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Table 5 The causality test in linear model

From: Are life insurance futures a safe haven during COVID-19?

Insurance company

L

Model

\({\varvec{H}}_{{\varvec{0}}} {\varvec{:dm}}\xrightarrow{{\varvec{ \times }}}{\varvec{dp}}\) \(\left( {\sum\nolimits_{{{\varvec{i = 1}}}}^{{\varvec{L}}} {{\varvec{\alpha }}_{{{\varvec{2i}}}} } } \right)\)

\({\varvec{H}}_{{\varvec{0}}} {\varvec{:dc}}\xrightarrow{{\varvec{ \times }}}{\varvec{dp}}\) \(\left( {\sum\nolimits_{{{\varvec{i = 1}}}}^{{\varvec{L}}} {{\varvec{\alpha }}_{{{\varvec{3i}}}} } } \right)\)

\({\varvec{H}}_{{\varvec{0}}} {\varvec{:dp}}\xrightarrow{{\varvec{ \times }}}{\varvec{dm}}\) \(\left( {\sum\nolimits_{{{\varvec{i = 1}}}}^{{\varvec{L}}} {{\varvec{\beta }}_{{{\varvec{1i}}}} } } \right)\)

\({\varvec{H}}_{{\varvec{0}}} {\varvec{:dc}}\xrightarrow{{\varvec{ \times }}}{\varvec{dm}}\) \(\left( {\sum\nolimits_{{{\varvec{i = 1}}}}^{{\varvec{L}}} {{\varvec{\beta }}_{{{\varvec{3i}}}} } } \right)\)

HDFC

8

VECM

29.717*** (0.015)

38.523*** (0.603)

40.883*** (− 0.086)

67.129*** (− 0.071)

ICICI

8

VAR

44.082*** (0.555)

33.448*** (0.199)

23.129*** (− 0.024)

66.682*** (− 0.033)

SBIL

5

VAR

3.712 (0.110)

17.206*** (0.353)

5.138 (0.007)

5.947 (0.012)

TWN

5

VAR

8.942 (0.186)

14.660*** (0.364)

13.016** (0.048)

11.463** (0.032)

  1. We use the Akaike information criterion (AIC) statistic to select the optimal Lag periods (L) and the Wald chi-square statistic to test the causality between variables. The notation "***" and "**" represent 1% and 5% significance levels, respectively; L represents the Lag periods