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. 2022 Jul 3;83:102261. doi: 10.1016/j.irfa.2022.102261

Table 4.

Parameter estimates for multivariate MSIAH model.

Asia Europe USA Other
μ1 0.12** (0.06) 0.03 (0.06) 0.03 (0.08) 0.19*** (0.06)
μ2 −1.11*** (0.21) −0.14 (0.21) 0.43* (0.24) −0.73** (0.30)
β1_Asia 0.38*** (0.07) 0.13* (0.08) −0.14 (0.09) 0.02 (0.07)
β2_Asia −0.00 (0.01) −0.09 (0.13) 0.29*** (0.01) −0.09 (0.17)
β1_Europe 0.29*** (0.06) 0.57*** (0.07) −0.04 (0.08) −0.00 (0.02)
β2_Europe −0.08 (0.19) 0.42*** (0.16) 0.40* (0.21) 0.42* (0.23)
β1_USA −0.00 (0.06) 0.03 (0.06) 0.24*** (0.07) 0.07 (0.06)
β2_USA 0.31** (0.16) 0.13 (0.15) 0.14 (0.18) 0.21 (0.19)
β1_Others 0.13* (0.08) −0.01 (0.08) −0.04 (0.09) 0.28*** (0.08)
β2_Others −0.19 (0.15) 0.27** (0.14) 0.03 (0.18) 0.13 (0.18)
σ1 0.35*** (0.04)
σ2 1.16*** (0.27)
Duration 1 20.90
Duration 2 5.44

This table reports the parameter estimates of the multivariate 2-state Markov switching models for the daily flow of ETFs with exposure to Asia, Europe, U.S., and rest of the world. The model choice (MSIAH) is based on the lowest AIC and BIC score. The MSIAH model is specified as yt = mSt + bStyt−1 + et, where yt refers to a matrix of four flow series, mStrepresents a vector of mean flow in each state (1 and 2), and sStshows the conditional volatility of each state. bSt is a 4′4 matrix of autoregressive term in each state and et shows the error term. Duration shows the respective duration of being in one regime during the period of the study. The sample period is from January 2020 to October 2020. The parentheses contain the standard error. *, **, and ***, respectively, denote significance at the 10%, 5%, and 1% levels.