File(s) under permanent embargo

Volatility forecasting with bivariate multifractal models

journal contribution
posted on 01.03.2020, 00:00 authored by Ruipeng LiuRuipeng Liu, R Demirer, R Gupta, M Wohar
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.

History

Journal

Journal of forecasting

Volume

39

Issue

2

Pagination

155 - 167

Publisher

Wiley

Location

Chichester, Eng.

ISSN

0277-6693

eISSN

1099-131X

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal