Forecasting the prices of crude oil: An iterated combination approach

Zhang, Yaojie; Ma, Feng; Ben-shan, Shi; Huang, Dengshi

doi:10.1016/j.eneco.2018.01.027

Cited by 138 publications

(44 citation statements)

References 41 publications

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“…They found that investors do not gain much profit by following the volume curve. Zhang et al (2018) examined oil price forecasting by using 18 macroeconomic and 18 technical indicators. The results showed accurate forecasts and generated certainty equivalent return gains for a mean-variance investor.…”

Section: Literature Reviewmentioning

confidence: 99%

S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

2020

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This study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange. To achieve the objectives, the study uses descriptive statistics; tests including variance ratio, Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski Phillips Schmidt and Shin; and Autoregressive Integrated Moving Average (ARIMA). The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series, using the ARIMA model. The results reveal that the mean returns of both indices are positive but near zero. This is indicative of a regressive tendency in the long-term. The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values, with few deviations. Hence, the ARIMA model is capable of predicting medium- or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.

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Section: Literature Reviewmentioning

confidence: 99%

S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

2020

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“…Although the well‐known “forecast combination puzzle” shows that the simple mean combination cannot be systematically outperformed by any other combination approaches in out‐of‐sample evaluations, we still introduce another prevailing combination scheme as a robustness check. This combination scheme is the discount mean squared prediction error (DMSPE) combination method, which is widely used by a large body of research (see, e.g., Lin, Wu, & Zhou, ; Ma, Li, et al, ; Rapach et al, ; Stock & Watson, ; Wang et al, ; Zhang, Ma, Shi, & Huang, ; Zhu & Zhu, ). The DMSPE forecasts can be computed as the weighted averages of individual forecasts:

{\overset{truê}{σ}}_{DMSPE, t}^{2} = \sum_{i = 1}^{N} ω_{i, t - 1} {\overset{truê}{σ}}_{i, t}^{2},

where

{\overset{truê}{σ}}_{DMSPE, t}^{2}

denotes the DMSPE forecast at day t ,

{\overset{truê}{σ}}_{i, t}^{2}

is the i th individual forecast of interest, ω i , t − 1 denotes the ex ante combining weight of the i th individual forecast formed at t − 1; the DMSPE weights are determined by

ω_{i, t} = ϕ_{i, t}^{- 1} / \sum_{normalℓ = 1}^{N} ϕ_{normalℓ, t}^{- 1},

where

ϕ_{i, t} = \sum_{s = m + 1}^{t} θ^{t - s} {({RV}_{s} - {\overset{σ}{true}}_{i, s}^{2})}^{2},

where m is the length of in‐sample estimation period and θ denotes a discount factor.…”

Section: Resultsmentioning

confidence: 99%

“…Although the well-known "forecast combination puzzle" shows that the simple mean combination cannot be systematically outperformed by any other combination approaches in out-of-sample evaluations, we still introduce another prevailing combination scheme as a robustness check. This combination scheme is the discount mean squared prediction error (DMSPE) combination method, which is widely used by a large body of research (see, e.g., Lin, Wu, & Zhou, 2017;Ma, Li, et al, 2018;Rapach et al, 2010;Stock & Watson, 2004;Wang et al, 2016;Zhang, Ma, Shi, & Huang, 2018;Zhu & Zhu, 2013). The DMSPE forecasts can be computed as the weighted averages of individual forecasts:…”

Section: Alternative Model Combination Schemesmentioning

confidence: 99%

“…where m is the length of in-sample estimation period and θ denotes a discount factor. Following Rapach et al (2010), Zhu and Zhu (2013), Wang et al (2016), and Zhang et al (2018), we consider two values of θ, namely 1 and 0.9. Accordingly, two DMSPE combination methods-DMSPE(1) and DMSPE(0.9)-are used in this study.…”

Section: Alternative Model Combination Schemesmentioning

confidence: 99%

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Out‐of‐sample volatility prediction: A new mixed‐frequency approach

Zhang

Wang

et al. 2019

Journal of Forecasting

Self Cite

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This paper proposes a new mixed‐frequency approach to predict stock return volatilities out‐of‐sample. Based on the strategy of momentum of predictability (MoP), our mixed‐frequency approach has a model switching mechanism that switches between generalized autoregressive conditional heteroskedasticity (GARCH)‐class models that only use low‐frequency data and heterogeneous autoregressive models of realized volatility (HAR‐RV)‐type that only use high‐frequency data. The MoP model simply selects a forecast with relatively good past performance between the GARCH‐class and HAR‐RV‐type forecasts. The model confidence set (MCS) test shows that our MoP strategy significantly outperforms the competing models, which is robust to various settings. The MoP test shows that a relatively good recent past forecasting performance of the GARCH‐class or HAR‐RV‐type model is significantly associated with a relatively good current performance, supporting the success of the MoP model.

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“…From the perspective of the input of the forecasting task, it can be divided into two groups: multivariate forecasting and univariate forecasting. The former usually feeds the data associated with types of variables, such as macroeconomic variables, exchange rates, sentiment analysis, inventory variables, previous crude oil prices, and so on, to the predictors [1][2][3][4][5][6][7], while the latter uses the previous prices only [8][9][10][11][12]. These are two different perspectives for studying crude oil price forecasting.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors

Zhou

et al. 2019

Energies

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As one of the leading types of energy, crude oil plays a crucial role in the global economy. Understanding the movement of crude oil prices is very attractive for producers, consumers and even researchers. However, due to its complex features of nonlinearity and nonstationarity, it is a very challenging task to accurately forecasting crude oil prices. Inspired by the well-known framework “decomposition and ensemble” in signal processing and/or time series forecasting, we propose a new approach that integrates the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), differential evolution (DE) and several types of ridge regression (RR), namely, ICEEMDAN-DE-RR, for more accurate crude oil price forecasting in this paper. The proposed approach consists of three steps. First, we use the ICEEMDAN to decompose the complex daily crude oil price series into several relatively simple components. Second, ridge regression or kernel ridge regression is employed to forecast each decomposed component. To enhance the accuracy of ridge regression, DE is used to jointly optimize the regularization item, the weights and parameters of each single kernel for each component. Finally, the predicted results of all components are aggregated as the final predicted results. The publicly available West Texas Intermediate (WTI) daily crude oil spot prices are used to validate the performance of the proposed approach. The experimental results indicate that the proposed approach can achieve better performance than some state-of-the-art approaches in terms of several evaluation criteria, demonstrating that the proposed ICEEMDAN-DE-RR is very promising for daily crude oil price forecasting.

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Forecasting the prices of crude oil: An iterated combination approach

Cited by 138 publications

References 41 publications

S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

Out‐of‐sample volatility prediction: A new mixed‐frequency approach

Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors

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