# A New Index for Measuring Uncertainty Due to the COVID-19 Pandemic

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. The News-Based Index

#### 2.2. The Macro-Based Index

#### 2.3. The New Index for Measuring Uncertainty Due to COVID-19 Pandemic

#### 2.4. Methodology

## 3. Results

#### 3.1. Main Results

#### 3.2. Robustness

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Baker, S.R.; Bloom, M.A.; Davis, S.J.; Terry, S.J. Covid-Induced Economic Uncertainty; Working Paper No. 26983; NBER: Cambridge, MA, USA, 2020. [Google Scholar]
- Nicomedes, C.J.C.; Avila, R.M.A. An Analysis on the Panic during COVID-19 Pandemic through an Online Form. J. Aff. Dis.
**2020**. [Google Scholar] [CrossRef] - Salisu, A.A.; Akanni, L. Constructing a global fear index for COVID-19 pandemic. Emer. Mark. Fin. Trade
**2020**, 56, 2213–2230. [Google Scholar] [CrossRef] - Al-Awadhi, A.M.; Al-Saifi, K.; Al-Awadhi, A.; Alhamadi, S. Death and contagious infectious diseases: Impact of the COVID-19 virus on stock market returns. J. Beh. Exp. Fin.
**2020**, 100326. [Google Scholar] [CrossRef] [PubMed] - Albulescu, C.T. COVID-19 and the United States financial markets’ volatility. Fin. Res. Let.
**2020**, 101699. [Google Scholar] [CrossRef] - Ashraf, B.N. Stock markets’ reaction to COVID-19: Cases or fatalities? Res. Int. Bus. Fin.
**2020**, 54, 101249, 101016/jribaf2020101249. [Google Scholar] [CrossRef] - Baker, S.; Bloom, N.; Davis, S.J.; Kost, K.; Sammon, M.; Viratyosin, T. The Unprecedented Stock Market Reaction to COVID-19. Rev. Asset Pricing Stud.
**2020**, 10, 742–758. [Google Scholar] [CrossRef] - Ciner, C. Stock Return Predictability in the time of COVID-19. Fin. Res. Let.
**2020**. [Google Scholar] [CrossRef] - Dutta, A.; Das, D.; Jana, R.K.; Vo, X.V. COVID-19 and oil market crash: Revisiting the safe haven property of gold and Bitcoin. Res. Pol.
**2020**, 69. [Google Scholar] [CrossRef] - Erdem, O. Freedom and Stock Market Performance during Covid-19 Outbreak. Fin. Res. Let.
**2020**, 101671. [Google Scholar] [CrossRef] [PubMed] - Haroon, O.; Rizvi, S. COVID-19: Media coverage and financial markets behavior—A sectoral inquiry. J. Beh. Exp. Fin.
**2020**, 27. [Google Scholar] [CrossRef] [PubMed] - He, P.; Sun, Y.; Zhang, Y.; Li, T. COVID-19’s impact on stock prices across different sectors-An event study based on the Chinese stock market. Emer. Mark. Fin. Trade
**2020**, 56, 2198–2212. [Google Scholar] [CrossRef] - Lahmiri, S.; Bekiros, S. The Impact of COVID-19 pandemic upon Stability and Sequential Irregularity of Equity and Cryptocurrency Markets. Chaos Solitons Fractals
**2020**, 138, 109936. [Google Scholar] [CrossRef] [PubMed] - Mishra, A.; Rath, B.; Dash, A. Does the Indian financial market nosedive because of the COVID-19 outbreak, in comparison to after demonetisation and the GST? Emer. Mark. Fin. Trade
**2020**, 56, 2162–2180. [Google Scholar] [CrossRef] - Okorie, D.I.; Lin, B. Stock Markets and the COVID-19 Fractal Contagion Effects. Fin. Res. Let.
**2020**, 101640. [Google Scholar] [CrossRef] [PubMed] - Phan, D.; Narayan, P. Country responses and the reaction of the stock market to COVID-19—A Preliminary Exposition. Emer. Mark. Fin. Trade
**2020**, 56, 2138–2150. [Google Scholar] [CrossRef] - Salisu, A.A.; Ogbonna, A.; Adediran, I. Stock-induced Google trends and the predictability of sectoral stock returns. J. Forecast.
**2020**. [Google Scholar] [CrossRef] - Salisu, A.A.; Ogbonna, A.E.; Adewuyi, A. Google trends and the predictability of precious metals. Res. Pol.
**2020**, 65, 101542. [Google Scholar] [CrossRef] - Salisu, A.A.; Vo, X.V. Predicting stock returns in the presence of COVID-19 pandemic: The role of health news. Int. Rev. Fin. Ana.
**2020**, 71, 101546. [Google Scholar] [CrossRef] - Topcu, M.; Gulal, O.S. The impact of COVID-19 on emerging stock markets. Fin. Res. Let.
**2020**, 101691. [Google Scholar] [CrossRef] - Zhang, D.; Hu, M.; Ji, Q. Financial markets under the global pandemic of COVID 19. Fin. Res. Let.
**2020**, 36. [Google Scholar] [CrossRef] [PubMed] - Olubusoye, O.E.; Ogbonna, A.E.; Yaya, O.S.; Umolo, D. An Information-Based Index of Uncertainty and the predictability of Energy Prices. Int. J. Ener. Res.
**2020**. [Google Scholar] [CrossRef] - Salisu, A.A.; Adediran, I. Uncertainty due to infectious diseases and energy market volatility. Ener. Res. Let.
**2020**, 1. [Google Scholar] [CrossRef] - Prabheesh, K.; Padhan, R.; Garg, B. COVID-19 and the oil price-stock market nexus: Evidence from net oil-importing countries. Ener. Res. Let.
**2020**, 1. [Google Scholar] [CrossRef] - Zhao, R. Inferring private information from online news and searches: Correlation and prediction in Chinese stock market. Phys. A Stat. Mech. App.
**2019**, 528, 121450. [Google Scholar] [CrossRef] - Nguyen, C.P.; Schonckus, C.; Hong Nguyen, T.V. Google Search and Stock returns in Emerging Markets. Borsa Istanb. Rev.
**2019**, 19, 288–296. [Google Scholar] [CrossRef] - Chronopoulos, D.K.; Papadimitriou, F.I.; Vlastakis, N. Information demand and stock return predictability. J. Int. Money Fin.
**2018**, 80, 59–74. [Google Scholar] [CrossRef] [Green Version] - Salisu, A.A.; Swaray, R.; Oloko, T.F. Improving the predictability of the oil–US stock nexus: The role of macroeconomic variables. Econ. Model.
**2019**, 76, 153–171. [Google Scholar] [CrossRef] - Yaya, O.S.; Ogbonna, A.E. Do We Experience Day-of-the-Week Effects in Returns and Volatility of Cryptocurrency? MPRA Paper 91429; University Library of Munich: Munich, Germany, 2019. [Google Scholar]
- Westerlund, J.; Karabiyik, H.; Narayan, P. Testing for predictability in panels with general Predictors. J. App. Econom.
**2016**. [Google Scholar] [CrossRef] - Clark, T.; West, T.D. Approximately normal tests for equal predictive accuracy in nested models. J. Econom.
**2007**, 138, 291–311. [Google Scholar] [CrossRef] [Green Version] - Salisu, A.A.; Oloko, T.F. Modeling oil price–US stock nexus: A VARMA–BEKK–AGARCH approach. Ener. Econs.
**2015**, 50, 1–12. [Google Scholar] [CrossRef] - Lakonishok, J.; Shapiro, A.C. Systematic risk, total risk and size as determinants of stock market returns. J. Bank. Fin.
**1986**, 10, 115–132. [Google Scholar] [CrossRef] - Gatfaoui, H. How Does Systematic Risk Impact Stocks? A Study of the French Financial Market. In Asset Allocation and International Investments; Gregoriou, G.N., Ed.; Chapter 10; Palgrave Macmillan, a division of Macmillan Publishers Limited: London, UK, 2007; pp. 183–213. [Google Scholar]
- Santis, R.A.D. Unobservable Systematic Risk, Economic Activity and Stock Market. J. Bank. Fin.
**2018**, 97, 51–69. [Google Scholar] [CrossRef] - Chan, J.; Grant, A. Modeling energy price dynamics: GARCH versus stochastic volatility. Ener. Econs.
**2016**, 54, 182–189. [Google Scholar] [CrossRef] [Green Version] - Investing.com Website. 2020. Available online: https://www.investing.com (accessed on 13 March 2021).
- Salisu, A.A.; Ogbonna, A.E.; Omosebi, P.A. Does the Choice of Estimator Matter for Forecasting? A Revisit; Centre for Econometric and Allied Research, University of Ibadan Working Papers Series, CWPS 0053; CEAR, Univeristy of Ibadan: Oyo, Nigeria, 2018. [Google Scholar]
- Chudik, A.; Pesaran, M.H. Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors. J. Econom.
**2015**, 188, 393–420. [Google Scholar] [CrossRef] [Green Version] - Chudik, A.; Mohaddes, K.; Pesaran, M.; Raissi, M. Long-Run Effects in Large Heterogeneous Panel Data Models with Cross-Sectionally Correlated Errors; Emerald Group Publishing Limited: Bingley, UK, 2016; pp. 85–135. [Google Scholar]
- Westerlund, J.; Narayan, P. Testing for predictability in panels of any time series dimension. Int. J. Forecast.
**2016**, 32, 1162–1177. [Google Scholar] [CrossRef] - Ditzen, J. Estimating Dynamic Common Correlated Effects in Stata. Stata J.
**2018**, 18, 585–617. [Google Scholar] [CrossRef] - Ditzen, J. Estimating Long Run Effects in Models with Cross-Sectional Dependence Using Xtdcce2; CEERP Working Paper Series: 7; Heriot-Watt University: Edinburg, Scotland, 2019. [Google Scholar]
- Zhang, J.; Lai, Y.; Lin, J. The day-of-the-Week effects of stock markets in different countries. Fin. Res. Let.
**2017**, 20, 47–62. [Google Scholar] [CrossRef] - Campbell, J.; Thompson, S. Predicting excess stock returns out of sample: Can anything beat the historical average? Rev. Fin. Stud.
**2008**, 21, 1509–1531. [Google Scholar] [CrossRef] [Green Version]

**Figure 2.**Bivariate plot of major stocks of twenty worst-hit countries by COVID-19 and the news-based index of COVID-19-induced uncertainties.

**Figure 3.**Bivariate plot of major stocks of twenty worst-hit countries by COVID-19 and the macro-based index of COVID-19-induced uncertainties.

**Figure 4.**Bivariate plot of major stocks of twenty worst-hit countries by COVID-19 and the composite index of COVID-19-induced uncertainties.

Model | Gold Price | Oil Price | Commodity Index | Dollar Index | Stock Price |
---|---|---|---|---|---|

Scheme 240. | −240.9 | −427.1 | −253.8 | −90.0 | −291.3 |

SV_2 | −241.0 | −427.4 | −254.3 | −90.6 | −291.2 |

SV_J | −242.2 | −427.4 | −254.3 | −93.5 | −290.8 |

SV_L | −245.6 | −433.9 | −258.3 | −93.4 | −296.5 |

SV_M | −241.7 | −428.8 | −254.7 | −91.0 | −291.1 |

SV_MA | −240.8 | −426.9 | −253.4 | −90.5 | −291.3 |

SV_t | −240.9 | - | −252.0 | −90.6 | - |

Optimal Model | SV_MA | SV_MA | SV_t | SV | SV_M |

**Note:**SV incorporates a stationary AR(1) log-volatility process; SV-2 is SV model with AR(2) log-volatility process; SV-J is SV with a jump component in the price equation; SV-L is SV with leverage; SV-M is SV with log-volatility included as a covaria11te in the price equation; SV-MA is SV model with MA(1) observation error; while SV-t is SV model with t innovation. The models designated with (-) indicates no convergence.

Mean | SD | Min. | Max. | Skewness | Kurtosis | Mean | SD | Min | Max | Skewness | Kurtosis | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Stock Returns | Exchange Rate | |||||||||||

Argentina | −0.0004 | 0.0404 | −0.1596 | 0.1187 | −0.7919 | 5.5888 | 0.015 | 0.001 | 0.014 | 0.017 | 0.064 | 1.622 |

Brazil | −0.0030 | 0.0444 | −0.1943 | 0.1516 | −1.1057 | 8.3183 | 0.204 | 0.023 | 0.170 | 0.248 | 0.505 | 1.818 |

Canada | −0.0005 | 0.0278 | −0.1333 | 0.1180 | −0.9434 | 11.8686 | 0.734 | 0.021 | 0.689 | 0.771 | −0.046 | 1.963 |

Chile | −0.0019 | 0.0319 | −0.1674 | 0.0816 | −1.4908 | 10.2145 | 0.001 | 0.000 | 0.001 | 0.001 | 0.071 | 1.568 |

Colombia | −0.0041 | 0.0437 | −0.2190 | 0.1594 | −1.3138 | 12.4686 | 0.000 | 0.000 | 0.000 | 0.000 | 0.231 | 1.833 |

France | −0.0012 | 0.0247 | −0.1315 | 0.0806 | −1.2034 | 9.0450 | 1.105 | 0.019 | 1.069 | 1.145 | 0.170 | 1.902 |

Germany | −0.0005 | 0.0242 | −0.1334 | 0.0990 | −1.0600 | 10.8425 | 1.105 | 0.019 | 1.069 | 1.145 | 0.170 | 1.902 |

India | −0.0017 | 0.0224 | −0.1374 | 0.0631 | −1.9005 | 14.0439 | 0.013 | 0.000 | 0.013 | 0.014 | 0.635 | 1.727 |

Italy | −0.0012 | 0.0275 | −0.1878 | 0.0828 | −2.6413 | 19.2188 | 1.105 | 0.019 | 1.069 | 1.145 | 0.170 | 1.902 |

Mexico | −0.0026 | 0.0295 | −0.1118 | 0.0686 | −0.7194 | 4.9219 | 0.046 | 0.005 | 0.039 | 0.054 | 0.450 | 1.590 |

Pakistan | −0.0022 | 0.0208 | −0.0779 | 0.0493 | −0.9942 | 5.5772 | 0.006 | 0.000 | 0.006 | 0.006 | −0.030 | 1.474 |

Peru | −0.0029 | 0.0312 | −0.1356 | 0.1018 | −0.8587 | 6.7586 | 0.292 | 0.006 | 0.280 | 0.303 | −0.140 | 2.106 |

Qatar | −0.0009 | 0.0149 | −0.0718 | 0.0430 | −1.0439 | 9.5622 | 0.274 | 0.001 | 0.272 | 0.275 | −0.507 | 1.572 |

Russia | −0.0012 | 0.0279 | −0.1186 | 0.0837 | −1.0569 | 7.3341 | 0.014 | 0.001 | 0.012 | 0.016 | 0.294 | 1.944 |

South Africa | −0.0011 | 0.0174 | −0.0890 | 0.0643 | −1.2703 | 10.6315 | 0.060 | 0.006 | 0.052 | 0.071 | 0.414 | 1.800 |

Saudi Arabia | −0.0003 | 0.0247 | −0.0948 | 0.0723 | −1.0855 | 7.0813 | 0.266 | 0.000 | 0.266 | 0.267 | −0.903 | 2.664 |

Spain | −0.0017 | 0.0262 | −0.1547 | 0.0751 | −1.5283 | 11.5221 | 1.105 | 0.019 | 1.069 | 1.145 | 0.170 | 1.902 |

Turkey | −0.0005 | 0.0175 | −0.0840 | 0.0540 | −1.0176 | 7.9350 | 0.154 | 0.010 | 0.139 | 0.171 | 0.434 | 1.546 |

UK | −0.0014 | 0.0224 | −0.1150 | 0.0849 | −1.0183 | 8.8013 | 1.260 | 0.036 | 1.149 | 1.320 | −0.422 | 3.174 |

US | 0.0001 | 0.0277 | −0.1292 | 0.0899 | −0.7214 | 8.0750 | 1.000 | 0.000 | 1.000 | 1.000 | . | . |

Global Variables | ||||||||||||

$ciustk.news$ | 43.44 | 28.80 | 2.13 | 100.00 | −0.10 | 1.84 | ||||||

$ciustk.mac$ | 51.11 | 17.15 | 1.00 | 100.00 | 0.23 | 4.35 | ||||||

$ciustk.cmp$ | 56.22 | 21.01 | 0.00 | 100.00 | −0.26 | 2.81 | ||||||

$wti$ | 37.29 | 14.78 | −37.63 | 63.27 | −0.82 | 6.08 | ||||||

$vix$ | 32.22 | 15.61 | 12.10 | 82.69 | 0.97 | 3.85 |

Variables | Model without Control | Model with Control | ||
---|---|---|---|---|

Static | Dynamic | Static | Dynamic | |

News-Based Index of COVID-Induced Uncertainty$\left[ciust{k}_{news}\right]$ | ||||

$rstk\left(-1\right)$ | - | −0.128 *** [0.040] | - | −0.165 *** [0.044] |

$lwti\left(-1\right)$ | - | - | −0.672 *** [0.087] | −0.609 *** [0.097] |

$lexr\left(-1\right)$ | - | - | −0.189 * [0.115] | 0.056 [0.131] |

$lciust{k}_{news}\left(-1\right)$ | −1.060 *** [0.258] | −1.752 *** [0.282] | −1.204 *** [0.257] | −1.874 *** [0.279] |

$lciust{k}_{news}\left(-2\right)$ | 0.003 [0.396] | 0.598 [0.379] | 0.175 [0.404] | 0.659 * [0.363] |

$lciust{k}_{news}\left(-3\right)$ | −1.593 *** [0.262] | −1.605 *** [0.258] | −1.762 *** [0.267] | −1.616 *** [0.270] |

$lciust{k}_{news}\left(-4\right)$ | 2.181 *** [0.465] | 1.797 *** [0.396] | 2.261 *** [0.456] | 1.776 *** [0.363] |

$lciust{k}_{news}\left(-5\right)$ | 0.566 ** [0.281] | 1.050 *** [0.212] | 0.463 * [0.275] | 0.996 *** [0.207] |

Joint Coef. Est. | 0.096 *** | 0.088 *** | −0.066 ** | −0.059 ** |

Composite Index of COVID-Induced Uncertainty$\left[ciust{k}_{comp}\right]$ | ||||

$rstk\left(-1\right)$ | - | −0.107 ** [0.043] | - | −0.178 *** [0.049] |

$lwti\left(-1\right)$ | - | - | 0.747 *** [0.160] | 0.980 *** [0.197] |

$lexr\left(-1\right)$ | - | - | −0.282 * [0.170] | −0.043 [0.208] |

$lciust{k}_{comp}\left(-1\right)$ | −2.141 *** [0.334] | −1.673 *** [0.263] | −2.222 *** [0.355] | −1.651 *** [0.264] |

$lciust{k}_{comp}\left(-2\right)$ | 3.199 *** [0.743] | 2.176 *** [0.547] | 3.085 *** [0.735] | 1.711 *** [0.549] |

$lciust{k}_{comp}\left(-3\right)$ | 1.047 [0.769] | 2.171 *** [0.677] | 1.013 [0.764] | 2.376 *** [0.719] |

$lciust{k}_{comp}\left(-4\right)$ | −5.317 *** [0.777] | −5.739 *** [0.735] | −5.821 *** [0.777] | −6.309 *** [0.786] |

$lciust{k}_{comp}\left(-5\right)$ | 1.492 *** [0.515] | 1.160 *** [0.444] | 1.643 *** [0.492] | 1.164 ** [0.463] |

Joint Coef. Est. | −1.720*** | −1.905*** | −2.302 *** | −2.709 *** |

Variables | Model without Control | Model with Control | ||||||
---|---|---|---|---|---|---|---|---|

50% Sample | 75% Sample | 50% Sample | 75% Sample | |||||

Static | Dynamic | Static | Dynamic | Static | Dynamic | Static | Dynamic | |

Model with News-Based Index versus Historical Average Model | ||||||||

In-sample | 1.926 *** [0.359] | 3.662 *** [0.575] | 1.205 *** [0.218] | 2.243 *** [0.337] | 3.063 *** [0.416] | 4.989 *** [0.738] | 1.854 *** [0.246] | 3.170 *** [0.427] |

10-Day ahead | 1.635 *** [0.315] | 3.233 *** [0.503] | 1.185 *** [0.200] | 2.101 *** [0.308] | 0.530 [0.902] | 2.806 *** [1.001] | 0.911 *** [0.346] | 2.134 *** [0.468] |

20-Day ahead | 1.516 *** [0.280] | 2.883 *** [0.448] | 1.095 *** [0.184] | 1.957 *** [0.283] | −2.631 ** [1.330] | −0.104 [1.213] | 0.767 ** [0.344] | 1.909 *** [0.448] |

Model with Composite Index versus Historical Average Model | ||||||||

In-sample | 6.443 *** [0.877] | 8.344 *** [1.017] | 3.682 *** [0.477] | 4.618 *** [0.548] | 9.171 *** [1.021] | 12.768 *** [1.358] | 4.463 *** [0.539] | 5.922 *** [0.674] |

10-Day ahead | 5.085 *** [0.835] | 6.703 *** [0.966] | 2.534 *** [0.527] | 3.236 *** [0.599] | 7.839 *** [0.906] | 11.131 *** [1.202] | 3.293 *** [0.558] | 4.475 *** [0.683] |

20-Day ahead | 3.235 *** [0.831] | 4.372 *** [0.979] | 2.152 *** [0.503] | 2.806 *** [0.572] | 6.418 *** [0.811] | 9.175 *** [1.079] | 2.848 *** [0.526] | 3.928 *** [0.643] |

Model with Composite Index versus Model with News-Based Index | ||||||||

In-sample | 5.549 *** [0.783] | 6.259 *** [0.777] | 6.692 *** [0.817] | 8.368 *** [0.819] | 3.025 *** [0.441] | 3.204 *** [0.428] | 3.461 *** [0.475] | 3.852 *** [0.468] |

10-Day ahead | 6.191 *** [0.800] | 6.830 *** [0.822] | 146.105 *** [13.899] | 112.957 *** [11.609] | 2.764 *** [0.505] | 2.776 *** [0.520] | 5.109 *** [0.721] | 4.477 *** [0.739] |

20-Day ahead | 5.674 *** [0.813] | 6.021 *** [0.895] | 250.103 *** [16.158] | 187.254 *** [13.409] | 3.022 *** [0.487] | 3.033 *** [0.506] | 6.670 *** [0.820] | 5.203 *** [0.841] |

Variables | Model without Control | Model with Control | ||
---|---|---|---|---|

Static | Dynamic | Static | Dynamic | |

$rstk\left(-1\right)$ | - | −0.178 *** [0.043] | - | −0.206 *** [0.043] |

$lwti\left(-1\right)$ | - | - | −1.297 *** [0.121] | −1.236 *** [0.132] |

$lexr\left(-1\right)$ | - | - | −16.956 [13.099] | 5.727 [14.532] |

$lvix\left(-1\right)$ | −1.078 [0.807] | −3.878 *** [0.736] | −0.401 [0.867] | −3.125 *** [0.764] |

$lvix\left(-2\right)$ | −4.535 *** [1.300] | −2.284 ** [1.139] | −5.380 *** [1.406] | −3.306 *** [1.171] |

$lvix\left(-3\right)$ | 2.561 *** [0.642] | 2.328 *** [0.688] | 2.968 *** [0.683] | 2.895 *** [0.689] |

$lvix\left(-4\right)$ | 2.388 *** [0.516] | 2.923 *** [0.548] | 2.302 *** [0.496] | 2.839 *** [0.546] |

$lvix\left(-5\right)$ | 0.568 [0.409] | 0.713 * [0.397] | −0.341 [0.407] | −0.213 [0.422] |

Joint Coef. Est. | −0.096 * | −0.198 *** | −0.852 *** | −0.910 *** |

Variables | Model without Control | Model with Control | ||||||
---|---|---|---|---|---|---|---|---|

Static | Dynamic | Static | Dynamic | |||||

Model with Volatility Index [VIX] vs Historical Average Model | ||||||||

50% Sample | 75% Sample | 50% Sample | 75% Sample | 50% Sample | 75% Sample | 50% Sample | 75% Sample | |

In-sample | 3.472 *** [0.468] | 1.998 *** [0.264] | 4.619 *** [0.603] | 2.938 *** [0.349] | 5.071 *** [0.606] | 2.863 *** [0.331] | 6.321 *** [0.792] | 3.945 *** [0.457] |

10-Day ahead | 2.892 *** [0.411] | 1.848 *** [0.243] | 3.962 *** [0.531] | 2.637 *** [0.321] | 1.313 [1.145] | 1.531 *** [0.497] | 2.679 ** [1.205] | 2.488 *** [0.583] |

20-Day ahead | 2.657 *** [0.368] | 1.705 *** [0.225] | 3.590 *** [0.474] | 2.439 *** [0.296] | −3.116 * [1.607] | 1.312 *** [0.497] | −1.616 [1.567] | 2.213 *** [0.570] |

Model with Composite Index vs Model with Volatility Index [VIX] | ||||||||

In-Sample | 5.122 *** [0.724] | 5.627 *** [0.634] | 5.656 *** [0.744] | 6.750 *** [0.667] | 2.947 *** [0.414] | 3.125 *** [0.369] | 3.332 *** [0.469] | 3.645 *** [0.431] |

10-Day | 5.014 *** [0.760] | 4.557 *** [0.744] | 247.446 *** [22.417] | 228.908 *** [21.530] | 2.054 *** [0.479] | 1.419 *** [0.469] | 12.116 *** [1.508] | 11.503 *** [1.520] |

20-Day | 4.429 *** [0.775] | 3.180 *** [0.816] | 426.808 *** [26.156] | 388.680 *** [25.061] | 1.787 *** [0.468] | 0.736 [0.466] | 20.609 *** [1.948] | 18.864 *** [1.940] |

Variables | Model without Control | Model with Control | ||
---|---|---|---|---|

Static | Dynamic | Static | Dynamic | |

$rstk\left(-1\right)$ | - | −0.115 *** [0.020] | - | −0.174 *** [0.021] |

$lwti\left(-1\right)$ | - | - | 0.742 *** [0.192] | 0.977 *** [0.192] |

$lexr\left(-1\right)$ | - | - | 0.160 ** [0.068] | 0.357 *** [0.071] |

$lciust{k}_{comp}\left(-1\right)$ | −2.141 *** [0.329] | −1.698 *** [0.336] | −2.236 *** [0.328] | −1.688 *** [0.331] |

$lciust{k}_{comp}\left(-2\right)$ | 3.199 *** [0.601] | 2.390 *** [0.614] | 2.831 *** [0.596] | 1.603 *** [0.608] |

$lciust{k}_{comp}\left(-3\right)$ | 1.047 [0.698] | 1.677 ** [0.702] | 1.364 ** [0.693] | 2.262 *** [0.693] |

$lciust{k}_{comp}\left(-4\right)$ | −5.317 *** [0.661] | −5.322 *** [0.657] | −6.007 *** [0.659] | −6.073 *** [0.650] |

$lciust{k}_{comp}\left(-5\right)$ | 1.492 *** [0.390] | 1.061 *** [0.395] | 1.744 *** [0.388] | 1.195 *** [0.389] |

Joint Coef. Est. | −1.720 *** | −1.892 *** | −2.304 *** | −2.702 *** |

Variables | Model without Control | Model with Control | ||||||
---|---|---|---|---|---|---|---|---|

Static | Dynamic | Static | Dynamic | |||||

Our Predictive Model with Composite Index vs POLS Model with Composite Index | ||||||||

50% Sample | 75% Sample | 50% Sample | 75% Sample | 50% Sample | 75% Sample | 50% Sample | 75% Sample | |

In-Sample | 2.213 *** [0.397] | 3.410 *** [0.541] | 1.278 *** [0.225] | 1.911 *** [0.301] | 3.057 *** [0.449] | 4.768 *** [0.643] | 1.675 *** [0.253] | 2.588 *** [0.361] |

10-Day | 4.462 *** [0.594] | 6.618 *** [0.737] | 0.404 [0.358] | 0.779 * [0.434] | 3.978 *** [0.463] | 6.400 *** [0.669] | 0.979 *** [0.351] | 1.693 *** [0.450] |

20-Day | 3.735 *** [0.656] | 5.627 *** [0.821] | 0.389 [0.361] | 0.713 *[0.431] | 5.437 *** [0.496] | 9.111 *** [0.761] | 1.138 *** [0.351] | 1.835 *** [0.441] |

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## Share and Cite

**MDPI and ACS Style**

Salisu, A.A.; Ogbonna, A.E.; Oloko, T.F.; Adediran, I.A.
A New Index for Measuring Uncertainty Due to the COVID-19 Pandemic. *Sustainability* **2021**, *13*, 3212.
https://doi.org/10.3390/su13063212

**AMA Style**

Salisu AA, Ogbonna AE, Oloko TF, Adediran IA.
A New Index for Measuring Uncertainty Due to the COVID-19 Pandemic. *Sustainability*. 2021; 13(6):3212.
https://doi.org/10.3390/su13063212

**Chicago/Turabian Style**

Salisu, Afees A., Ahamuefula E. Ogbonna, Tirimisiyu F. Oloko, and Idris A. Adediran.
2021. "A New Index for Measuring Uncertainty Due to the COVID-19 Pandemic" *Sustainability* 13, no. 6: 3212.
https://doi.org/10.3390/su13063212