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Econometric modeling of volatility in the autoregressive conditional heteroscedasticity (ARCH) specification of conditional volatility gained significance especially following the important paper of Engle (1982).
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Yield simulations based on the available time series of recorded yields at the county level (minimum 10 years), modeling of yield volatility within the county, simulation of yield reduction, and derivation of insurance risk metrics.
That's an example of volatility.
Additionally, augmenting GARCH models with asymmetric power terms to obtain APGARCH models and merging fractional integration with APGARCH models to obtain FIAPGARCH models provide improvements in terms of volatility modeling.
Accurate modeling and forecasting of volatility in asset returns are major issues in financial economics.
The approximation of the solution of BSDEs in the Markovian case were initiated in the work Kutoyants and Zhou (2014), where the model of small volatility was considered.
A kinetic model of the volatility of oxide phases is used to describe the excellent thermal stability of the coatings that possess the highest post-anneal emittance values (ZrO2+18%TiO2+10%Y2O3O3, ZrC, and Al2O3+TiO2).
Similar to the VAR model, spillover effects of volatility from one market to another market are measured in the BEKK model over the off-diagonal elements of A and G. Transmission in the conditional mean equation is tested via the off-diagonal elements ϕ ij,l of the parameter matrices Φ l, l = 1 … p.
GARCH models can provide a systematic framework for modelling volatility of time series [14, 15].
The advances in data processing and the mathematics of volatility modeling allow us to quickly and efficiently model markets in ways unthinkable even some ten years ago.
The information content of option implied volatility and realized volatility under market imperfections are studied in the context of GARCH modeling and volatility forecasts of Taiwan stock market (TAIEX) returns.
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