Results 201 to 210 of about 291,653 (252)

Spatial Price Transmission and Dynamic Volatility Spillovers in the Global Grain Markets: A TVP-VAR-Connectedness Approach. [PDF]

Foods
Xue H, Du Y, Gao Y, Su WH.
europepmc   +1 more source

Forecasting and unveiling the impeded factors of total export of Bangladesh using nonlinear autoregressive distributed lag and machine learning algorithms. [PDF]

Heliyon
Akhter T, Ratna TS, Ahmed F, Babu MA, Hossain SFA.   +4 more
europepmc   +1 more source

Hedging irrigated maize crop yields using temperature derivatives in Malawi. [PDF]

Heliyon
Dennis Chirwa PB, Dzupire NC.
europepmc   +1 more source

Modeling the distribution of jet fuel price returns based on fat-tail stable Paretian distribution. [PDF]

PLoS One
Lin S, Zhang S, Wang C, He F, Xu Z, Zhang Y.   +5 more
europepmc   +1 more source

DeepVol: volatility forecasting from high-frequency data with dilated causal convolutions. [PDF]

Quant Finance
Moreno-Pino F, Zohren S.
europepmc   +1 more source

Autoregressive Conditional Parameter Model with Heteroskedastic Regressors [PDF]

SSRN Electronic Journal, 2016
To do with the ARCH effects in explanatory variables, a new time-varying parameter regression is developed. The autoregressive conditional parameter (ACP) model with heteroskedastic regressors extends the ACP model of Lu and Wang (2016) by allowing explanatory variables to follow a multivariate GARCH process.
Shouyang Wang, Fengbin Lu
openaire   +1 more source

Fractionally integrated generalized autoregressive conditional heteroskedasticity [PDF]

Journal of Econometrics, 1996
Abstract The new class of Fractionally Integrated Generalized AutoRegressive Conditionally Heteroskedastic (FIGARCH) processes is introduced. The conditional variance of the process implies a slow hyperbolic rate of decay for the influence of lagged squared innovations.
Tim Bollerslev, Hans Ole Mikkelsen, Richard T. Baillie   +2 more
openaire   +1 more source

Autoregressive Conditional Heteroskedasticity [PDF]

, 2007
All models discussed so far use the conditional expectation to describe the mean development of one or more time series. The optimal forecast, in the sense that the variance of the forecast errors will be minimised, is given by the conditional mean of the underlying model.
Uwe Hassler, Gebhard Kirchgässner, Jürgen Wolters   +2 more
openaire   +1 more source

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Autoregressive conditional heteroskedasticity and changes in regime

Journal of Econometrics, 1994
ARCH models often impute a lot of persistence to stock volatility and yet give relatively poor forecasts. One explanation is that extremely large shocks, such as the October 1987 crash, arise from quite different causes and have different consequences for subsequent volatility than do small shocks. We explore this possibility with U.S.
James D. Hamilton, Raul Susmel
openaire   +2 more sources