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The stochastic volatility in mean model
Koopman, Siem Jan; Hol Uspensky, Eugenie - 2000
In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean(SVM) model based on Monte Carlo simulation methods. The SVM modelincorporates the unobserved volatility as anexplanatory variable in the mean equation. The same extension...
Persistent link: https://www.econbiz.de/10011303314
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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
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Stock index volatility forecasting with high frequency data
Hol Uspensky, Eugenie; Koopman, Siem Jan - 2002
Volatility (SV) and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models which are both extended to include … outperforms the GARCH model. …
Persistent link: https://www.econbiz.de/10011326944
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GARCH models for daily stock returns : impact of estimation frequency on value-at-risk and expected shortfall forecasts
Ardia, David; Hoogerheide, Lennart - 2013
Persistent link: https://www.econbiz.de/10010191413
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Modeling and forecasting crude oil price volatility : evidence from historical and recent data
Lux, Thomas; Segnon, Mawuli; Gupta, Rangan - 2015
heteroscedasticity (GARCH)-type models to forecast oil price volatility over the time periods from January 02, 1875 to December 31, 1895 … outperformed by other models, with long memory GARCH-type models coming out second best. …
Persistent link: https://www.econbiz.de/10010488966