The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform

Mustofa Usman - One of the best experts on this subject based on the ideXlab platform.

  • Vector Autoregressive with Exogenous Variable Model and itsApplication in Modeling and Forecasting Energy Data: CaseStudy of PTBA and HRUM Energy
    International Journal of Energy Economics and Policy, 2019
    Co-Authors: Warsono Warsono, Edwin Russel, Wamiliana Wamiliana, Widiarti Widiarti, Mustofa Usman
    Abstract:

    Owing to its simplicity and less restrictions, the vector autoregressive with Exogenous Variable (VARX) model is one of the statistical analyses frequently used in many studies involving time series data, such as finance, economics, and business. The VARX model can explain the dynamic behavior of the relationship between endogenous and Exogenous Variables or of that between endogenous Variables only. It can also explain the impact of a Variable or a set of Variables on others through the impulse response function (IRF). Furthermore, VARX can be used to predict and forecast time series data. In this study, PTBA and HRUM energy as endogenous Variables and exchange rate as an Exogenous Variable were studied. The data used herein were collected from January 2014 to October 2017. The dynamic behavior of the data was also studied through IRF and Granger causality analyses. The forecasting data for the next 1 month was also investigated. On the basis of the data provided by these different models, it was found that VARX (3,0) is the best model to assess the relationship between the Variables considered in this work. Keywords: Vector Autoregressive Model, Vector Autoregressive with Exogenous Variable Model, Granger Causality, Impulse Response Function, Forecasting JEL Classifications: C32, Q4, Q47

Ahmed Ibrahim Alzahrani - One of the best experts on this subject based on the ideXlab platform.

  • multistep short term wind speed prediction using nonlinear auto regressive neural network with Exogenous Variable selection
    alexandria engineering journal, 2021
    Co-Authors: Fuad Noman, Gamal Alkawsi, Ammar Ahmed Alkahtani, Ali Q Alshetwi, S K Tiong, Nasser Alalwan, Janaka Ekanayake, Ahmed Ibrahim Alzahrani
    Abstract:

    Abstract Precise wind speed prediction is a key factor in many energy applications, especially when wind energy is integrated with power grids. However, because of the intermittent and nonstationary nature of wind speed, modeling and predicting it is a challenge. In addition, using uncorrelated multivariate Variables as Exogenous input Variables often adversely impacts the performance of prediction models. In this paper, we present a multistep short-term wind speed prediction using multivariate Exogenous input Variables. We implement different Variable selection methods to select the best set of Variables that significantly improve the performance of prediction models. We evaluate the performance of eight transfer learning methods, four shallow neural networks (NNs), and the persistence method on predicting the future values of wind speed using ultrashort-term, short-term, and multistep time horizons. We performed the evaluation over two-year high-sampled wind speed data averaged at 10-minute intervals. Results show that Nonlinear Auto-Regressive Exogenous (NARX) model outperformed all other methods, achieving an average mean absolute error (MAE) and root mean square error (RMSE) of 0.2205 and 0.3405 for multistep predictions, respectively. Despite the lower performance of the transfer learning methods (i.e., 0.43 and 0.58 for MAE and RMSE, respectively), it is believed that results could be further improved with a better enhancement of the feature selection and model parameters.

Julien Chevallier - One of the best experts on this subject based on the ideXlab platform.

  • Volatility returns with vengeance: Financial markets vs. commodities
    Research in International Business and Finance, 2015
    Co-Authors: Sofiane Aboura, Julien Chevallier
    Abstract:

    To assess how financial markets and commodities are inter-related, this paper introduces a ‘volatility surprise’ component into the asymmetric DCC with one Exogenous Variable (ADCCX) framework. We develop an econometric model in which returns and volatility allow to influence pairs of assets, and derive several case studies linking commodities to stocks, bonds and currencies from 1983 to 2013. The innovative feature of our model is that these volatility spillovers are modeled consistently within the correlation dynamics of the ADCCX model. We find evidence that return and volatility spillovers do exist between commodity and financial markets and that in turn, their relative impact on each other is very substantial.

  • Cross-Market Spillovers with 'Volatility Surprise
    Review of Financial Economics, 2014
    Co-Authors: Sofiane Aboura, Julien Chevallier
    Abstract:

    This article adopts the asymmetric DCC with one Exogenous Variable (ADCCX) model developed by Vargas (2008), by updating the concept of ‘volatility surprise’ to capture cross-market relationships. Current methods for measuring spillovers do not focus on volatility interactions, and neglect cross-effects between the conditional variances. This paper aims to fill this gap. The dataset includes four aggregate indices representing equities, bonds, foreign exchange rates and commodities from 1983 to 2013. The results provide strong evidence of spillover effects coming from the ‘volatility surprise’ component across markets. Against the background of the recent financial crisis, the aim is to contribute to the literature on the interdependencies of financial markets, both in conditional means and (co)variances. In addition, asset management implications are derived.

Manuel Parras - One of the best experts on this subject based on the ideXlab platform.

  • A study on the medium-term forecasting using Exogenous Variable selection of the extra-virgin olive oil with soft computing methods
    Applied Intelligence, 2011
    Co-Authors: Antonio J. Rivera, Pedro Pérez-recuerda, María Dolores Pérez-godoy, María Jose Jesús, María Pilar Frías, Manuel Parras
    Abstract:

    Time series forecasting is an important task for the business sector. Agents involved in the olive oil sector consider that, for the olive oil price, medium-term predictions are more important than short-term predictions. In collaboration with these agents the forecasting of the price of extra-virgin olive oil six months ahead has been established as the aim of this work. According to expert opinion, the use of Exogenous Variables and technical indicators can help in this task and must be included in the forecasting process. The amount of Variables that can be considered makes necessary the use of feature selection algorithms in order to reduce the number of Variables and to increase the interpretability and usefulness of the obtained forecasting system. Thus, in this paper CO^2RBFN, a cooperative-competitive algorithm for Radial Basis Function Network design, and other soft computing methods have been applied to the data sets with the whole set of input Variables and to the data sets with the selected set of input Variables. The experimentation carried out shows that CO^2RBFN obtains the best results in medium term forecasting for olive oil prices with the whole and with the selected set of input Variables. Moreover, the feature selection methods applied to the data sets highlighted some influential Variables which could be considered not only for the prediction but also for the description of the complex process involved in the medium-term forecasting of the olive oil price.

Warsono Warsono - One of the best experts on this subject based on the ideXlab platform.

  • Vector Autoregressive with Exogenous Variable Model and itsApplication in Modeling and Forecasting Energy Data: CaseStudy of PTBA and HRUM Energy
    International Journal of Energy Economics and Policy, 2019
    Co-Authors: Warsono Warsono, Edwin Russel, Wamiliana Wamiliana, Widiarti Widiarti, Mustofa Usman
    Abstract:

    Owing to its simplicity and less restrictions, the vector autoregressive with Exogenous Variable (VARX) model is one of the statistical analyses frequently used in many studies involving time series data, such as finance, economics, and business. The VARX model can explain the dynamic behavior of the relationship between endogenous and Exogenous Variables or of that between endogenous Variables only. It can also explain the impact of a Variable or a set of Variables on others through the impulse response function (IRF). Furthermore, VARX can be used to predict and forecast time series data. In this study, PTBA and HRUM energy as endogenous Variables and exchange rate as an Exogenous Variable were studied. The data used herein were collected from January 2014 to October 2017. The dynamic behavior of the data was also studied through IRF and Granger causality analyses. The forecasting data for the next 1 month was also investigated. On the basis of the data provided by these different models, it was found that VARX (3,0) is the best model to assess the relationship between the Variables considered in this work. Keywords: Vector Autoregressive Model, Vector Autoregressive with Exogenous Variable Model, Granger Causality, Impulse Response Function, Forecasting JEL Classifications: C32, Q4, Q47