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Forecasting volatility with component conditional autoregressive range model

Wu, Xinyu; Hou, Xinmeng - In: The North American journal of economics and finance : a … 51 (2020), pp. 1-13

Persistent link: https://www.econbiz.de/10012659660

Modeling and forecasting carbon dioxide emission allowance spot price volatility : multifractal vs. GARCH-type volatility models

Segnon, Mawuli; Lux, Thomas; Gupta, Rangan - 2015

price volatility, and compares their forecasting performance to the standard GARCH, fractionally integrated GARCH (FIGARCH …) and the two-state Markov-switching GARCH (MS-GARCH) models via three loss functions (the mean squared error, the mean … criteria and forecast horizons, while MS-GARCH mostly comes out as the least successful model. Applying various VaR backtesting …

Persistent link: https://www.econbiz.de/10011296114

Strict stationarity, persistence and volatility forecasting in ARCH (∞) processes

Davidson, James E. H.; Li, Xiaoyu - In: Journal of empirical finance 38 (2016), pp. 534-547

Persistent link: https://www.econbiz.de/10011663340

How does news sentiment impact asset volatility? : evidence from long memory and regime-switching approaches

Ho, Kin-Yip; Shi, Yanlin; Zhang, Zhaoyong - In: The North American journal of economics and finance : a … 26 (2013), pp. 436-456

Persistent link: https://www.econbiz.de/10010367572

Forecasting volatility in commodity markets with long-memory models

Alfeus, Mesias; Nikitopoulos, Christina Sklibosios - In: Journal of commodity markets 28 (2022), pp. 1-29

Persistent link: https://www.econbiz.de/10014335250

Modelling long-run trends and cycles in financial time series data

Caporale, Guglielmo Maria; Cunado, Juncal; Gil-Alana, … - 2008

This paper proposes a very general time series framework to capture the long-run behaviour of financial series. The suggested model includes linear and non-linear time trends, and stationary and nonstationary processes based on integer and/or fractional degrees of differentiation. Moreover, the...

Persistent link: https://www.econbiz.de/10010264382