# Investor Happiness and Predictability of the Realized Volatility of Oil Price

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methods

## 3. Data

## 4. Empirical Results

## 5. Concluding Remarks

## Author Contributions

## Acknowledgments

## Conflicts of Interest

## References

- Bahloul, W.; Balcilar, M.; Cunado, J.; Gupta, R. The role of economic and financial uncertainties in predicting commodity futures returns and volatility: Evidence from a nonparametric causality-in-quantiles test. J. Multinatl. Financ. Manag.
**2018**, 45, 52–71. [Google Scholar] [CrossRef] - Bonato, M. Realized correlations, betas and volatility spillover in the agricultural commodity market: What has changed? J. Int. Financ. Mark. Inst. Money
**2019**, 62, 184–202. [Google Scholar] [CrossRef] - Asai, M.; Gupta, R.; McAleer, M. Forecasting Volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks. Int. J. Forecast.
**2020**. [Google Scholar] [CrossRef][Green Version] - Asai, M.; Gupta, R.; McAleer, M. The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures. Energies
**2019**, 12, 3379. [Google Scholar] [CrossRef][Green Version] - Demirer, R.; Gupta, R.; Suleman, T.; Wohar, M.E. Time-varying rare disaster risks, oil returns and volatility. Energy Econ.
**2018**, 75, 239–248. [Google Scholar] [CrossRef][Green Version] - Elder, J.; Serletis, A. Oil price uncertainty. J. Money Credit Bank.
**2010**, 42, 1137–1159. [Google Scholar] [CrossRef] - Van Eyden, R.; Difeto, M.; Gupta, R.; Wohar, M.E. Oil price volatility and economic growth: Evidence from advanced OECD countries using over one century of data. Appl. Energy
**2019**, 233, 612–621. [Google Scholar] [CrossRef][Green Version] - Henriques, I.; Sadorsky, P. Can environmental sustainability be used to manage energy price risk? Energy Econ.
**2010**, 32, 1131–1138. [Google Scholar] [CrossRef] - Jiang, Y.; Ma, C.Q.; Yang, X.G.; Ren, Y.S. Time-Varying Volatility Feedback of Energy Prices: Evidence from Crude Oil, Petroleum Products, and Natural Gas Using a TVP-SVM Model. Sustainability
**2018**, 10, 4705. [Google Scholar] [CrossRef][Green Version] - Zhao, L.T.; Liu, L.N.; Wang, Z.J.; He, L.Y. Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach. Sustainability
**2019**, 11, 3892. [Google Scholar] [CrossRef][Green Version] - Gkillas, K.; Gupta, R.; Wohar, M.E. Oil shocks and volatility jumps. Rev. Quant. Financ. Account.
**2020**, 54, 247–272. [Google Scholar] [CrossRef][Green Version] - Lux, T.; Segnon, M.; Gupta, R. Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data. Energy Econ.
**2016**, 56, 117–133. [Google Scholar] [CrossRef][Green Version] - McAleer, M.; Medeiros, M.C. Realized volatility: A review. Econom. Rev.
**2008**, 27, 10–45. [Google Scholar] [CrossRef] - Haugom, E.; Langeland, H.; Molnár, P.; Westgaard, S. Forecasting volatility of the US oil market. J. Bank. Financ.
**2014**, 47, 1–14. [Google Scholar] [CrossRef][Green Version] - Sévi, B. Forecasting the volatility of crude oil futures using intraday data. Eur. J. Oper. Res.
**2014**, 235, 643–659. [Google Scholar] [CrossRef] - Prokopczuk, M.; Symeonidis, L.; Wese Simen, C. Do jumps matter for volatility forecasting? Evidence from energy markets. J. Futur. Mark.
**2015**, 36, 758–792. [Google Scholar] [CrossRef] - Degiannakis, S.; Filis, G. Forecasting oil price realized volatility using information channels from other asset classes. J. Int. Money Financ.
**2017**, 76, 28–49. [Google Scholar] [CrossRef][Green Version] - Liu, J.; Ma, F.; Yang, K.; Zhang, Y. Forecasting the oil futures price volatility: Large jumps and small jumps. Energy Econ.
**2018**, 72, 321–330. [Google Scholar] [CrossRef] - Chen, Y.; Ma, F.; Zhang, Y. Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets. Energy Econ.
**2019**, 81, 52–62. [Google Scholar] [CrossRef] - Gkillas, K.; Gupta, R.; Pierdzioch, C. Forecasting realized oil-price volatility: The Role of financial stress and asymmetric loss. J. Int. Money Financ.
**2020**, 104, 102137. [Google Scholar] [CrossRef] - Corsi, F. A simple approximate long-memory model of realized volatility. J. Financ. Econ.
**2009**, 7, 174–196. [Google Scholar] [CrossRef] - Andersen, T.G.; Bollerslev, T. Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. Int. Econ. Rev.
**1998**, 39, 885–905. [Google Scholar] [CrossRef] - Phan, D.H.B.; Sharma, S.S.; Narayan, P.K. Intraday volatility interaction between the crude oil and equity markets. J. Int. Financ. Mark. Inst. Money
**2016**, 40, 1–13. [Google Scholar] [CrossRef] - Chatrath, A.; Miao, H.; Ramchander, S.; Wang, T. The forecasting efficacy of risk-neutral moments for crude oil volatility. J. Forecast.
**2015**, 34, 177–190. [Google Scholar] [CrossRef] - Zhang, W.; Li, X.; Shen, D.; Teglio, A. Daily happiness and stock returns: Some international evidence. Phys. A
**2016**, 460, 201–209. [Google Scholar] [CrossRef] - Zhang, W.; Wang, P.; Li, X.; Shen, D. Twitter’s daily happiness sentiment and international stock returns: Evidence from linear and nonlinear causality tests. J. Behav. Exp. Financ.
**2018**, 18, 50–53. [Google Scholar] [CrossRef] - You, W.; Guo, Y.; Cheng, P. Twitter’s daily happiness sentiment and the predictability of stock returns. Financ. Res. Lett.
**2017**, 23, 58–64. [Google Scholar] [CrossRef] - Reboredo, J.C.; Ugolini, A. The impact of Twitter sentiment on renewable energy stocks. Energy Econ.
**2018**, 76, 153–169. [Google Scholar] [CrossRef] - Hong, H.; Yogo, M. What does futures market interest tell us about the macroeconomy and asset prices? J. Financ. Econ.
**2012**, 105, 473–490. [Google Scholar] [CrossRef][Green Version] - Singleton, K.J. Investor flows and the 2008 boom/bust in oil prices. Manag. Sci.
**2014**, 60, 300–318. [Google Scholar] [CrossRef][Green Version] - Olson, E.; Vivian, A.J.; Wohar, M.E. Do commodities make effective hedges for equity investors? Res. Int. Bus. Financ.
**2017**, 1274–1288. [Google Scholar] [CrossRef][Green Version] - Olson, E.; Vivian, A.J.; Wohar, M.E. What is a better cross-hedge for energy: Equities or other commodities? Glob. Financ. J.
**2019**, 42, 100417. [Google Scholar] [CrossRef][Green Version] - Qadan, M.; Nama, H. Investor sentiment and the price of oil. Energy Econ.
**2018**, 69, 42–58. [Google Scholar] [CrossRef] - Zhang, Y.-J.; Li, S.-H. The impact of investor sentiment on crude oil market risks: Evidence from the wavelet approach. Quant. Financ.
**2019**, 19, 1357–1371. [Google Scholar] [CrossRef] - Guo, J.-F.; Ji, Q. How does market concern derived from the Internet affect oil prices? Appl. Energy
**2013**, 112, 1536–1543. [Google Scholar] [CrossRef][Green Version] - Ji, Q.; Guo, J.-F. Oil price volatility and oil-related events: An Internet concern study perspective. Appl. Energy
**2015**, 137, 256–264. [Google Scholar] [CrossRef] - Campbell, J.Y. Viewpoint: Estimating the equity premium. Can. J. Econ.
**2008**, 41, 1–21. [Google Scholar] [CrossRef][Green Version] - Andersen, T.G.; Dobrev, D.; Schaumburg, E. Jump-robust volatility estimation using nearest neighbor truncation. J. Econom.
**2012**, 169, 75–93. [Google Scholar] [CrossRef][Green Version] - Müller, U.A.; Dacorogna, M.M.; Davé, R.D.; Olsen, R.B.; Pictet, O.V. Volatilities of different time resolutions—Analyzing the dynamics of market components. J. Empir. Financ.
**1997**, 4, 213–239. [Google Scholar] [CrossRef] - Amaya, D.; Christoffersen, P.; Jacobs, K.; Vasquez, A. Does realized skewness predict the cross-section of equity returns? J. Financ. Econ.
**2015**, 118, 135–167. [Google Scholar] [CrossRef][Green Version] - Andersen, T.G.; Bollerslev, T.; Huang, X. A reduced form framework for modeling volatility of speculative prices based on realized variation measures. J. Econom.
**2011**, 160, 176–189. [Google Scholar] [CrossRef] - Barndorff-Nielsen, O.E. and Shephard, N. Power and bipower variation with stochastic volatility and jumps. J. Financ. Econom.
**2004**, 2, 1–37. [Google Scholar] [CrossRef][Green Version] - Barndorff-Nielsen, O.E.; Shephard, N. Econometrics of Testing for Jumps in Financial Economics using Bipower Variation. J. Financ. Econom.
**2006**, 4, 1–30. [Google Scholar] [CrossRef] - Zhou, H.; Zhu, J.Q. An empirical examination of jump risk in asset pricing and volatility forecasting in China’s equity and bond markets. Pac. Basin Financ. J.
**2012**, 20, 857–880. [Google Scholar] [CrossRef] - Diebold, F.X.; Mariano, R.S. Comparing predictive accuracy. J. Bus. Econ. Stat.
**1995**, 13, 253–263. [Google Scholar] - Harvey, D.; Leybourne, S.; Newbold, P. Testing the equality of prediction mean squared errors. Int. J. Forecast.
**1997**, 13, 281–291. [Google Scholar] [CrossRef] - Bollerslev, T.; Ghysels, E. Periodic autoregressive conditional heteroscedasticity. J. Bus. Econ. Stat.
**1996**, 14, 139–151. [Google Scholar] - R Core Team. R: A Language and Environment for Statistical Computing, R version 3.3.3; R Foundation for Statistical Computing: Vienna, Austria, 2019; Available online: http://www.R-project.org/ (accessed on 20 March 2020).
- Hyndman, R.J. Forecast: Forecasting Functions for Time Series and Linear Models; R Package Version 8.0; 2017; Available online: http://github.com/robjhyndman/forecast (accessed on 20 March 2020).
- Hyndman, R.J.; Khandakar, Y. Automatic time series forecasting: The forecast package for R. J. Stat. Softw.
**2008**, 26, 1–22. [Google Scholar] - Liu, L.Y.; Patton, A.J.; Sheppard, K. Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes. J. Econom.
**2015**, 187, 293–311. [Google Scholar] [CrossRef][Green Version] - Bathia, D.; Bredin, D. An examination of investor sentiment effect in G7 stock market returns. Eur. J. Financ.
**2013**, 19, 909–937. [Google Scholar] [CrossRef] - Bathia, D.; Bredin, D.; Nitzsche, D. International sentiment spillovers in equity returns. Int. J. Financ. Econ.
**2016**, 21, 332–359. [Google Scholar] [CrossRef] - Baker, M.; Wurgler, J. Investor sentiment and the cross-section of stock returns. J. Financ.
**2006**, 61, 1645–1680. [Google Scholar] [CrossRef][Green Version] - Baker, M.; Wurgler, J. Investor sentiment in the stock market. J. Econ. Perspect.
**2007**, 21, 129–152. [Google Scholar] [CrossRef][Green Version] - Da, Z.; Engelberg, J.; Gao, P. The Sum of All FEARS Investor Sentiment and Asset Prices. Rev. Financ. Stud.
**2015**, 28, 1–32. [Google Scholar] [CrossRef][Green Version] - García, D. Sentiment during recessions. J. Financ.
**2013**, 68, 1267–1300. [Google Scholar] [CrossRef] - Mei, D.; Liu, J.; Ma, F.; Chen, W. Forecasting stock market volatility: Do realized skewness and kurtosi? Help. Phys. A
**2017**, 481, 153–159. [Google Scholar] [CrossRef] - Giacomini, R.; Rossi., B. Forecast comparisons in unstable environments. J. Appl. Econom.
**2010**, 25, 595–620. [Google Scholar] [CrossRef] - Barndorff-Nielsen, O.E.; Kinnebrouk, S.; Shephard, N. Measuring downside risk: Realised semivariance. In Volatility and Time Series Econometrics: Essays in Honor of Robert F. Engle; Bollerslev, T., Russell, J., Watson, M., Eds.; Oxford University Press: Oxford, UK, 2010; pp. 117–136. [Google Scholar]
- Deeney, P.; Cummins, M.; Dowling, M.; Bermingham, A. Sentiment in oil markets. Int. Rev. Financ. Anal.
**2015**, 39, 179–185. [Google Scholar] [CrossRef][Green Version]

Statistic | MRV | HA |
---|---|---|

Min | 0.001 | 5.840 |

Mean | 0.424 | 6.026 |

Median | 0.222 | 6.033 |

Max | 4.997 | 6.357 |

Results.Table | Intercept | MRV | MRV${}_{\mathit{w}}$ | MRV${}_{\mathit{m}}$ | HA | RKU | RSK | Adj. R2 |
---|---|---|---|---|---|---|---|---|

$h=1$ | ||||||||

HAR-RV | 2.8153 | 4.0303 | 8.8586 | 1.7208 | – | – | – | 0.6354 |

p-value | 0.0049 | 0.0001 | 0.0000 | 0.0853 | – | – | – | – |

HAR-RV-HA | 4.2709 | 3.7583 | 8.9359 | 1.9765 | −4.2584 | – | – | 0.6390 |

p-value | 0.0000 | 0.0002 | 0.0000 | 0.0481 | 0.0000 | – | – | – |

HAR-RV-HA-RKU | 4.5456 | 3.9123 | 8.5785 | 1.8141 | −4.5257 | −1.3242 | – | 0.6390 |

p-value | 0.0000 | 0.0001 | 0.0000 | 0.0697 | 0.0000 | 0.1854 | – | – |

HAR-RV-HA-RSK | 4.2172 | 3.7246 | 8.9613 | 1.9992 | −4.2049 | – | −1.4846 | 0.6391 |

p-value | 0.0000 | 0.0002 | 0.0000 | 0.0456 | 0.0000 | – | 0.1377 | – |

HAR-RV-HA-RKU-RSK | 4.4645 | 3.8698 | 8.6267 | 1.8390 | −4.4448 | −1.0451 | −1.2514 | 0.6391 |

p-value | 0.0000 | 0.0001 | 0.0000 | 0.0659 | 0.0000 | 0.2960 | 0.2108 | – |

$h=5$ | ||||||||

HAR-RV | 1.4702 | 3.9532 | 5.4185 | 2.8944 | – | – | – | 0.8431 |

p-value | 0.1415 | 0.0001 | 0.0000 | 0.0038 | – | – | – | – |

HAR-RV-HA | −0.2349 | 3.8698 | 5.4262 | 2.7933 | 0.2532 | – | – | 0.8431 |

p-value | 0.8143 | 0.0001 | 0.0000 | 0.0052 | 0.8001 | – | – | – |

HAR-RV-HA-RKU | −0.2244 | 4.0214 | 4.8399 | 2.6085 | 0.2416 | −0.1102 | – | 0.8430 |

p-value | 0.8225 | 0.0001 | 0.0000 | 0.0091 | 0.8091 | 0.9122 | – | – |

HAR-RV-HA-RSK | −0.2348 | 3.8914 | 5.4489 | 2.8141 | 0.253 | – | −0.0847 | 0.8430 |

p-value | 0.8144 | 0.0001 | 0.0000 | 0.0049 | 0.8003 | – | 0.9325 | – |

HAR-RV-HA-RKU-RSK | −0.2235 | 4.0578 | 4.8533 | 2.6302 | 0.2406 | −0.0956 | −0.0679 | 0.8429 |

p-value | 0.8231 | 0.0000 | 0.0000 | 0.0085 | 0.8099 | 0.9239 | 0.9459 | – |

$h=22$ | ||||||||

HAR-RV | 1.2423 | 4.9368 | 2.7946 | 1.9409 | – | – | – | 0.8410 |

p-value | 0.2141 | 0.0000 | 0.0052 | 0.0523 | – | – | – | – |

HAR-RV-HA | −1.0653 | 4.9981 | 3.0358 | 2.0031 | 1.0739 | – | – | 0.8416 |

p-value | 0.2868 | 0.0000 | 0.0024 | 0.0452 | 0.2829 | – | – | – |

HAR-RV-HA-RKU | −1.1839 | 4.8468 | 2.6076 | 1.8103 | 1.1898 | 0.9923 | – | 0.8415 |

p-value | 0.2365 | 0.0000 | 0.0091 | 0.0702 | 0.2341 | 0.3210 | – | – |

HAR-RV-HA-RSK | −1.0820 | 4.9983 | 3.0343 | 2.0029 | 1.0908 | – | −1.0739 | 0.8416 |

p-value | 0.2793 | 0.0000 | 0.0024 | 0.0452 | 0.2753 | – | 0.2829 | – |

HAR-RV-HA-RKU-RSK | −1.1341 | 4.8989 | 2.6945 | 1.8347 | 1.1397 | 1.2809 | −1.2352 | 0.8416 |

p-value | 0.2567 | 0.0000 | 0.0071 | 0.0666 | 0.2544 | 0.2002 | 0.2167 | – |

Rolling Window | $\mathit{h}=1$ | $\mathit{h}=5$ | $\mathit{h}=22$ |
---|---|---|---|

L1 loss | |||

1000 | 0.0269 | 0.5714 | 0.2693 |

1200 | 0.0007 | 0.4707 | 0.3105 |

1400 | 0.0000 | 0.9985 | 0.9274 |

L2 loss | |||

1000 | 0.0327 | 0.7654 | 0.6027 |

1200 | 0.0049 | 0.8196 | 0.6977 |

1400 | 0.0015 | 0.9641 | 0.9762 |

Specification Window | $\mathit{h}=1$ | $\mathit{h}=5$ | $\mathit{h}=22$ |
---|---|---|---|

HAR-RV-RKU vs. HAR-RV-RKU-HA | 0.0055 | 0.8292 | 0.7168 |

HAR-RV-RSK vs. HAR-RV-RSK-HA | 0.0045 | 0.8188 | 0.6888 |

HAR-RV-JUMP vs. HAR-RV-JUMP-HA | 0.0055 | 0.8171 | 0.6962 |

Rolling Window | $\mathit{h}=1$ | $\mathit{h}=5$ | $\mathit{h}=22$ |
---|---|---|---|

RVG | |||

1000 | 0.0711 | 0.7816 | 0.4886 |

1200 | 0.0015 | 0.8577 | 0.6647 |

1400 | 0.0005 | 0.9646 | 0.9708 |

RVB | |||

1000 | 0.0615 | 0.7825 | 0.5795 |

1200 | 0.0519 | 0.8274 | 0.6431 |

1400 | 0.0095 | 0.9687 | 0.9663 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Bonato, M.; Gkillas, K.; Gupta, R.; Pierdzioch, C.
Investor Happiness and Predictability of the Realized Volatility of Oil Price. *Sustainability* **2020**, *12*, 4309.
https://doi.org/10.3390/su12104309

**AMA Style**

Bonato M, Gkillas K, Gupta R, Pierdzioch C.
Investor Happiness and Predictability of the Realized Volatility of Oil Price. *Sustainability*. 2020; 12(10):4309.
https://doi.org/10.3390/su12104309

**Chicago/Turabian Style**

Bonato, Matteo, Konstantinos Gkillas, Rangan Gupta, and Christian Pierdzioch.
2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price" *Sustainability* 12, no. 10: 4309.
https://doi.org/10.3390/su12104309