/10.1002/for.2619.0277-6693 (print)1099-131X (online)10.1002/for.2619http://hdl.handle.net/2263/74907This 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.en© 2019 John Wiley & Sons, Ltd. This is the pre-peer reviewed version of the following article : Volatility forecasting with bivariate multifractal models. Journal of Forecasting. 2020;39:155–167. https://doi.org/10.1002/for.2619. The definite version is available at : http://wileyonlinelibrary.com/journal/for.Brazil, Russia, India, China and South Africa (BRICS)Long memoryMultifractal modelsSimulation‐based inferenceVolatility forecastingPostprint Article