Multi‐model Forecasts of the West Texas Intermediate Crude Oil Spot Price

Ryan, Laura; Whiting, Bronwen

doi:10.1002/for.2440

Cited by 7 publications

(7 citation statements)

References 40 publications

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“…Furthermore, using a quadratic error measure leads to a penalization of comparatively large forecast errors. The RMSE given in Formula below is one very commonly applied error measure (see, e.g., Andres & Spiwoks, ; Espinoza, Fornari, & Lombardi, ; Hyndman & Koehler, ; Kisinbay, ; Leitch & Tanner, ; and more recently Herwartz & Schlüter, ; Ryan & Whiting, ; as well as Wegener, Spreckelsen, Basse, & Mettenheim, ).

\begin{matrix} lefttrue \\ {italicRMSE}_{Brent, h}^{italicj} = \sqrt{\frac{1}{N} {false\sum}_{i = 1}^{N} {((), p_{italicBrent, i, h} - {\hat{p}}_{italicBrent, i, h}^{j})}^{2}} \\ and \\ {italicRMSE}_{WTI, h}^{italick} = \sqrt{\frac{1}{N} {false\sum}_{i = 1}^{N} {((), p_{italicWTI, i, h} - {\hat{p}}_{italicWTI, i, h}^{k})}^{2}} . \end{matrix}

…”

Section: Methodologies Of Forecast Evaluationmentioning

confidence: 99%

The usefulness of oil price forecasts—Evidence from survey predictions

et al. 2018

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JEL Classification: D81; F37; F47This paper evaluates survey forecasts for crude oil prices and discusses the implications for decision makers. A novel disaggregated data set incorporating individual forecasts for Brent and Western Texas Intermediate is used. We carry out tests for unbiasedness, sign accuracy, and forecast encompassing, followed by the computation of coefficients for topically oriented trend adjustments and the Theil's U measure. We also control for the forecast horizon finding heterogeneous results. Forecasts are more precise for shorter horizons, but less accurate than the naïve prediction. For longer horizons, topically oriented trend adjustments become more pronounced, but forecasters tend to outperform the naïve predictions.

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\begin{matrix} lefttrue \\ {italicRMSE}_{Brent, h}^{italicj} = \sqrt{\frac{1}{N} {false\sum}_{i = 1}^{N} {((), p_{italicBrent, i, h} - {\hat{p}}_{italicBrent, i, h}^{j})}^{2}} \\ and \\ {italicRMSE}_{WTI, h}^{italick} = \sqrt{\frac{1}{N} {false\sum}_{i = 1}^{N} {((), p_{italicWTI, i, h} - {\hat{p}}_{italicWTI, i, h}^{k})}^{2}} . \end{matrix}

…”

Section: Methodologies Of Forecast Evaluationmentioning

confidence: 99%

The usefulness of oil price forecasts—Evidence from survey predictions

et al. 2018

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“…Also, they can be stored to be sold at higher prices in the future. This is called speculative inventory [119][120][121][122].…”

Section: Inventoriesmentioning

confidence: 99%

Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R

Drachal

2020

Signals

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The described R package allows to estimate Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model. The original methods, and additionally, some selected modifications of these methods are implemented. For example the user can choose between recursive moment estimation and exponentially moving average for variance updating in the base DMA. Moreover, inclusion probabilities can be computed in a way using “Google Trends” data. The code is written with respect to minimise the computational burden, which is quite an obstacle for DMA algorithm if numerous variables are used. For example, this package allows for parallel computations and implementation of the Occam’s window approach. However, clarity and readability of the code, and possibility for an R-familiar user to make his or her own small modifications in reasonably small time and with low effort are also taken under consideration. Except that, some alternative (benchmark) forecasts can also be quickly performed within this package. Indeed, this package is designed in a way that is hoped to be especially useful for practitioners and researchers in economics and finance.

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“…Ye et al presented a model for forecasting short-term oil prices using only lagged OECD petroleum inventory levels as explanatory variables, providing a wellfitted forecast both in-sample and out-of-sample. Ryan and Whiting (2016) found that the OECD inventory level lagged by two months had a significant relationship with oil prices. Inventory levels are generally accepted as an important fundamental factor when explaining oil price dynamics (Hamilton 2008;He et al 2010;Kilian and Murphy 2014;Merino and Ortiz 2005).…”

Section: Supply Factorsmentioning

confidence: 99%

“…For instance, Sarwar (2012) found a strong negative correlation between changes in VIX and US stock market returns. Ryan and Whiting (2016) found that the VIX, lagged with one month, was the most important indicator in terms of forecasting oil prices. A large amount of literature has examined the relationship between oil prices and the stock market (Bernanke 2016;Hammoudeh et al 2004;Kang et al 2015;Sadorsky 1999), generally explaining a significant positive correlation between the two.…”

Section: Financial Factorsmentioning

confidence: 99%

Modeling superior predictors for crude oil prices

Westgaard¹,

Osmundsen²,

Stenslet³

et al. 2017

JEM

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A common perception in the literature is that oil price dynamics are most adequately explained by fundamental supply-and-demand factors. We use a general-to-specific approach and find that financial indicators are even more significant at modeling and predicting oil prices. We demonstrate empirically that the futures spreads level, highyield bond spreads and PHLX Oil Service Sector (OSX) index are the best predictors of oil prices in the period February 2000-June 2013. (The OSX index is designed to track the performance of a set of companies involved in the oil services sector.) The OSX index is particularly interesting, as no study has analyzed its predictive power prior to our analysis. The relationship is intuitively meaningful, as stock prices, which strongly depend on the oil price, are determined in a market with well-informed investors that have strong incentives to gather correct market information. Moreover, the share prices serve as strong proxies or price signals, as they reflect future oil price expectations at any point of time. Furthermore, we demonstrate through an out-ofsample analysis that our most parsimonious model is superior to relevant benchmarks at forecasting oil price changes (two benchmarks were used: (1) a random walk

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Multi‐model Forecasts of the West Texas Intermediate Crude Oil Spot Price

Cited by 7 publications

References 40 publications

The usefulness of oil price forecasts—Evidence from survey predictions

The usefulness of oil price forecasts—Evidence from survey predictions

Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R

Modeling superior predictors for crude oil prices

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