Forecasting oil price realized volatility using information channels from other asset classes

Degiannakis, Stavros; Filis, George

doi:10.1016/j.jimonfin.2017.05.006

Cited by 221 publications

(119 citation statements)

References 91 publications

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“…Despite the ability of these models to show that the relationship between stock market and oil price is Cor(y t,stock , y t,oil ) and Cor(e t,stock , e t,oil ) differ, as they represent different information. Degiannakis et al (2013) and Degiannakis and Filis (2017) report evidence and support the estimation of model framework with mean equation that includes only the constant term.…”

Section: Econometric Methods and Data Usedmentioning

confidence: 71%

Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence

Degiannakis¹,

Filis²,

Arora³

2018

The Energy Journal

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160

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Do oil prices and stock markets move in tandem or in opposite directions? The complex and time varying relationship between oil prices and stock markets has caught the attention of the financial press, investors, policymakers, researchers, and the general public in recent years. In light of such attention, this paper reviews research on the oil price and stock market relationship. The majority of papers we survey study the impacts of oil markets on stock markets, whereas, little research in the reverse direction exists. Our review finds that the causal effects between oil and stock markets depend heavily on whether research is performed using aggregate stock market indices, sectorial indices, or firm-level data and whether stock markets operate in net oil-importing or net oil-exporting countries. Additionally, conclusions vary depending on whether studies use symmetric or asymmetric changes in the price of oil, or whether they focus on unexpected changes in oil prices. Finally, we find that most studies show oil price volatility transmits to stock market volatility, and that including measures of stock market performance improves forecasts of oil prices and oil price volatility. Several important avenues for further research are identified.

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Section: Econometric Methods and Data Usedmentioning

confidence: 71%

Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence

Degiannakis¹,

Filis²,

Arora³

2018

The Energy Journal

Self Cite

160

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“…Hence, we maintain that this is an appropriate framework for modeling and forecasting economic uncertainty. Degiannakis and Filis () further proposed the HAR‐X model incorporating information from exogenous assets. In our case, the HAR‐X model for the EPU t is employed for monthly data in the form:

\begin{matrix} log (E P U_{t}) = & w_{0} + w_{1} log false(E P U_{t - 1} false) + w_{2} false(3^{- 1} \sum_{k = 1}^{3} log (E P U_{t - k}) false) \\ + w_{3} false(12^{- 1} \sum_{k = 1}^{12} log (E P U_{t - k}) false) + w_{4} log (R V_{x, t - 1}^{(M)}) \\ + w_{5} (3^{- 1} \sum_{k = 1}^{3} log ()(, R V_{x, t - k}^{false(M false)})) \\ + w_{6} (12^{- 1} \sum_{k = 1}^{12} log ()(, R V_{x, t - k}^{false(M false)})) + ε_{t}, \end{matrix}

where ɛ t is white noise and

R V_{x, t - k}^{()(, M)}

denotes the monthly realized volatility of the exogenous asset for t − k month.…”

Section: Forecasting Modelsmentioning

confidence: 99%

Forecasting European economic policy uncertainty

Degiannakis

Filis

2019

Scottish J Political Eco

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Forecasting the economic policy uncertainty in Europe is of paramount importance given the ongoing sovereign debt crisis. This paper evaluates monthly economic policy uncertainty index forecasts and examines whether ultra-high frequency information from asset market volatilities and global economic uncertainty can improve the forecasts relatively to the no-change forecast. The results show that the global economic policy uncertainty provides the highest predictive gains, followed by the European and US stock market realized volatilities. In addition, the European stock market implied volatility index is shown to be an important predictor of the economic policy uncertainty.

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“…Against this backdrop, the objective of this paper is to forecast the daily co-volatility of gold and oil futures derived from 1-min intraday data over the period 27 September 2009 to 25 May 2017 (Although the variability of daily gold and oil price returns have traditionally been forecasted based on Generalized Autoregressive Conditional Heteroskedasticity (GARCH)-type models of volatility, recent empirical evidence suggests that the rich information contained in intraday data can produce more accurate estimates and forecasts of daily volatility (see Reference [20] for a detailed discussion)). In particular, realizing the importance of jumps, that is, discontinuities, in governing the volatility of asset prices [21][22][23], we investigate the impact of jumps by simultaneously accommodating leverage effects in forecasting the co-volatility of gold and oil markets, following the econometric approach of [24] (applied to three stocks traded on the New York Stock Exchange (NYSE)).…”

Section: Introductionmentioning

confidence: 99%

The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures

2019

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This paper investigates the impact of jumps in forecasting co-volatility in the presence of leverage effects for daily crude oil and gold futures. We use a modified version of the jump-robust covariance estimator of Koike (2016), such that the estimated matrix is positive definite. Using this approach, we can disentangle the estimates of the integrated co-volatility matrix and jump variations from the quadratic covariation matrix. Empirical results show that more than 80% of the co-volatility of the two futures contains jump variations and that they have significant impacts on future co-volatility but that the impact is negligible in forecasting weekly and monthly horizons.

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Forecasting oil price realized volatility using information channels from other asset classes

Cited by 221 publications

References 91 publications

Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence

Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence

Forecasting European economic policy uncertainty

The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures

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