Effect of rare disaster risks on crude oil: evidence from El Niño from over 145 years of data

Demirer, Rıza; Gupta, Rangan; Nel, Jacobus; Pierdzioch, Christian

doi:10.1007/s00704-021-03856-x

Cited by 23 publications

(7 citation statements)

References 60 publications

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“…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: 80%

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

Gupta

Pierdzioch

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.

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Section: Introductionmentioning

confidence: 80%

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

Gupta

Pierdzioch

2021

Energies

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“…In fact, in earlier studies on climate risks and stock markets, researchers primarily have concentrated on developed countries and on in-sample movements of the first moment [13,15,27,29,30], with the only exception being [14], who have analyzed stock market volatility of the state-level data in the United States (US). When it comes to volatility, the literature thus far has concentrated on predicting second moments of commodity returns due to climate risks (for example, [58][59][60][61][62][63]). Another somewhat related paper is that of [64], who have forecasted indicators of financial stress, comprised of both first and second moments of the underlying assets, of developed countries.…”

Section: Brief Discussion Of Stock Return Volatility Literature Of So...mentioning

confidence: 99%

Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa

Wu,

Karmakar,

Gupta

et al. 2024

Climate

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Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock return volatility. To this end, we do not apply only the classical GARCH and GARCHX models, but rather we apply newly proposed model-free prediction methods, and use GARCH-NoVaS and GARCHX-NoVaS models to compute volatility predictions. These two models are based on a normalizing and variance-stabilizing transformation (NoVaS transformation) and are guided by a so-called model-free prediction principle. Applying the new models to data for South Africa, we find that climate-related information is helpful in forecasting stock return volatility. Moreover, the novel model-free prediction method can incorporate such exogenous information better than the classical GARCH approach, as revealed by the the squared prediction errors. More importantly, the forecast comparison test reveals that the advantage of applying exogenous information related to climate risks in prediction of the South African stock return volatility is significant over a century of monthly data (February 1910–February 2023). Our findings have important implications for academics, investors, and policymakers.

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“…They showed that the benefits of PAW and SOI tactical adaptation could be useful for farmers to adjust farm management practices according to the season, but it may be improved further with new forecasting climate methods. On the other hand, some studies recently showed that the ENSO also affects the volatility of the oil price (Hu and Fedorov, 2019;Demirer et al, 2020;Bouri et al, 2021), since disaster risks contribute to jump risk in oil prices (Demirer et al, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

A Persistent Cyclical Pattern in the Soi and Eqsoi Series

Gil-Alana

Romero-Rojo

2022

Preprint

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This paper deals with the analysis of the SOI and EQSOI series and their statistical properties, focussing in particular in their cyclical pattern and its persistence across time. For this purpose, we use a long memory model that is characterized because the spectral density function is unbounded at a frequency away from zero. Our results indicate that the two series display a long memory pattern, with the order of integration in the interval (0, 0.5) and the length of the cycles ranging between three and four years.JEL Classification: C00; C02; C22; Q54;

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Effect of rare disaster risks on crude oil: evidence from El Niño from over 145 years of data

Cited by 23 publications

References 60 publications

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

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

Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa

A Persistent Cyclical Pattern in the Soi and Eqsoi Series

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