Oil and the short-term predictability of stock return volatility

Wang, Yudong; Wei, Yu; Wu, Chongfeng; Yin, Libo

doi:10.1016/j.jempfin.2018.03.002

Cited by 182 publications

(118 citation statements)

References 51 publications

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“…The majority of the equity market volatility prediction studies has employed another popular evaluation method-that is, out-of-sample R 2 (R 2 OOS ), to assess the predictive quality (see, e.g., Campbell & Thompson, 2008;Rapach, Strauss, & Zhou, 2010;Wang, Wei, Wu, & Yin, 2018). R 2 OOS denotes the percentage reduction in the mean squared forecast error (MSFE) of the RV forecast relative to the MSFE of the benchmark, and it is expressed as…”

Section: Alternative Evaluation Methodsmentioning

confidence: 99%

Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach

Liang

et al. 2020

Journal of Forecasting

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The primary purpose of this paper is to investigate whether a novel Markov regime-switching mixed-data sampling (MRS-MIADS) model we design can improve the prediction accuracy of the realized variance (RV) of Bitcoin. Moreover, to verify whether the importance of jumps for RV forecasting changes over time, we extend the standard MIDAS model to characterize two volatility regimes and introduce a jump-driven time-varying transition probability between the two regimes. Our results suggest that the proposed novel MRS-MIDAS model exhibits statistically significant improvement for forecasting the RV of Bitcoin. In addition, we find that jump occurrences significantly increase the persistence of the high-volatility regime and switch between high-and low-volatility regimes. A wide range of checks confirm the robustness of our results. Finally, the proposed model shows significant improvement for 2-week and 1-month horizon forecasts.

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Section: Alternative Evaluation Methodsmentioning

confidence: 99%

Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach

Liang

et al. 2020

Journal of Forecasting

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“…One further key argument that has recently been given much attention is related to different sources of financial volatility and major events that have affected stock market returns ( Zhu et al, 2019 , Corbet et al, 2020 , Zaremba et al, 2020 , Albulescu, 2020 , Onali, 2020 , Choudhry et al, 2016 , Demirer et al, 2019 , Wang et al, 2018 , Antonakakis and Darby, 2013 ). These sources of financial volatility are market uncertainty due to disasters ( Kowalewski and Śpiewanowski, 2020 , Liu et al, 2019 ; Papadamou et al, 2020 ), economic conditions and political events ( Bash and Alsaifi, 2019 ), institutional issues and social Media news ( Bollerslev et al, 2018 , Reboredo and Ugolini, 2018 ), environmental issues ( Alsaifi et al, 2020 ), information demand and investor attention ( Chronopoulos et al, 2018 , Nikkinen and Peltomäki, 2020 ), and pandemic diseases ( Chen et al, 2009 , Ichev and Marinč, 2018 ).…”

Section: Literature Reviewmentioning

confidence: 99%

The impact of Coronavirus (COVID-19) outbreak on faith-based investments: An original analysis

Sherif

2020

Journal of Behavioral and Experimental Finance

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This paper examines the rapid spread of Coronavirus (COVID-19) and its short-term impact on the Shariah-compliant UK Dow Jones market index to capture the dynamic behavior of stock returns at economy and industry levels. Using daily data over the period January 20 to May 20 and ten UK industrial sector groupings, the findings suggest a strong and statistically significant relationship between the COVID-19 pandemic and the performance of the conventional stock market index. The findings also suggest that the disease interacts negatively but insignificantly with the Dow Jones faith-based ethical (Islamic) index compared to its UK counterpart. In addition, through an analysis of sector groupings, the paper shows that the stock returns of the information technology sector performed significantly better than the market, while stock returns of consumer discretionary sector, which includes transportation, beverages, tourism and leisure, consumer services performed significantly worse than the market during the COVID-19 outbreak. Other sector groupings fail to yield significantly plausible parameter values.

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“…2 The JLN macroeconomic uncertainty factor produces statistically significant forecasts when forecasting the volatility in agricultural, energy and metals commodity markets, with the forecasting horizon ranging from one to 1 Our paper also contributes to the empirical works that study the economic drivers of financial volatility. Starting with Schwert (1989), a large body of literature explores the various financial and macroeconomic predictors of volatility in financial markets (e.g., Christiansen et al, 2012;Feng et al, 2017;Paye, 2012;Wang et al, 2018). The previous literature mainly focuses on either the stock market volatility or the volatility of energy commodities.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…In this section we provide forecasting comparison tests for our in-sample and out-ofsample forecasting regressions by comparing the predictive power of our models with the respective power of autoregressive (AR) models which have been extensively used as benchmarks for volatility forecasting in financial markets (Christiansen et al, 2012;Feng et al, 2017;Paye, 2012;Scwhert, 1989;Wang et al, 2018;among others). 12 Since volatility in commodity markets is highly persistent, it is of crucial…”

Section: Forecasting Comparisonsmentioning

confidence: 99%

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Volatility forecasting in commodity markets using macro uncertainty

Bakas

Triantafyllou

2019

Energy Economics

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In this paper, we empirically examine the predictive power of macroeconomic uncertainty on the volatility of agricultural, energy and metals commodity markets. We find that the latent macroeconomic uncertainty measure of Jurado et al. (2015) is a common volatility forecasting factor for commodity markets, which provides statistically significant volatility predictions for forecasting horizons up to twelve months ahead. The results indicate that the forecasting power of macroeconomic uncertainty is higher when predicting the volatility of energy commodities. Our findings also show that higher macroeconomic uncertainty is associated with large volatility episodes subsequently observed in all commodity markets. The predictive power of the unobservable macroeconomic uncertainty factor remains robust to the inclusion of observable economic uncertainty measures, historical commodity price volatility, stock-market realized and news implied volatility, oil price shocks and other macroeconomic variables which are closely related to the production process and the mechanics of commodity markets.

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Oil and the short-term predictability of stock return volatility

Cited by 182 publications

References 51 publications

Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach

Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach

The impact of Coronavirus (COVID-19) outbreak on faith-based investments: An original analysis

Volatility forecasting in commodity markets using macro uncertainty

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