Journal ArticleDOI
Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches
TLDR
In order to reduce the uncertainty associated with a single predictor model, the authors incorporate bagging and combination approaches into a HAR model with the lags of realized volatility and other potential predictors to forecast the realized volatility of agricultural commodity futures in China.About:
This article is published in International Review of Economics & Finance.The article was published on 2017-05-01. It has received 29 citations till now. The article focuses on the topics: Realized variance & Futures contract.read more
Citations
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Journal ArticleDOI
Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series
Matheus Henrique Dal Molin Ribeiro,Matheus Henrique Dal Molin Ribeiro,Leandro dos Santos Coelho,Leandro dos Santos Coelho +3 more
TL;DR: The use of ensembles is recommended to forecast agricultural commodities prices one month ahead, since a more assertive performance is observed, which allows to increase the accuracy of the constructed model and reduce decision-making risk.
Journal ArticleDOI
Forecasting realized volatility of agricultural commodity futures with infinite Hidden Markov HAR models
TL;DR: In this article, a set of HAR models with three types of infinite Hidden Markov regime-switching structures were constructed, and the forecast performance was evaluated using both statistical and economic evaluation measures.
Journal ArticleDOI
Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices
TL;DR: The results indicated that the prediction performance of EEMD combined model is better than that of individual models, especially for the 3‐days forecasting horizon, and the machine learning methods outperform the statistical methods to forecast high‐frequency volatile components.
Journal ArticleDOI
Modeling the Dependency between Extreme Prices of Selected Agricultural Products on the Derivatives Market Using the Linkage Function
Zofia Gródek-Szostak,Gabriela Malik,Danuta Kajrunajtys,Anna Szeląg-Sikora,Jakub Sikora,Maciej Kuboń,Marcin Niemiec,Joanna Kapusta-Duch +7 more
TL;DR: In this paper, the authors identify and estimate the dependency model for the extreme prices of agricultural products listed on the Chicago Mercantile Exchange and test whether the structure of the dependency described with the estimated copulas is a sufficient approximation of reality, and whether it is suitable for modeling empirical data.
Journal ArticleDOI
Forecasting Realized Volatility of Agricultural Commodities
TL;DR: In this article, the authors forecast the realized and median realized volatility of agricultural commodities using variants of the heterogeneous autoregressive (HAR) model, which decomposes volatility measures into their continuous path and jump components and incorporate leverage effects.
References
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Journal ArticleDOI
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
Journal ArticleDOI
Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev,Tim Bollerslev +1 more
TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
Journal ArticleDOI
Bagging predictors
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
Posted Content
Comparing Predictive Accuracy
TL;DR: The authors describes the advantages of these studies and suggests how they can be improved and also provides aids in judging the validity of inferences they draw, such as multiple treatment and comparison groups and multiple pre- or post-intervention observations.
ReportDOI
Comparing Predictive Accuracy
TL;DR: In this article, explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts are proposed and evaluated, and asymptotic and exact finite-sample tests are proposed, evaluated and illustrated.