Time-varying persistence in real oil prices and its determinant

Kruse, Robinson; Wegener, Christoph

doi:10.1016/j.eneco.2019.02.020

Cited by 33 publications

(20 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some other research focused on a multivariate analysis of crude oil prices. Kruse and Wegener used model averaging to analyze time-varying persistence in real oil prices from more than one hundred and fifty variables and found that the index of global economic activity by Kilian [15] was the only significant measure to explain time-varying oil price persistence [16]. However, all the statistical and econometric models have the limitation that they are built on the assumption that the time series of crude oil prices has the characteristics of linearity and stationarity.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning

Jia

et al. 2018

Energies

View full text Add to dashboard Cite

Crude oil is one of the most important types of energy and its prices have a great impact on the global economy. Therefore, forecasting crude oil prices accurately is an essential task for investors, governments, enterprises and even researchers. However, due to the extreme nonlinearity and nonstationarity of crude oil prices, it is a challenging task for the traditional methodologies of time series forecasting to handle it. To address this issue, in this paper, we propose a novel approach that incorporates ensemble empirical mode decomposition (EEMD), sparse Bayesian learning (SBL), and addition, namely EEMD-SBL-ADD, for forecasting crude oil prices, following the “decomposition and ensemble” framework that is widely used in time series analysis. Specifically, EEMD is first used to decompose the raw crude oil price data into components, including several intrinsic mode functions (IMFs) and one residue. Then, we apply SBL to build an individual forecasting model for each component. Finally, the individual forecasting results are aggregated as the final forecasting price by simple addition. To validate the performance of the proposed EEMD-SBL-ADD, we use the publicly-available West Texas Intermediate (WTI) and Brent crude oil spot prices as experimental data. The experimental results demonstrate that the EEMD-SBL-ADD outperforms some state-of-the-art forecasting methodologies in terms of several evaluation criteria such as the mean absolute percent error (MAPE), the root mean squared error (RMSE), the directional statistic (Dstat), the Diebold–Mariano (DM) test, the model confidence set (MCS) test and running time, indicating that the proposed EEMD-SBL-ADD is promising for forecasting crude oil prices.

show abstract

Section: Introductionmentioning

confidence: 99%

Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning

Jia

et al. 2018

Energies

View full text Add to dashboard Cite

show abstract

“…These three specifications differ in the choice of whether the real oil price and other regressors are differenced prior to estimation, and on whether we impose the prior that the real price of oil is mean reverting. In particular, we consider a BVAR in "levels" (LBVAR, for y, p, i and q), one expressed as a mixed model of variables expressed in "levels" and "differences" (DBVAR, for ∆ y, ∆ p,ĩ and ∆ q) and one where we exploit the methodology of Villani (MBVAR, for ∆ y, ∆ p, i and q) to elicit the prior that the real price of oil is mean reverting, as would suggest the work by Dvir and Rogoff (2009); Kruse and Wegener (2019). In all cases we use the specification with four lags as the models are fitted to the data of quarterly frequency.…”

Section: Bvar Counterparts Of the Dsge Modelmentioning

confidence: 99%

“…Two of them are standard, with the difference that one is estimated on variables in levels, whereas the second one after their differentiation. The third one exploits the methodology of Villani (2009) to elicit the prior that the real price of oil reverts to its recursive mean, as would suggest the discussion by Dvir and Rogoff (2009) and Kruse and Wegener (2019). The next two models are random walks, with and without drift, which are the most popular benchmarks in real oil price forecasting competitions.…”

Section: Introductionmentioning

confidence: 99%

Forecasting crude oil prices with DSGE models

Rubaszek

2021

International Journal of Forecasting

View full text Add to dashboard Cite

We run an oil prices forecasting competition among a set of structural models, including vector autoregressions and dynamic stochastic general equilibrium models. Our results highlights two principles. First, forecasts should exploit the mean reversion of the real oil price over long horizons. Second, models should not replicate the high volatility of oil prices observed in sample. Abiding by these principles, we show that a small scale DSGE model performs much better in real oil price forecasting than the random walk as well as vector autoregressions.

show abstract

“…Behavioral economics found people to be risk-averse and their loss prospects loom larger than gains, which may influence their inflated willing-to-pay in light of loss prospects surrounding scarcity of goods (Kahneman & Tversky, 1979). How scarcity prospects reflect on energy prices, is a currently growing research field in finance with applications in the natural gas market and oil sector (Kilian & Zhou, 2018;Klein, von Mettenheim, Walther & Wegener, 2018;Kruse & Wegener, 2017;Legnazzi, 2018;Mahadeo, Heinleinb & Legrenzib, 2018;Markowitz, Grasso & Jamieson, 2015).…”

Section: Graph 4: the Collective Soul Of Booms And Bustsmentioning

confidence: 99%

An inquiry into the nature and causes of Climate Wealth of Nations: What temperature finance gravitates towards? Sketching a climate-finance nexus and outlook on climate change-induced finance prospects

Puaschunder

2019

ABR

View full text Add to dashboard Cite

Puaschunder, J. M. (2019). An inquiry into the nature and causes of Climate Wealth of Nations: What temperature finance gravitates towards? Sketching a climate-finance nexus and outlook on climate change-induced finance prospects. ABSTRACTWhat is the optimal temperature finance gravitates towards? While we have empirical evidence for the optimal cardinal temperature for GDP production and international development links external climate conditions to levels of societal development, no literature exists on climate-induced-finance flows.Research on temperaturedependent financial flows holds invaluable insights in light of climate change. The connection between climate change and finance will be drawn via price mechanisms. Given the extinction potential of crops, industry and service production, price mechanism will be prospected with a hyperbolic tilt towards the end of durability and the closeness to extinction. In contrast to classical and standard approaches in economics to determine prices, the following research thereby takes into account that agents in their behavior can be constrained by shrinking timeframes for production in light of global warming. The contemporary attention to global warming and climate shocks is thereby assumed to affect the price expectations and hence actual market prices of commodities. Paying attention to supply and demand side perspectives, Puaschunder, J. M. (2019). An inquiry into the nature and causes of Climate Wealth of Nations: What temperature finance gravitates towards? Sketching a climate-finance nexus and outlook on climate change-induced finance prospects. inflated prices surrounding scarcity will be first modelled and then back-tested on data about prices in commodities of food and beverages. Future wealth of nations will be introduced by the concept of climate flexibility defined as the range of temperature variation of a country. So far, a broad spectrum of climate zones has not been defined as asset but climate change will require territories being more flexible in terms of changing economic production. The more climate variation a nation state possesses, this paper argues, the more degrees of freedom a country has in terms of GDP production capabilities in a differing climate. These preliminary insights aid in answering what financial patterns can we expect given predictions the earth will become hotter. Already now this paper presents human capital flows and financial market inflows into areas that are winning economically from a warming globe. The degree of climate flexibility is found to be related to human migration inflow. Puaschunder, J. M. (2019). An inquiry into the nature and causes of Climate Wealth of Nations: What temperature finance gravitates towards? Sketching a climate-finance nexus and outlook on climate change-induced finance prospects.

show abstract

Time-varying persistence in real oil prices and its determinant

Cited by 33 publications

References 49 publications

Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning

Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning

Forecasting crude oil prices with DSGE models

An inquiry into the nature and causes of Climate Wealth of Nations: What temperature finance gravitates towards? Sketching a climate-finance nexus and outlook on climate change-induced finance prospects

Contact Info

Product

Resources

About