Figure 1.
Evolution of TUNINDEX stock return (2 January 2020–30 December 2022). Source: The Author.
Figure 1.
Evolution of TUNINDEX stock return (2 January 2020–30 December 2022). Source: The Author.
Figure 2.
Wavelet transform coherence: Tunisian stock return volatility versus COVID-19 cases rate. Notes: The black contour shows where the spectrum is significantly different from red noise at the 5% level. The lighter shade represents the cone of influence, marking high-power areas and indicating the autocorrelation of wavelet power at each scale. The horizontal axis represents time from 3 March 2020 to 30 December 2022, and the vertical axis denotes scale bands with daily frequency. Arrows to the right (left) indicate in-phase (out-of-phase) relationships, meaning a positive (negative) connection. If arrows move right and up (down), the first variable “m” (cases rate) drives (follows), while if arrows move left and up (down), variable “n” (TUNINDEX volatility) leads (lags). This visualization helps understand the dynamic relationships between variables. Source: The Author.
Figure 2.
Wavelet transform coherence: Tunisian stock return volatility versus COVID-19 cases rate. Notes: The black contour shows where the spectrum is significantly different from red noise at the 5% level. The lighter shade represents the cone of influence, marking high-power areas and indicating the autocorrelation of wavelet power at each scale. The horizontal axis represents time from 3 March 2020 to 30 December 2022, and the vertical axis denotes scale bands with daily frequency. Arrows to the right (left) indicate in-phase (out-of-phase) relationships, meaning a positive (negative) connection. If arrows move right and up (down), the first variable “m” (cases rate) drives (follows), while if arrows move left and up (down), variable “n” (TUNINDEX volatility) leads (lags). This visualization helps understand the dynamic relationships between variables. Source: The Author.
Figure 3.
Wavelet transform coherence: Tunisian stock return volatility versus COVID-19 death rate. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 23 March 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 3.
Wavelet transform coherence: Tunisian stock return volatility versus COVID-19 death rate. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 23 March 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 4.
Wavelet transform coherence: Tunisian stock return volatility versus stringency index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 4.
Wavelet transform coherence: Tunisian stock return volatility versus stringency index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 5.
Wavelet transform coherence: Tunisian stock return volatility versus containment health index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 5.
Wavelet transform coherence: Tunisian stock return volatility versus containment health index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 6.
Wavelet transform coherence: Tunisian stock return volatility versus economic support index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 6.
Wavelet transform coherence: Tunisian stock return volatility versus economic support index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 7.
Wavelet transform coherence: Tunisian stock return volatility versus government response index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 7.
Wavelet transform coherence: Tunisian stock return volatility versus government response index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 8.
Wavelet transform coherence: Tunisian stock return volatility versus school closing index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 8.
Wavelet transform coherence: Tunisian stock return volatility versus school closing index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 9.
Wavelet transform coherence: Tunisian stock return volatility versus workplace closing. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 9.
Wavelet transform coherence: Tunisian stock return volatility versus workplace closing. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 10.
Wavelet transform coherence: Tunisian stock return volatility versus public events canceling. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 10.
Wavelet transform coherence: Tunisian stock return volatility versus public events canceling. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 11.
Wavelet transform coherence: Tunisian stock return volatility versus public gathering restrictions. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 11.
Wavelet transform coherence: Tunisian stock return volatility versus public gathering restrictions. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 12.
Wavelet transform coherence: Tunisian stock return volatility versus public transport closure. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 12.
Wavelet transform coherence: Tunisian stock return volatility versus public transport closure. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 13.
Wavelet transform coherence: Tunisian stock return volatility versus stay-at-home requirements. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 13.
Wavelet transform coherence: Tunisian stock return volatility versus stay-at-home requirements. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 14.
Wavelet transform coherence: Tunisian stock return volatility versus internal movement restrictions. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 14.
Wavelet transform coherence: Tunisian stock return volatility versus internal movement restrictions. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 15.
Wavelet transform coherence: Tunisian stock return volatility versus international travel control. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 15.
Wavelet transform coherence: Tunisian stock return volatility versus international travel control. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 16.
Wavelet transform coherence: Tunisian stock return volatility versus public information gathering. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 16.
Wavelet transform coherence: Tunisian stock return volatility versus public information gathering. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 17.
Wavelet transform coherence: Tunisian realized volatility versus the COVID-19 cases rate. Notes: The black contour shows where the spectrum is significantly different from red noise at the 5% level. The lighter shade represents the cone of influence, marking high-power areas, indicating autocorrelation of wavelet power at each scale. The horizontal axis represents time from 3 March 2020 to 30 December 2022, and the vertical axis denotes scale bands with daily frequency. Arrows to the right (left) indicate in-phase (out-of-phase) relationships, meaning a positive (negative) connection. If arrows move right and up (down), the first variable “m” (cases rate) drives (follows), while if arrows move left and up (down), variable “n” (TUNINDEX volatility) leads (lags). This visualization helps understand the dynamic relationships between variables. Source: The Author.
Figure 17.
Wavelet transform coherence: Tunisian realized volatility versus the COVID-19 cases rate. Notes: The black contour shows where the spectrum is significantly different from red noise at the 5% level. The lighter shade represents the cone of influence, marking high-power areas, indicating autocorrelation of wavelet power at each scale. The horizontal axis represents time from 3 March 2020 to 30 December 2022, and the vertical axis denotes scale bands with daily frequency. Arrows to the right (left) indicate in-phase (out-of-phase) relationships, meaning a positive (negative) connection. If arrows move right and up (down), the first variable “m” (cases rate) drives (follows), while if arrows move left and up (down), variable “n” (TUNINDEX volatility) leads (lags). This visualization helps understand the dynamic relationships between variables. Source: The Author.
Figure 18.
Wavelet transform coherence: Tunisian realized volatility versus COVID-19 death rate. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 23 March 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 18.
Wavelet transform coherence: Tunisian realized volatility versus COVID-19 death rate. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 23 March 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 19.
Wavelet transform coherence: Tunisian realized volatility versus stringency index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 19.
Wavelet transform coherence: Tunisian realized volatility versus stringency index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 20.
Wavelet transform coherence: Tunisian realized volatility versus containment health index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 20.
Wavelet transform coherence: Tunisian realized volatility versus containment health index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 21.
Wavelet transform coherence: Tunisian realized volatility versus economic support index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 21.
Wavelet transform coherence: Tunisian realized volatility versus economic support index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 22.
Tunisian realized volatility versus government response index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 22.
Tunisian realized volatility versus government response index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 23.
Wavelet transform coherence: Tunisian realized volatility versus school closing index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 23.
Wavelet transform coherence: Tunisian realized volatility versus school closing index. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 24.
Wavelet transform coherence: Tunisian realized volatility versus workplace closing. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 24.
Wavelet transform coherence: Tunisian realized volatility versus workplace closing. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 25.
Wavelet transform coherence: Tunisian stock return volatility versus public events canceling. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 25.
Wavelet transform coherence: Tunisian stock return volatility versus public events canceling. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 26.
Wavelet transform coherence: Tunisian realized volatility versus public gathering restrictions. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 26.
Wavelet transform coherence: Tunisian realized volatility versus public gathering restrictions. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 27.
Wavelet transform coherence: Tunisian realized volatility versus public transport closure. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 27.
Wavelet transform coherence: Tunisian realized volatility versus public transport closure. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 28.
Wavelet transform coherence: Tunisian realized volatility versus stay-at-home requirements. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 28.
Wavelet transform coherence: Tunisian realized volatility versus stay-at-home requirements. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 29.
Wavelet transform coherence: Tunisian realized volatility versus internal movement restrictions. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 29.
Wavelet transform coherence: Tunisian realized volatility versus internal movement restrictions. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 30.
Wavelet transform coherence: Tunisian realized volatility versus international travel control. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 30.
Wavelet transform coherence: Tunisian realized volatility versus international travel control. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 31.
Wavelet transform coherence: Tunisian realized volatility versus public information gathering. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Figure 31.
Wavelet transform coherence: Tunisian realized volatility versus public information gathering. Notes: The black contour identifies areas where the spectrum is statistically significant at the 5% level compared to red noise. The lighter shade designates the cone of influence, outlining high-power regions. Time, ranging from 3 January 2020 to 30 December 2022, is represented on the horizontal axis, while the vertical axis denotes scale bands with daily frequency. Source: The Author.
Table 1.
Data definitions.
Table 1.
Data definitions.
Variable | Symbol | Definition | Source |
---|
TUNINDEX | tun | The daily closing price of the Tunisian market index | https://www.investing.com/indices/tunindex-historical-data, accessed on 1 December 2023 |
Total cases | total_cases | Cumulative number of the confirmed cases due to COVID-19 | Our World in Data https://ourworldindata.org/coronavirus/country/tunisia, accessed on 1 December 2023 |
Total deaths | total_death | Cumulative number of the confirmed death cases due to COVID-19 |
New cases | new_cases | Total number of the new confirmed cases due to COVID-19 |
New deaths | new_death | Total number of new confirmed death cases due to COVID-19 |
Rate of confirmed cases | cases_rate | News cases/Total cases | Calculus |
Rate of deaths | death_rate | Death cases/Total deaths | Calculus |
Stringency index | stringency_index | Ranges from 0 to 100, where higher values indicate stricter government policies and restrictions in response to the COVID-19 pandemic | Oxford COVID-19 Government Response Tracker (OxCGRT) https://github.com/OxCGRT/covid-policy-dataset, accessed on 1 December 2023 |
Containment health index | containement_health_index | Typically ranges from 0 to 100, where higher values indicate better virus containment |
Economic policy index | economic_policy_index | Ranges from 0 to 100, where higher values indicate a more accommodative economic policy stance and lower values indicate a more restrictive stance |
Government response index | government_response_index | Ranges between 0 and 100. A higher score means a better country’s response to the pandemic |
WTI crude oil price | wti_price | Crude oil price (United States) | Energy Information Administration database https://www.eia.gov/dnav/pet/pet_pri_spt_s1_d.htm, accessed on 1 December 2023 |
Interest rate | intn | Key rate if interest | Central bank of Tunisia https://www.bct.gov.tn/bct/siteprod/tableau_statistique_a.jsp?params=PL203260&la=AN, accessed on 1 December 2023 |
School closing | s1 | Sub-indicator 1 of the stringency index | Oxford COVID-19 Government Response Tracker (OxCGRT) https://github.com/OxCGRT/covid-policy-dataset, accessed on 1 December 2023 |
Workplace closing | s2 | Sub-indicator 2 of the stringency index |
Cancel public events | s3 | Sub-indicator 3 of the stringency index |
Restrictions on public gatherings | s4 | Sub indicator 4 of the stringency index |
Closures of public transport | s5 | Sub-indicator 5 of the stringency index |
Stay-at-home requirements | s6 | Sub-indicator 6 of the stringency index |
Restrictions on internal movements | s7 | Sub indicator 7 of the stringency index |
International travel control | s8 | Sub-indicator 8 of the stringency index |
Public information gatherings | s9 | Sub indicator 9 of the stringency index |
Table 2.
Descriptive statistics of the variables.
Table 2.
Descriptive statistics of the variables.
| RETURN_TUN | TOTAL_CASES | TOTAL_DEATHS | STRINGENCY_INDEX | S1 | S2 | S3 | S4 | S5 | S6 | S7 | S8 | S9 | CONTAINMENT_HEALTH_INDEX | ECONOMIC_SUPPORT_INDEX | GOVERNMENT_RESPONSE_INDEX | WTI_OIL_RETURN |
---|
Mean | 0.000150 | 551,839.1 | 16,432.85 | 49.03363 | 1.034043 | 1.144681 | 1.597163 | 3.262411 | 0.307801 | 0.808511 | 0.639716 | 1.876596 | 1.846809 | 50.28391 | 46.84397 | 49.85403 | 0.001816 |
Median | 0.000339 | 603,981.0 | 21,941.00 | 45.31000 | 1.000000 | 1.000000 | 2.000000 | 4.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 2.000000 | 49.96000 | 50.00000 | 48.80000 | 0.002294 |
Maximum | 0.018974 | 1,147,571 | 29,284.00 | 90.74000 | 3.000000 | 3.000000 | 2.000000 | 4.000000 | 2.000000 | 2.000000 | 2.000000 | 4.000000 | 2.000000 | 77.74000 | 75.00000 | 77.40000 | 0.425832 |
Minimum | −0.041859 | 1.000000 | 3.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | −0.281382 |
Std.Dev. | 0.004802 | 444,625.6 | 11,959.82 | 21.88917 | 1.017728 | 0.804146 | 0.757157 | 1.546832 | 0.603267 | 0.967621 | 0.918936 | 1.122006 | 0.490616 | 18.04488 | 25.69635 | 17.32935 | 0.045112 |
Skewness | −2.203420 | 0.060338 | −0.263656 | −0.119352 | 0.546806 | 0.438110 | −1.480724 | −1.632203 | 1.799000 | 0.390066 | 0.772150 | 0.298314 | −3.148689 | −1.074264 | −0.287851 | −1.055407 | 1.353766 |
Kurtosis | 20.12168 | 1.393214 | 1.331061 | 2.671155 | 2.097217 | 2.843062 | 3.384040 | 3.672980 | 4.993379 | 1.183648 | 1.632938 | 2.231494 | 11.52594 | 4.109855 | 1.785051 | 4.541901 | 29.12992 |
Jarque-Bera | 9181.807 | 72.04800 | 83.47809 | 4.850368 | 59.07326 | 23.27648 | 261.9562 | 326.3342 | 497.0006 | 114.7899 | 124.9530 | 27.80539 | 3300.243 | 171.7835 | 53.09627 | 200.7193 | 20,271.78 |
Probability | 0.000000 | 0.000000 | 0.000000 | 0.088462 | 0.000000 | 0.000009 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000001 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
Sum | 0.105709 | 3.68 × 108 | 10,747,083 | 34,568.71 | 729.0000 | 807.0000 | 1126.000 | 2300.000 | 217.0000 | 570.0000 | 451.0000 | 1323.000 | 1302.000 | 35,450.16 | 33,025.00 | 35,147.09 | 1.280245 |
SumSq.Dev. | 0.016235 | 1.31 × 1014 | 9.34 × 1010 | 337,311.6 | 729.1830 | 455.2426 | 403.5943 | 1684.454 | 256.2071 | 659.1489 | 594.4879 | 886.2638 | 169.4553 | 229,234.8 | 464,852.8 | 211,415.7 | 1.432724 |
Observations | 705 | 666 | 654 | 705 | 705 | 705 | 705 | 705 | 705 | 705 | 705 | 705 | 705 | 705 | 705 | 705 | 705 |
Table 3.
Results of ARCH/GARCH family models for the TUNINDEX volatility and oil volatility.
Table 3.
Results of ARCH/GARCH family models for the TUNINDEX volatility and oil volatility.
Panel A: Stock return volatility |
Model | ARCH(1), constant | ARCH(1), AR(1) | ARCH(1), MA(1) | ARCH(1), ARMA(1,1) | GARCH(1,1), constant | GARCH(1,1), AR(1) | GARCH(1,1), MA(1) | GARCH(1,1), ARMA(1,1) |
AIC | −8.248105 | −8.283928 | −8.274759 | −8.281654 | −8.332585 | −8.376225 | −8.365392 | −8.378094 |
SC | −8.229721 | −8.259390 | −8.250246 | −8.250982 | −8.308073 | −8.345553 | −8.334751 | −8.341287 |
Model | EGARCH(1,1), constant | EGARCH(1,1), AR(1) | EGARCH(1,1), MA(1) | EGARCH(1,1), ARMA(1,1) | TGARCH(1,1), constant | TGARCH(1,1), AR(1) | TGARCH(1,1), MA(1) | TGARCH(1,1), ARMA(1,1) |
AIC | −8.323690 | −8.369747 | −8.358517 | −8.369772 | −8.330636 | −8.374358 | −8.363444 | −8.376183 |
SC | −8.293050 | −8.332940 | −8.321748 | −8.326830 | −8.299996 | −8.337551 | −8.326675 | −8.333242 |
Panel B: WTI oil volatility |
Model | ARCH(1), constant | ARCH(1), AR(1) | ARCH(1), MA(1) | ARCH(1), ARMA(1,1) | GARCH(1,1), constant | GARCH(1,1), AR(1) | GARCH(1,1), MA(1) | GARCH(1,1), ARMA(1,1) |
AIC | −3.856832 | −3.990224 | −4.003052 | −4.000366 | −4.272393 | −4.269562 | −4.269562 | −4.257070 |
SC | −3.837436 | −3.964362 | −3.977190 | −3.968039 | −4.246531 | −4.237234 | −4.237235 | −4.231253 |
| EGARCH(1,1), constant | EGARCH(1,1), AR(1) | EGARCH(1,1), MA(1) | EGARCH(1,1), ARMA(1,1) | TGARCH(1,1), constant | TGARCH(1,1), AR(1) | TGARCH(1,1), MA(1) | TGARCH(1,1), ARMA(1,1) |
AIC | −4.278102 | −4.275790 | −4.275864 | −3.344925 | −4.292975 | −4.290166 | −4.290171 | −4.287428 |
SC | −4.245774 | −4.236997 | −4.237071 | −3.299666 | −4.260648 | −4.251373 | −4.251377 | −4.242170 |
Table 4.
Effect of COVID-19 announcements and government intervention on TUNINDEX stock return volatility.
Table 4.
Effect of COVID-19 announcements and government intervention on TUNINDEX stock return volatility.
Eq Name | EQ01 | EQ02 | EQ03 | EQ04 |
---|
cases_rate | 0.000357 *** | 0.000356 *** | 0.000364 *** | 0.000387 *** |
| [3.006] | [2.976] | [3.035] | [3.190] |
death_rate | 0.000049 | 0.000053 | 0.000036 | 0.000009 |
| [0.554] | [0.599] | [0.404] | [0.100] |
condvar_oil | 0.001428 *** | 0.001423 *** | 0.001423 *** | 0.001386 *** |
| [3.233] | [3.213] | [3.239] | [3.202] |
dtintn | −0.000022 | −0.000022 | −0.000023 | −0.000023 |
| [−1.322] | [−1.319] | [−1.320] | [−1.314] |
dstringencyindex | 0.000001 | | | |
| [1.147] | | | |
dcontainmenthealthindex | | 0.000001 | | |
| | [0.961] | | |
dgovernmentresponseindex | | | 0.000001 | |
| | | [1.156] | |
deconomicsupportindex | | | | 0.000001 |
| | | | [1.573] |
_cons | 0.000011 *** | 0.000011 *** | 0.000011 *** | 0.000011 *** |
| [13.036] | [13.057] | [13.480] | [13.810] |
N°observations | 654 | 654 | 654 | 654 |
Table 5.
Effect of stringency index sub-indicators on TUNINDEX stock return volatility.
Table 5.
Effect of stringency index sub-indicators on TUNINDEX stock return volatility.
Eq Name | EQ01 | EQ02 | EQ03 | EQ04 | EQ05 | EQ06 | EQ07 | EQ08 | EQ09 |
---|
cases_rate | 0.000345 *** | 0.000352 *** | 0.000343 *** | 0.000365 *** | 0.000355 *** | 0.000366 *** | 0.000343 *** | 0.000346 *** | 0.000346 *** |
| [2.891] | [2.990] | [2.884] | [3.169] | [2.969] | [3.119] | [2.814] | [2.902] | [2.897] |
death_rate | 0.000078 | 0.000059 | 0.000076 | 0.000024 | 0.000061 | 0.000041 | 0.000082 | 0.000077 | 0.000077 |
| [0.889] | [0.691] | [0.864] | [0.277] | [0.716] | [0.510] | [0.904] | [0.880] | [0.881] |
condvar_oil | 0.001410 *** | 0.001437 *** | 0.001421 *** | 0.001441 *** | 0.001408 *** | 0.001407 *** | 0.001410 *** | 0.001411 *** | 0.001410 *** |
| [3.160] | [3.240] | [3.202] | [3.301] | [3.182] | [3.192] | [3.153] | [3.163] | [3.162] |
dtintn | −0.000021 | −0.000022 | −0.000022 | −0.000023 | −0.000022 | −0.000022 | −0.000021 | −0.000021 | −0.000022 |
| [−1.312] | [−1.319] | [−1.315] | [−1.319] | [−1.312] | [−1.321] | [−1.312] | [−1.311] | [−1.313] |
ds1 | −0.000002 | | | | | | | | |
| [−0.719] | | | | | | | | |
ds2 | | 0.000008 | | | | | | | |
| | [1.313] | | | | | | | |
ds3 | | | 0.000005 | | | | | | |
| | | [0.620] | | | | | | |
ds4 | | | | 0.000011 * | | | | | |
| | | | [1.656] | | | | | |
ds5 | | | | | 0.000014 | | | | |
| | | | | [0.953] | | | | |
ds6 | | | | | | 0.000017 * | | | |
| | | | | | [1.726] | | | |
ds7 | | | | | | | −0.000002 | | |
| | | | | | | [−0.408] | | |
ds8 | | | | | | | | −0.000002 | |
| | | | | | | | [−0.936] | |
ds9 | | | | | | | | | 0.000004 *** |
| | | | | | | | | [5.676] |
_cons | 0.000011 *** | 0.000011 *** | 0.000011 *** | 0.000011 *** | 0.000011 *** | 0.000011 *** | 0.000011 *** | 0.000011 *** | 0.000011 *** |
| [12.382] | [12.880] | [12.258] | [13.564] | [12.662] | [13.170] | [12.560] | [12.351] | [12.386] |
N°observations | 654 | 654 | 654 | 654 | 654 | 654 | 654 | 654 | 654 |
Table 6.
Effect of COVID-19 announcements and government intervention on TUNINDEX stock return TUNINDEX volatility, excluding cases_rate.
Table 6.
Effect of COVID-19 announcements and government intervention on TUNINDEX stock return TUNINDEX volatility, excluding cases_rate.
Eq Name | EQ01 | EQ02 | EQ03 | EQ04 |
---|
death_rate | 0.000215 *** | 0.000220 *** | 0.000215 *** | 0.000209 *** |
| [5.759] | [5.682] | [5.316] | [4.828] |
condvar_oil | 0.001973 *** | 0.001965 *** | 0.001970 *** | 0.001970 *** |
| [2.799] | [2.785] | [2.788] | [2.795] |
dtintn | −0.000028 | −0.000027 | −0.000028 | −0.000028 |
| [−1.392] | [−1.391] | [−1.391] | [−1.390] |
dstringencyindex | 0.000000 | | | |
| [0.701] | | | |
dcontainmenthealthindex | | 0.000000 | | |
| | [0.439] | | |
dgovernmentresponseindex | | | 0.000000 | |
| | | [0.619] | |
deconomicsupportindex | | | | 0.000000 |
| | | | [1.215] |
_cons | 0.000012 *** | 0.000012 *** | 0.000012 *** | 0.000012 *** |
| [14.944] | [15.159] | [15.323] | [15.933] |
N°observations | 654 | 654 | 654 | 654 |
Table 7.
Effect of stringency index sub-indicators on TUNINDEX stock return volatility, excluding cases_rate.
Table 7.
Effect of stringency index sub-indicators on TUNINDEX stock return volatility, excluding cases_rate.
Eq Name | EQ01 | EQ02 | EQ03 | EQ04 | EQ05 | EQ06 | EQ07 | EQ08 | EQ09 |
---|
death_rate | 0.000226 *** | 0.000215 *** | 0.000222 *** | 0.000193 *** | 0.000224 *** | 0.000206 *** | 0.000238 *** | 0.000226 *** | 0.000226 *** |
| [6.233] | [5.829] | [6.233] | [4.742] | [5.767] | [5.692] | [5.896] | [6.174] | [6.171] |
condvar_oil | 0.001955 *** | 0.001981 *** | 0.001967 *** | 0.002001 *** | 0.001958 *** | 0.001977 *** | 0.001941 *** | 0.001957 *** | 0.001957 *** |
| [2.783] | [2.812] | [2.805] | [2.843] | [2.782] | [2.813] | [2.756] | [2.788] | [2.786] |
dtintn | −0.000027 | −0.000028 | −0.000027 | −0.000029 | −0.000027 | −0.000028 | −0.000027 | −0.000027 | −0.000027 |
| [−1.389] | [−1.391] | [−1.391] | [−1.389] | [−1.390] | [−1.392] | [−1.388] | [−1.389] | [−1.391] |
ds1 | −0.000002 | | | | | | | | |
| [−0.823] | | | | | | | | |
ds2 | | 0.000005 | | | | | | | |
| | [1.143] | | | | | | | |
ds3 | | | 0.000008 | | | | | | |
| | | [0.837] | | | | | | |
ds4 | | | | 0.000008 | | | | | |
| | | | [1.495] | | | | | |
ds5 | | | | | 0.000002 | | | | |
| | | | | [0.251] | | | | |
ds6 | | | | | | 0.000012 | | | |
| | | | | | [1.440] | | | |
ds7 | | | | | | | −0.000006 | | |
| | | | | | | [−1.641] | | |
ds8 | | | | | | | | −0.000001 | |
| | | | | | | | [−0.514] | |
ds9 | | | | | | | | | 0.000006 *** |
| | | | | | | | | [9.493] |
_cons | 0.000012 *** | 0.000012 *** | 0.000012 *** | 0.000012 *** | 0.000012 *** | 0.000012 *** | 0.000012 *** | 0.000012 *** | 0.000012 *** |
| [15.148] | [15.368] | [15.045] | [15.484] | [15.329] | [15.293] | [15.283] | [15.150] | [15.153] |
N°observations | 654 | 654 | 654 | 654 | 654 | 654 | 654 | 654 | 654 |
Table 8.
Effect of lagged COVID-19 announcements and government intervention on stock return volatility.
Table 8.
Effect of lagged COVID-19 announcements and government intervention on stock return volatility.
Eq Name | EQ01 | EQ02 | EQ03 | EQ04 |
---|
L.cases_rate | 0.000293 *** | 0.000291 *** | 0.000296 *** | 0.000296 *** |
| [2.766] | [2.734] | [2.738] | [2.722] |
L.death_rate | 0.000062 | 0.000068 | 0.000059 | 0.000066 |
| [0.945] | [1.022] | [0.804] | [0.860] |
L.dtintn | −0.000067 | −0.000067 | −0.000067 | −0.000067 |
| [−0.995] | [−0.994] | [−0.995] | [−0.992] |
L.condvar_oil | 0.001199 *** | 0.001193 *** | 0.001191 *** | 0.001171 *** |
| [3.051] | [3.020] | [3.014] | [2.954] |
L.dstringencyindex | 0.000001 | | | |
| [1.027] | | | |
L.dcontainmenthealthindex | | 0.000001 | | |
| | [0.910] | | |
L.dgovernmentresponseindex | | | 0.000001 | |
| | | [1.000] | |
L.deconomicsupportindex | | | | 0.000000 |
| | | | [0.860] |
_cons | 0.000011 *** | 0.000011 *** | 0.000011 *** | 0.000011 *** |
| [14.284] | [14.329] | [14.519] | [14.734] |
N°observations | 653 | 653 | 653 | 653 |
Table 9.
Effect of lagged stringency index sub-indicators on TUNINDEX stock return volatility.
Table 9.
Effect of lagged stringency index sub-indicators on TUNINDEX stock return volatility.
Eq Name | EQ01 | EQ02 | EQ03 | EQ04 | EQ05 | EQ06 | EQ07 | EQ08 | EQ09 |
---|
L.cases_rate | 0.000281 *** | 0.000287 *** | 0.000272 *** | 0.000303 *** | 0.000283 *** | 0.000292 *** | 0.000272 ** | 0.000281 *** | 0.000281 *** |
| [2.680] | [2.753] | [2.681] | [2.979] | [2.673] | [2.788] | [2.533] | [2.685] | [2.684] |
L.death_rate | 0.000092 * | 0.000074 | 0.000089 | 0.000029 | 0.000088 | 0.000072 | 0.000109 * | 0.000092 * | 0.000092 * |
| [1.680] | [1.262] | [1.607] | [0.389] | [1.429] | [1.211] | [1.749] | [1.673] | [1.673] |
L.dtintn | −0.000066 | −0.000067 | −0.000067 | −0.000069 | −0.000067 | −0.000067 | −0.000066 | −0.000066 | −0.000066 |
| [−0.990] | [−0.993] | [−0.993] | [−0.998] | [−0.990] | [−0.992] | [−0.988] | [−0.990] | [−0.990] |
L.condvar_oil | 0.001180 *** | 0.001206 *** | 0.001217 *** | 0.001217 *** | 0.001180 *** | 0.001179 *** | 0.001178 *** | 0.001180 *** | 0.001180 *** |
| [2.954] | [3.067] | [3.137] | [3.146] | [2.958] | [2.950] | [2.946] | [2.955] | [2.955] |
L.ds1 | −0.000001 | | | | | | | | |
| [−0.472] | | | | | | | | |
L.ds2 | | 0.000008 | | | | | | | |
| | [1.319] | | | | | | | |
L.ds3 | | | 0.000017 | | | | | | |
| | | [1.152] | | | | | | |
L.ds4 | | | | 0.000013 | | | | | |
| | | | [1.527] | | | | | |
L.ds5 | | | | | 0.000004 | | | | |
| | | | | [0.389] | | | | |
L.ds6 | | | | | | 0.000009 | | | |
| | | | | | [1.530] | | | |
L.ds7 | | | | | | | −0.000006 | | |
| | | | | | | [−1.598] | | |
L.ds8 | | | | | | | | −0.000000 | |
| | | | | | | | [−0.088] | |
L.ds9 | | | | | | | | | 0.000004 *** |
| | | | | | | | | [6.110] |
_cons | 0.000011 *** | 0.000011 *** | 0.000011 *** | 0.000011 *** | 0.000011 *** | 0.000011 *** | 0.000011 *** | 0.000011 *** | 0.000011 *** |
| [14.062] | [14.387] | [13.882] | [14.969] | [14.166] | [14.538] | [14.037] | [14.025] | [14.060] |
N°observations | 653 | 653 | 653 | 653 | 653 | 653 | 653 | 653 | 653 |
Table 10.
Effect of lagged COVID-19 announcements and government intervention on TUNINDEX volatility, excluding cases_rate.
Table 10.
Effect of lagged COVID-19 announcements and government intervention on TUNINDEX volatility, excluding cases_rate.
Eq Name: | EQ01 | EQ02 | EQ03 | EQ04 |
---|
L.death_rate | 0.000198 *** | 0.000204 *** | 0.000204 *** | 0.000220 *** |
| [4.836] | [4.907] | [4.680] | [4.147] |
L.dtintn | −0.000072 | −0.000072 | −0.000071 | −0.000071 |
| [−1.021] | [−1.020] | [−1.020] | [−1.018] |
L.condvar_oil | 0.001646 *** | 0.001636 *** | 0.001635 *** | 0.001618 *** |
| [2.786] | [2.767] | [2.762] | [2.728] |
L.dstringencyindex | 0.000000 | | | |
| [0.638] | | | |
L.dcontainmenthealthindex | | 0.000000 | | |
| | [0.465] | | |
L.dgovernmentresponseindex | | | 0.000000 | |
| | | [0.411] | |
L.deconomicsupportindex | | | | −0.000000 |
| | | | [−0.495] |
_cons | 0.000012 *** | 0.000012 *** | 0.000012 *** | 0.000012 *** |
| [16.227] | [16.593] | [16.627] | [18.033] |
N°observations | 653 | 653 | 653 | 653 |
Table 11.
Effect of lagged stringency index sub-indicators on TUNINDEX stock return volatility, excluding cases_rate.
Table 11.
Effect of lagged stringency index sub-indicators on TUNINDEX stock return volatility, excluding cases_rate.
Eq Name | EQ01 | EQ02 | EQ03 | EQ04 | EQ05 | EQ06 | EQ07 | EQ08 | EQ09 |
---|
L.death_rate | 0.000213 *** | 0.000202 *** | 0.000205 *** | 0.000169 *** | 0.000218 *** | 0.000203 *** | 0.000232 *** | 0.000213 *** | 0.000213 *** |
| [5.109] | [4.849] | [5.831] | [3.352] | [4.843] | [4.478] | [5.239] | [5.101] | [5.101] |
L.dtintn | −0.000071 | −0.000072 | −0.000071 | −0.000073 | −0.000071 | −0.000072 | −0.000070 | −0.000071 | −0.000071 |
| [−1.017] | [−1.020] | [−1.020] | [−1.025] | [−1.017] | [−1.020] | [−1.014] | [−1.018] | [−1.018] |
L.condvar_oil | 0.001622 *** | 0.001649 *** | 0.001649 *** | 0.001682 *** | 0.001618 *** | 0.001634 *** | 0.001599 *** | 0.001624 *** | 0.001624 *** |
| [2.742] | [2.794] | [2.810] | [2.847] | [2.732] | [2.753] | [2.703] | [2.744] | [2.744] |
L.ds1 | −0.000002 | | | | | | | | |
| [−0.583] | | | | | | | | |
L.ds2 | | 0.000006 | | | | | | | |
| | [1.054] | | | | | | | |
L.ds3 | | | 0.000020 | | | | | | |
| | | [1.202] | | | | | | |
L.ds4 | | | | 0.000011 | | | | | |
| | | | [1.208] | | | | | |
L.ds5 | | | | | −0.000007 | | | | |
| | | | | [−1.063] | | | | |
L.ds6 | | | | | | 0.000006 | | | |
| | | | | | [1.010] | | | |
L.ds7 | | | | | | | −0.000010 *** | | |
| | | | | | | [−2.704] | | |
L.ds8 | | | | | | | | 0.000001 | |
| | | | | | | | [0.258] | |
L.ds9 | | | | | | | | | 0.000005 *** |
| | | | | | | | | [9.502] |
_cons | 0.000012 *** | 0.000012 *** | 0.000012 *** | 0.000012 *** | 0.000012 *** | 0.000012 *** | 0.000012 *** | 0.000012 *** | 0.000012 *** |
| [17.371] | [17.171] | [17.217] | [16.422] | [17.548] | [17.448] | [17.726] | [17.368] | [17.378] |
N°observations | 653 | 653 | 653 | 653 | 653 | 653 | 653 | 653 | 653 |
Table 12.
Effect of COVID-19 announcements and government intervention on TUNINDEX realized volatility.
Table 12.
Effect of COVID-19 announcements and government intervention on TUNINDEX realized volatility.
Eq. Name | EQ01 | EQ02 | EQ03 | EQ04 |
---|
cases_rate | 0.068686 | 0.067568 | 0.053939 | 0.029122 |
| [0.926] | [0.924] | [0.758] | [0.422] |
death_rate | −0.012786 | −0.011335 | 0.014373 | 0.037779 |
| [−0.168] | [−0.154] | [0.213] | [0.632] |
condvar_oil | 5.350280 *** | 5.354720 *** | 5.355325 *** | 5.413046 *** |
| [11.381] | [11.378] | [11.382] | [11.569] |
dtintn | −0.053879 *** | −0.053868 *** | −0.053194 *** | −0.053067 *** |
| [−9.722] | [−9.753] | [−10.209] | [−10.451] |
dstringencyindex | −0.000839 * | | | |
| [−1.769] | | | |
dcontainmenthealthindex | | −0.001099 * | | |
| | [−1.864] | | |
dgovernmentresponseindex | | | −0.001528 ** | |
| | | [−2.306] | |
deconomicsupportindex | | | | −0.001159 *** |
| | | | [−2.942] |
_cons | 0.053226 *** | 0.053245 *** | 0.053160 *** | 0.053198 *** |
| [40.496] | [40.365] | [40.524] | [40.101] |
N°observations | 654 | 654 | 654 | 654 |
Table 13.
Effect of stringency index sub-indicators on TUNINDEX realized volatility.
Table 13.
Effect of stringency index sub-indicators on TUNINDEX realized volatility.
Eq. Name | EQ01 | EQ02 | EQ03 | EQ04 | EQ05 | EQ06 | EQ07 | EQ08 |
---|
cases_rate | 0.087219 | 0.083637 | 0.064925 | 0.068031 | 0.079253 | 0.076295 | 0.087134 | 0.086742 |
| [1.128] | [1.094] | [0.891] | [0.939] | [1.044] | [0.983] | [1.126] | [1.121] |
death_rate | −0.058369 | −0.049018 | 0.004556 | −0.026478 | −0.043969 | −0.038608 | −0.057719 | −0.057569 |
| [−0.671] | [−0.595] | [0.066] | [−0.358] | [−0.552] | [−0.450] | [−0.661] | [−0.659] |
condvar_oil | 5.379272 *** | 5.366026 *** | 5.342108 *** | 5.382491 *** | 5.379450 *** | 5.375517 *** | 5.378758 *** | 5.378363 *** |
| [11.439] | [11.389] | [11.388] | [11.506] | [11.487] | [11.464] | [11.435] | [11.435] |
dtintn | −0.055314 *** | −0.055010 *** | −0.053278 *** | −0.054589 *** | −0.054937 *** | −0.054799 *** | −0.055269 *** | −0.055289 *** |
| [−8.754] | [−9.013] | [−10.098] | [−9.393] | [−9.095] | [−9.103] | [−8.741] | [−8.751] |
ds1 | 0.001770 | | | | | | | |
| [0.315] | | | | | | | |
ds2 | | −0.003696 | | | | | | |
| | [−0.508] | | | | | | |
ds4 | | | −0.013077 *** | | | | | |
| | | [−2.689] | | | | | |
ds5 | | | | −0.028130 | | | | |
| | | | [−1.534] | | | | |
ds6 | | | | | −0.006273 | | | |
| | | | | [−0.690] | | | |
ds7 | | | | | | −0.007234 | | |
| | | | | | [−0.638] | | |
ds8 | | | | | | | −0.003069 | |
| | | | | | | [−0.521] | |
ds9 | | | | | | | | −0.001666 |
| | | | | | | | [−1.204] |
_cons | 0.053456 *** | 0.053411 *** | 0.053218 *** | 0.053419 *** | 0.053391 *** | 0.053376 *** | 0.053420 *** | 0.053444 *** |
| [39.344] | [39.590] | [39.950] | [39.779] | [39.606] | [40.035] | [39.181] | [39.188] |
N°observations | 654 | 654 | 654 | 654 | 654 | 654 | 654 | 654 |
Table 14.
Effect of COVID-19 announcements and government intervention on realized TUNINDEX volatility, excluding cases_rate.
Table 14.
Effect of COVID-19 announcements and government intervention on realized TUNINDEX volatility, excluding cases_rate.
Eq. Name | EQ01 | EQ02 | EQ03 | EQ04 |
---|
death_rate | 0.019156 | 0.020295 | 0.040883 | 0.052863 |
| [0.329] | [0.359] | [0.835] | [1.300] |
condvar_oil | 5.455116 *** | 5.457813 *** | 5.436255 *** | 5.457014 *** |
| [12.156] | [12.133] | [12.064] | [12.163] |
dtintn | −0.054896 *** | −0.054860 *** | −0.053928 *** | −0.053430 *** |
| [−9.040] | [−9.122] | [−9.814] | [−10.357] |
dstringencyindex | −0.000894 * | | | |
| [−1.855] | | | |
dcontainmenthealthindex | | −0.001174 * | | |
| | [−1.936] | | |
dgovernmentresponseindex | | | −0.001616 ** | |
| | | [−2.387] | |
deconomicsupportindex | | | | −0.001201 *** |
| | | | [−3.080] |
_cons | 0.053428 *** | 0.053444 *** | 0.053311 *** | 0.053275 *** |
| [41.703] | [41.674] | [41.988] | [41.598] |
N°observations | 654 | 654 | 654 | 654 |
Table 15.
Effect of stringency index sub-indicators on TUNINDEX realized volatility, excluding cases_rate.
Table 15.
Effect of stringency index sub-indicators on TUNINDEX realized volatility, excluding cases_rate.
Eq. Name | EQ01 | EQ02 | EQ03 | EQ04 | EQ05 | EQ06 | EQ07 | EQ08 | EQ09 |
---|
death_rate | −0.020821 | −0.011808 | −0.019509 | 0.034642 | 0.004763 | −0.008266 | −0.004016 | −0.020268 | −0.020288 |
| [−0.282] | [−0.176] | [−0.259] | [0.650] | [0.082] | [−0.130] | [−0.059] | [−0.273] | [−0.274] |
condvar_oil | 5.516901 *** | 5.495188 *** | 5.512694 *** | 5.441774 *** | 5.487786 *** | 5.502996 *** | 5.493677 *** | 5.516354 *** | 5.515391 *** |
| [12.320] | [12.315] | [12.282] | [12.177] | [12.281] | [12.364] | [12.245] | [12.314] | [12.311] |
dtintn | −0.056744 *** | −0.056326 *** | −0.056678 *** | −0.054246 *** | −0.055619 *** | −0.056163 *** | −0.055970 *** | −0.056702 *** | −0.056714 *** |
| [−7.852] | [−8.183] | [−7.843] | [−9.531] | [−8.769] | [−8.326] | [−8.314] | [−7.840] | [−7.852] |
ds1 | 0.001593 | | | | | | | | |
| [0.289] | | | | | | | | |
ds2 | | −0.004341 | | | | | | | |
| | [−0.578] | | | | | | | |
ds3 | | | −0.002024 | | | | | | |
| | | [−0.264] | | | | | | |
ds4 | | | | −0.013618 ** | | | | | |
| | | | [−2.546] | | | | | |
ds5 | | | | | −0.030568 | | | | |
| | | | | [−1.572] | | | | |
ds6 | | | | | | −0.007261 | | | |
| | | | | | [−0.774] | | | |
ds7 | | | | | | | −0.008131 | | |
| | | | | | | [−0.730] | | |
ds8 | | | | | | | | −0.002860 | |
| | | | | | | | [−0.486] | |
ds9 | | | | | | | | | −0.001291 |
| | | | | | | | | [−0.952] |
_cons | 0.053735 *** | 0.053671 *** | 0.053719 *** | 0.053414 *** | 0.053629 *** | 0.053633 *** | 0.053608 *** | 0.053701 *** | 0.053723 *** |
| [40.029] | [40.443] | [39.983] | [41.026] | [40.949] | [40.565] | [41.092] | [39.875] | [39.869] |
N°observations | 654 | 654 | 654 | 654 | 654 | 654 | 654 | 654 | 654 |
Table 16.
Effect of lagged COVID-19 announcements and government intervention on realized volatility.
Table 16.
Effect of lagged COVID-19 announcements and government intervention on realized volatility.
Eq Name | EQ01 | EQ02 | EQ03 | EQ04 |
---|
L.cases_rate | −0.034246 | −0.035573 | −0.049672 | −0.042196 |
| [−0.502] | [−0.529] | [−0.722] | [−0.656] |
L.death_rate | 0.129279 ** | 0.130501 ** | 0.154870 *** | 0.118714 ** |
| [2.189] | [2.253] | [2.663] | [2.381] |
L.condvar_oil | 5.316555 *** | 5.324149 *** | 5.330161 *** | 5.383070 *** |
| [11.095] | [11.087] | [11.078] | [11.263] |
L.dtintn | −0.018730 ** | −0.018746 ** | −0.018154 ** | −0.019561 ** |
| [−2.062] | [−2.072] | [−2.108] | [−2.070] |
L.dstringencyindex | −0.001331 ** | | | |
| [−2.113] | | | |
L.dcontainmenthealthindex | | −0.001719 ** | | |
| | [−2.157] | | |
L.dgovernmentresponseindex | | | −0.002053 ** | |
| | | [−2.477] | |
L.deconomicsupportindex | | | | −0.000735 ** |
| | | | [−2.454] |
_cons | 0.052887 *** | 0.052922 *** | 0.052852 *** | 0.053080 *** |
| [40.851] | [40.727] | [40.647] | [39.950] |
N°observations | 653 | 653 | 653 | 653 |
Table 17.
Effect of lagged stringency index sub-indicators on TUNINDEX realized volatility.
Table 17.
Effect of lagged stringency index sub-indicators on TUNINDEX realized volatility.
Eq Name | Eq01 | Eq02 | Eq03 | Eq04 | Eq05 | Eq06 | Eq07 | Eq08 | Eq09 |
---|
L.cases_rate | −0.006974 | −0.015687 | 0.002751 | −0.028292 | −0.033772 | −0.012208 | −0.024125 | −0.005724 | −0.005711 |
| [−0.106] | [−0.242] | [0.041] | [−0.402] | [−0.513] | [−0.188] | [−0.362] | [−0.088] | [−0.088] |
L.death_rate | 0.060258 | 0.086174 | 0.060787 | 0.122789 * | 0.105019 * | 0.070166 | 0.091852 | 0.058229 | 0.058208 |
| [0.917] | [1.443] | [0.908] | [1.954] | [1.801] | [1.156] | [1.454] | [0.903] | [0.903] |
L.condvar_oil | 5.358549 *** | 5.320837 *** | 5.327282 *** | 5.323396 *** | 5.367313 *** | 5.362021 *** | 5.356067 *** | 5.360940 *** | 5.360934 *** |
| [11.221] | [10.980] | [11.122] | [11.105] | [11.253] | [11.244] | [11.240] | [11.214] | [11.213] |
L.dtintn | −0.020915 ** | −0.020051 ** | −0.020541 ** | −0.018879 ** | −0.019915 ** | −0.020661 ** | −0.020098 ** | −0.020977 ** | −0.020977 ** |
| [−1.971] | [−2.012] | [−1.977] | [−2.082] | [−2.061] | [−1.992] | [−2.014] | [−1.975] | [−1.975] |
L.ds1 | −0.004532 | | | | | | | | |
| [−0.918] | | | | | | | | |
L.ds2 | | −0.012094 | | | | | | | |
| | [−1.170] | | | | | | | |
L.ds3 | | | −0.015882 | | | | | | |
| | | [−0.871] | | | | | | |
L.ds4 | | | | −0.013596 | | | | | |
| | | | [−1.397] | | | | | |
L.ds5 | | | | | −0.042358 | | | | |
| | | | | [−1.404] | | | | |
L.ds6 | | | | | | −0.005518 | | | |
| | | | | | [−0.612] | | | |
L.ds7 | | | | | | | −0.012838 | | |
| | | | | | | [−1.331] | | |
L.ds8 | | | | | | | | 0.000494 | |
| | | | | | | | [0.085] | |
L.ds9 | | | | | | | | | 0.002428 * |
| | | | | | | | | [1.781] |
_cons | 0.053215 *** | 0.053121 *** | 0.053162 *** | 0.053000 *** | 0.053196 *** | 0.053189 *** | 0.053113 *** | 0.053243 *** | 0.053243 *** |
| [39.881] | [40.360] | [40.182] | [40.296] | [40.156] | [40.121] | [40.187] | [39.679] | [39.727] |
N°observations | 653 | 653 | 653 | 653 | 653 | 653 | 653 | 653 | 653 |
Table 18.
Effect of lagged COVID-19 announcements and government intervention on TUNINDEX volatility, excluding cases_rate.
Table 18.
Effect of lagged COVID-19 announcements and government intervention on TUNINDEX volatility, excluding cases_rate.
Eq Name | EQ01 | EQ02 | EQ03 | EQ04 |
---|
L.death_rate | 0.113355 *** | 0.113849 *** | 0.130459 *** | 0.096859 *** |
| [2.728] | [2.736] | [3.015] | [2.907] |
L.condvar_oil | 5.264298 *** | 5.269886 *** | 5.255650 *** | 5.319379 *** |
| [11.110] | [11.085] | [11.038] | [11.269] |
L.dtintn | −0.018223 ** | −0.018224 ** | −0.017477 ** | −0.019035 ** |
| [−2.075] | [−2.087] | [−2.124] | [−2.082] |
L.dstringencyindex | −0.001304 ** | | | |
| [−2.114] | | | |
L.dcontainmenthealthindex | | −0.001680 ** | | |
| | [−2.138] | | |
L.dgovernmentresponseindex | | | −0.001972 ** | |
| | | [−2.429] | |
L.deconomicsupportindex | | | | −0.000674 ** |
| | | | [−2.561] |
_cons | 0.052786 *** | 0.052817 *** | 0.052714 *** | 0.052967 *** |
| [42.808] | [42.762] | [42.614] | [41.712] |
N°observations | 653 | 653 | 653 | 653 |
Table 19.
Effect of lagged stringency index sub-indicators on TUNINDEX realized volatility, excluding cases_rate.
Table 19.
Effect of lagged stringency index sub-indicators on TUNINDEX realized volatility, excluding cases_rate.
Eq Name | EQ01 | EQ02 | EQ03 | EQ04 | EQ05 | EQ06 | EQ07 | EQ08 | EQ09 |
---|
L.death_rate | 0.057256 | 0.079196 * | 0.061959 | 0.109680 ** | 0.089511 ** | 0.064666 | 0.080914 * | 0.055769 | 0.055753 |
| [1.201] | [1.852] | [1.233] | [2.486] | [2.261] | [1.522] | [1.865] | [1.187] | [1.187] |
L.condvar_oil | 5.347547 *** | 5.296617 *** | 5.331660 *** | 5.279976 *** | 5.315056 *** | 5.342994 *** | 5.318713 *** | 5.351903 *** | 5.351914 *** |
| [11.370] | [11.077] | [11.322] | [11.178] | [11.279] | [11.358] | [11.277] | [11.373] | [11.373] |
L.dtintn | −0.020801 ** | −0.019804 ** | −0.020586 ** | −0.018457 ** | −0.019404 ** | −0.020472 ** | −0.019728 ** | −0.020882 ** | −0.020883 ** |
| [−1.970] | [−2.018] | [−1.973] | [−2.091] | [−2.071] | [−1.995] | [−2.019] | [−1.974] | [−1.974] |
L.ds1 | −0.004518 | | | | | | | | |
| [−0.919] | | | | | | | | |
L.ds2 | | −0.011973 | | | | | | | |
| | [−1.167] | | | | | | | |
L.ds3 | | | −0.015857 | | | | | | |
| | | [−0.869] | | | | | | |
L.ds4 | | | | −0.013361 | | | | | |
| | | | [−1.416] | | | | | |
L.ds5 | | | | | −0.041148 | | | | |
| | | | | [−1.403] | | | | |
L.ds6 | | | | | | −0.005365 | | | |
| | | | | | [−0.605] | | | |
L.ds7 | | | | | | | −0.012554 | | |
| | | | | | | [−1.328] | | |
L.ds8 | | | | | | | | 0.000480 | |
| | | | | | | | [0.083] | |
L.ds9 | | | | | | | | | 0.002403 * |
| | | | | | | | | [1.869] |
_cons | 0.053193 *** | 0.053072 *** | 0.053171 *** | 0.052915 *** | 0.053091 *** | 0.053152 *** | 0.053039 *** | 0.053224 *** | 0.053225 *** |
| [41.537] | [42.288] | [41.711] | [41.965] | [42.153] | [41.960] | [42.029] | [41.356] | [41.397] |
N°observations | 653 | 653 | 653 | 653 | 653 | 653 | 653 | 653 | 653 |
Table 20.
Long-run and short-run results of uncertainty impact on TUNINDEX conditional volatility.
Table 20.
Long-run and short-run results of uncertainty impact on TUNINDEX conditional volatility.
Using EPU and VIX | Levels Equation |
Case 2: Restricted Constant and No Trend |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
EPU | −1.10 × 10−8 | 2.72 × 10−8 | −0.406081 | 0.6848 |
VIX | 2.44 × 10−6 | 9.45 × 10−7 | 2.576858 | 0.0102 |
C | −4.02 × 10−5 | 1.99 × 10−5 | −2.022307 | 0.0435 |
EC = CONDVAR_TUN − (−0.0000 × EPU + 0.0000 × VIX − 0.0000) |
F-Bounds Test | Null Hypothesis: No levels of relationship |
Test Statistic | Value | Signif. | I(0) | I(1) |
| | | Asymptotic: n = 1000 | |
F-statistic | 20.22501 | 10% | 2.63 | 3.35 |
k | 2 | 5% | 3.1 | 3.87 |
| | 2.5% | 3.55 | 4.38 |
| | 1% | 4.13 | 5 |
ECM Regression |
Case 2: Restricted Constant and No Trend |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
D(CONDVAR_TUN(−1)) | 0.305200 | 0.036072 | 8.460905 | 0.0000 |
D(CONDVAR_TUN(−2)) | 0.163597 | 0.037889 | 4.317848 | 0.0000 |
D(CONDVAR_TUN(−3)) | −0.135407 | 0.038054 | −3.558262 | 0.0004 |
D(CONDVAR_TUN(−4)) | 0.042966 | 0.036567 | 1.174983 | 0.2404 |
D(CONDVAR_TUN(−5)) | 0.156755 | 0.036535 | 4.290596 | 0.0000 |
D(CONDVAR_TUN(−6)) | −0.064229 | 0.036672 | −1.751465 | 0.0803 |
D(VIX) | 4.99 × 10−7 | 1.75 × 10−7 | 2.846085 | 0.0046 |
D(VIX(−1)) | 5.59 × 10−7 | 1.83 × 10−7 | 3.060780 | 0.0023 |
CointEq(−1) * | −0.214080 | 0.023749 | −9.014119 | 0.0000 |
Using EMU + VIX | Levels Equation |
Case 2: Restricted Constant and No Trend |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
EMU | 1.02 × 10−7 | 5.16 × 10−8 | 1.982838 | 0.0478 |
VIX | 1.33 × 10−6 | 5.94 × 10−7 | 2.233982 | 0.0258 |
C | −2.63 × 10−5 | 1.49 × 10−5 | −1.769359 | 0.0773 |
EC = CONDVAR_TUN − (0.0000 × EMU + 0.0000 × VIX − 0.0000) |
F-Bounds Test | Null Hypothesis: No levels relationship |
Test Statistic | Value | Signif. | I(0) | I(1) |
| | | Asymptotic: n = 1000 | |
F-statistic | 16.68016 | 10% | 2.63 | 3.35 |
k | 2 | 5% | 3.1 | 3.87 |
| | 2.5% | 3.55 | 4.38 |
| | 1% | 4.13 | 5 |
ECM Regression |
Case 2: Restricted Constant and No Trend |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
D(CONDVAR_TUN(−1)) | 0.280360 | 0.037720 | 7.432660 | 0.0000 |
D(CONDVAR_TUN(−2)) | 0.169588 | 0.039402 | 4.303995 | 0.0000 |
D(CONDVAR_TUN(−3)) | −0.147548 | 0.039403 | −3.744638 | 0.0002 |
D(CONDVAR_TUN(−4)) | 0.033769 | 0.037466 | 0.901324 | 0.3677 |
D(CONDVAR_TUN(−5)) | 0.153700 | 0.037312 | 4.119377 | 0.0000 |
D(CONDVAR_TUN(−6)) | −0.080614 | 0.037319 | −2.160150 | 0.0311 |
D(EMU) | −5.65 × 10−9 | 6.22 × 10−9 | −0.908323 | 0.3640 |
D(EMU(−1)) | −2.82 × 10−8 | 7.75 × 10−9 | −3.638482 | 0.0003 |
D(EMU(−2)) | −2.49 × 10−8 | 8.39 × 10−9 | −2.968032 | 0.0031 |
D(EMU(−3)) | −3.79 × 10−8 | 8.57 × 10−9 | −4.418311 | 0.0000 |
D(EMU(−4)) | −2.99 × 10−8 | 8.34 × 10−9 | −3.587680 | 0.0004 |
D(EMU(−5)) | −3.58 × 10−9 | 7.60 × 10−9 | −0.471456 | 0.6375 |
D(EMU(−6)) | 1.44 × 10−8 | 6.23 × 10−9 | 2.315235 | 0.0209 |
D(VIX) | 4.39 × 10−7 | 1.75 × 10−7 | 2.513163 | 0.0122 |
D(VIX(−1)) | 7.64 × 10−7 | 1.87 × 10−7 | 4.091321 | 0.0000 |
D(VIX(−2)) | 3.64 × 10−7 | 1.88 × 10−7 | 1.933189 | 0.0536 |
D(VIX(−3)) | 4.45 × 10−7 | 1.89 × 10−7 | 2.355937 | 0.0188 |
D(VIX(−4)) | 2.90 × 10−7 | 1.90 × 10−7 | 1.523589 | 0.1281 |
D(VIX(−5)) | 2.81 × 10−7 | 1.89 × 10−7 | 1.489922 | 0.1367 |
D(VIX(−6)) | 4.49 × 10−7 | 1.80 × 10−7 | 2.488358 | 0.0131 |
CointEq(−1) * | −0.208323 | 0.025447 | −8.186456 | 0.0000 |
Using IDEMV + VIX | Levels Equation |
Case 2: Restricted Constant and No Trend |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
IMEDV | 7.74 × 10−7 | 3.47 × 10−7 | 2.230579 | 0.0260 |
VIX | 1.56 × 10−6 | 6.78 × 10−7 | 2.293725 | 0.0221 |
C | −3.15 × 10−5 | 1.69 × 10−5 | −1.860152 | 0.0633 |
EC = CONDVAR_TUN − (0.0000 × IMEDV + 0.0000 × VIX − 0.0000) |
F-Bounds Test | Null Hypothesis: No levels relationship |
Test Statistic | Value | Signif. | I(0) | I(1) |
| | | Asymptotic: n = 1000 | |
F-statistic | 16.16596 | 10% | 2.63 | 3.35 |
k | 2 | 5% | 3.1 | 3.87 |
| | 2.5% | 3.55 | 4.38 |
| | 1% | 4.13 | 5 |
ECM Regression |
Case 2: Restricted Constant and No Trend |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
D(CONDVAR_TUN(−1)) | 0.294640 | 0.038541 | 7.644852 | 0.0000 |
D(CONDVAR_TUN(−2)) | 0.156728 | 0.040128 | 3.905726 | 0.0001 |
D(CONDVAR_TUN(−3)) | −0.148119 | 0.039993 | −3.703629 | 0.0002 |
D(CONDVAR_TUN(−4)) | 0.032052 | 0.037988 | 0.843737 | 0.3991 |
D(CONDVAR_TUN(−5)) | 0.149479 | 0.037947 | 3.939136 | 0.0001 |
D(CONDVAR_TUN(−6)) | −0.066308 | 0.037763 | −1.755902 | 0.0796 |
D(IMEDV) | 4.17 × 10−8 | 6.49 × 10−8 | 0.642959 | 0.5205 |
D(IDEMV(−1)) | −1.54 × 10−7 | 8.11 × 10−8 | −1.893384 | 0.0587 |
D(IDEMV(−2)) | −1.26 × 10−7 | 8.97 × 10−8 | −1.409433 | 0.1592 |
D(IDEMV(−3)) | −1.98 × 10−7 | 9.24 × 10−8 | −2.139318 | 0.0328 |
D(IDEMV(−4)) | −1.67 × 10−7 | 8.99 × 10−8 | −1.854306 | 0.0641 |
D(IDEMV(−5)) | −5.06 × 10−8 | 8.15 × 10−8 | −0.620560 | 0.5351 |
D(IDEMV(−6)) | 1.28 × 10−7 | 6.64 × 10−8 | 1.925914 | 0.0545 |
D(VIX) | 4.08 × 10−7 | 1.81 × 10−7 | 2.259421 | 0.0242 |
D(VIX(−1)) | 7.04 × 10−7 | 1.95 × 10−7 | 3.607629 | 0.0003 |
D(VIX(−2)) | 3.73 × 10−7 | 1.98 × 10−7 | 1.889694 | 0.0592 |
D(VIX(−3)) | 3.89 × 10−7 | 1.98 × 10−7 | 1.966048 | 0.0497 |
D(VIX(−4)) | 1.44 × 10−7 | 1.99 × 10−7 | 0.724840 | 0.4688 |
D(VIX(−5)) | 2.50 × 10−7 | 1.94 × 10−7 | 1.290037 | 0.1975 |
D(VIX(−6)) | 4.65 × 10−7 | 1.84 × 10−7 | 2.522034 | 0.0119 |
CointEq(−1) * | −0.212775 | 0.026401 | −8.059500 | 0.0000 |
Table 21.
Long-run and short-run results of uncertainty impact on TUNINDEX realized volatility.
Table 21.
Long-run and short-run results of uncertainty impact on TUNINDEX realized volatility.
Using EPU and VIX | Levels Equation |
Case 2: Restricted Constant and No Trend |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
EPU | 0.000152 | 6.74 × 10−5 | 2.255491 | 0.0244 |
VIX | 0.002621 | 0.001188 | 2.205860 | 0.0277 |
C | −0.031637 | 0.025702 | −1.230915 | 0.2188 |
EC = RV − (0.0002 × EPU + 0.0026 × VIX − 0.0316) |
F-Bounds Test | Null Hypothesis: No levels relationship |
Test Statistic | Value | Signif. | I(0) | I(1) |
| | | Asymptotic: n = 1000 | |
F-statistic | 9.062312 | 10% | 2.63 | 3.35 |
k | 2 | 5% | 3.1 | 3.87 |
| | 2.5% | 3.55 | 4.38 |
| | 1% | 4.13 | 5 |
ECM Regression |
Case 2: Restricted Constant and No Trend |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
D(RV(−1)) | −0.832247 | 0.038215 | −21.77803 | 0.0000 |
D(RV(−2)) | −0.850705 | 0.047015 | −18.09418 | 0.0000 |
D(RV(−3)) | −0.621916 | 0.051961 | −11.96900 | 0.0000 |
D(RV(−4)) | −0.471166 | 0.046596 | −10.11176 | 0.0000 |
D(RV(−5)) | 0.098257 | 0.036586 | 2.685628 | 0.0074 |
D(EPU) | −3.38 × 10−5 | 8.59 × 10−6 | −0.393325 | 0.6942 |
D(EPU(−1)) | −3.14 × 10−5 | 9.86 × 10−6 | −3.179622 | 0.0015 |
D(EPU(−2)) | −1.77 × 10−5 | 8.65 × 10−6 | −2.041472 | 0.0416 |
D(VIX) | −6.80 × 10−5 | 0.000204 | −0.333057 | 0.7392 |
D(VIX(−1)) | 0.000629 | 0.000212 | 2.966445 | 0.0031 |
D(VIX(−2)) | 7.76 × 10−5 | 0.000213 | 0.363678 | 0.7162 |
D(VIX(−3)) | 8.51 × 10−5 | 0.000214 | 0.397124 | 0.6914 |
D(VIX(−4)) | −2.47 × 10−5 | 0.000214 | −0.115518 | 0.9081 |
D(VIX(−5)) | 0.000936 | 0.000207 | 4.516407 | 0.0000 |
CointEq(−1) * | −0.131308 | 0.021761 | −6.033982 | 0.0000 |
Using EMU + VIX | Levels Equation |
Case 4: Unrestricted Constant and Restricted Trend |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
EMU | 0.000250 | 0.000148 | 1.683678 | 0.0927 |
VIX | 0.001862 | 0.001029 | 1.809186 | 0.0709 |
@TREND | −3.65 × 10−5 | 1.91 × 10−5 | −1.910376 | 0.0565 |
EC = RV − (0.0002 × EMU + 0.0019 × VIX − 0.0000 × @TREND) |
F-Bounds Test | Null Hypothesis: No levels relationship |
Test Statistic | Value | Signif. | I(0) | I(1) |
| | | Asymptotic: n = 1000 | |
F-statistic | 9.754886 | 10% | 3.38 | 4.02 |
k | 2 | 5% | 3.88 | 4.61 |
| | 2.5% | 4.37 | 5.16 |
| | 1% | 4.99 | 5.85 |
ECM Regression |
Case 4: Unrestricted Constant and Restricted Trend |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
C | −2.36 × 10−5 | 0.000514 | −0.045975 | 0.9633 |
D(RV(−1)) | −0.841176 | 0.037294 | −22.55528 | 0.0000 |
D(RV(−2)) | −0.865885 | 0.046192 | −18.74517 | 0.0000 |
D(RV(−3)) | −0.655573 | 0.051174 | −12.81059 | 0.0000 |
D(RV(−4)) | −0.493393 | 0.046515 | −10.60725 | 0.0000 |
D(RV(−5)) | 0.082368 | 0.036446 | 2.260035 | 0.0241 |
D(EMU) | −1.53 × 10−5 | 7.15 × 10−6 | −2.134200 | 0.0332 |
D(EMU(−1)) | −3.15 × 10−5 | 9.11 × 10−6 | −3.456614 | 0.0006 |
D(EMU(−2)) | −2.91 × 10−5 | 9.58 × 10−6 | −3.033014 | 0.0025 |
D(EMU(−3)) | −3.51 × 10−5 | 9.37 × 10−6 | −3.739097 | 0.0002 |
D(EMU(−4)) | −2.67 × 10−5 | 8.57 × 10−6 | −3.115008 | 0.0019 |
D(EMU(−5)) | −1.93 × 10−5 | 7.14 × 10−6 | −2.709524 | 0.0069 |
D(VIX) | −0.000137 | 0.000202 | −0.676110 | 0.4992 |
D(VIX(−1)) | 0.000697 | 0.000215 | 3.243837 | 0.0012 |
D(VIX(−2)) | 1.96 × 10−5 | 0.000214 | 0.091599 | 0.9270 |
D(VIX(−3)) | 0.000108 | 0.000217 | 0.498118 | 0.6186 |
D(VIX(−4)) | 2.34 × 10−5 | 0.000217 | 0.107871 | 0.9141 |
D(VIX(−5)) | 0.000943 | 0.000210 | 4.494051 | 0.0000 |
CointEq(−1) * | −0.121561 | 0.019417 | −6.260388 | 0.0000 |
Using IMEDV + VIX | Levels Equation |
Case 4: Unrestricted Constant and Restricted Trend |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
IMEDV | −0.000920 | 0.000998 | −0.921465 | 0.3571 |
VIX | 0.004672 | 0.001739 | 2.687171 | 0.0074 |
@TREND | −6.39 × 10−5 | 2.62 × 10−5 | −2.444031 | 0.0148 |
EC = RV − (−0.0009 × IMEDV + 0.0047 × VIX − 0.0001 × @TREND) |
F-Bounds Test | Null Hypothesis: No levels relationship |
Test Statistic | Value | Signif. | I(0) | I(1) |
| | | Asymptotic: n = 1000 | |
F-statistic | 9.035485 | 10% | 3.38 | 4.02 |
k | 2 | 5% | 3.88 | 4.61 |
| | 2.5% | 4.37 | 5.16 |
| | 1% | 4.99 | 5.85 |
ECM Regression |
Case 4: Unrestricted Constant and Restricted Trend |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
C | −0.002070 | 0.000614 | −3.371047 | 0.0008 |
D(RV(−1)) | −0.822980 | 0.038000 | −21.65732 | 0.0000 |
D(RV(−2)) | −0.841194 | 0.046688 | −18.01748 | 0.0000 |
D(RV(−3)) | −0.611366 | 0.051893 | −11.78118 | 0.0000 |
D(RV(−4)) | −0.459613 | 0.046307 | −9.925402 | 0.0000 |
D(RV(−5)) | 0.097188 | 0.036423 | 2.668281 | 0.0078 |
D(IMEDV) | −0.000319 | 6.09 × 10−5 | −5.234177 | 0.0000 |
D(VIX) | −6.01 × 10−5 | 0.000202 | −0.298059 | 0.7657 |
D(VIX(−1)) | 0.000527 | 0.000217 | 2.429744 | 0.0154 |
D(VIX(−2)) | −0.000155 | 0.000216 | −0.718291 | 0.4728 |
D(VIX(−3)) | −9.15 × 10−5 | 0.000214 | −0.426881 | 0.6696 |
D(VIX(−4)) | −7.96 × 10−5 | 0.000215 | −0.370724 | 0.7110 |
D(VIX(−5)) | 0.000739 | 0.000214 | 3.446990 | 0.0006 |
D(VIX(−6)) | −0.000345 | 0.000210 | −1.642774 | 0.1009 |
CointEq(−1) * | −0.110925 | 0.018411 | −6.025102 | 0.0000 |