El Niño and forecastability of oil-price realized volatility

Finance Research Letters

2022

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We examine the predictive value of tail risks of oil returns for the realized variance of oil returns using monthly data for the modern oil industry (1859:10-2020:10). The Conditional Autoregressive Value at Risk (CAViaR) framework is employed to generate the tail risks for both 1% and 5% VaRs across four variants of the CAViaR framework. We find evidence of both in-sample and out-of-sample predictability emanating from both 1% and 5% tail risks. Given the importance of real-time oil-price volatility forecasts, our results have important implications for investors and policymakers.

Section: Introductionmentioning

confidence: 99%

Oil tail risks and the forecastability of the realized variance of oil-price: Evidence from over 150 years of data

Salisu

Finance Research Letters

2022

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“…In this regard, it should be noted that the better performance of the Lasso model over forecast-combination methods in forecasting oil-market volatility has been demonstrated by Liang et al [6] and, hence, motivates us to rely on this framework as well. Our paper, thus, adds to the already existing large literature on the forecastability of oil-returns volatility by considering the role of the uncertainties of major economies in the world and the associated spillover, where the literature can be grouped into the following broad categories, using a wide variety of models and macroeconomic, financial, behavioral, and climate pattern-related predictors (see, for example, Lux et al [20]), Bonato et al [21], Demirer et al [22,23], Gkillas et al [24], Bouri et al [25]; Salisu et al [26], and the references cited within these papers).…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Volatility of Crude Oil: The Role of Uncertainty and Spillovers

2021

Energies

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We use a dataset for the group of G7 countries and China to study the out-of-sample predictive value of uncertainty and its international spillovers for the realized variance of crude oil (West Texas Intermediate and Brent) over the sample period from 1996Q1 to 2020Q4. Using the Lasso estimator, we found evidence that uncertainty and international spillovers had predictive value for the realized variance at intermediate (two quarters) and long (one year) forecasting horizons in several of the forecasting models that we studied. This result holds also for upside (good) and downside (bad) variance, and irrespective of whether we used a recursive or a rolling estimation window. Our results have important implications for investors and policymakers.

Section: Introductionmentioning

confidence: 99%

“…At this stage, it is important to indicate that two somewhat related papers are the works of [36,37], which forecasted monthly RVs of heating oil and crude oil prices, respectively, based on the information content of the El Niño Southern Oscillation (ENSO) phases, using a HAR-RV framework. Note that, ref [37] extended the work of [38], which provided in-sample evidence of the role of the ENSO in causing oil returns and volatility based on a nonparametric k-th order quantile causality test. With our paper providing forecasts at the daily frequency of the RVs of not only crude oil and heating oil prices, but also natural gas prices, it can be considered as an improvement over these two papers, given the importance of high-frequency forecasts for investors and policymakers in making their respective decisions.…”

Section: Introductionmentioning

confidence: 99%

Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment

2021

Energies

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We extend the widely-studied Heterogeneous Autoregressive Realized Volatility (HAR-RV) model to examine the out-of-sample forecasting value of climate-risk factors for the realized volatility of movements of the prices of crude oil, heating oil, and natural gas. The climate-risk factors have been constructed in recent literature using techniques of computational linguistics, and consist of daily proxies of physical (natural disasters and global warming) and transition (U.S. climate policy and international summits) risks involving the climate. We find that climate-risk factors contribute to out-of-sample forecasting performance mainly at a monthly and, in some cases, also at a weekly forecast horizon. We demonstrate that our main finding is robust to various modifications of our forecasting experiment, and to using three different popular shrinkage estimators to estimate the extended HAR-RV model. We also study longer forecast horizons of up to three months, and we account for the possibility that policymakers and forecasters may have an asymmetric loss function.