**CHAPTER 4 EMPIRICAL FINDINGS**

**4.4. News Diffusion Model**

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of US lagged return as strongest predictive power in industrialized countries. Moreover, lagged France returns and Switzerland returns which are positive and statistically significant in table IV, in table V the results show that both lagged returns have significant negative effect on ASEAN equity market returns.

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**Table VI**

**News Diffusion Model Parameter Estimates **

The table reports two-step GMM parameters estimates for the news-diffusion model
𝑟_{𝑗,𝑡+1}= 𝑥′_{𝑗,𝑡}𝛽_{𝑗}+ 𝑢_{𝑗,𝑡+1},

𝑟𝑖,𝑡+1= 𝑥′𝑖,𝑡𝛽𝑖+ 𝜃𝑖,𝑗𝜆𝑖,𝑗𝑢𝑗,𝑡+1+ (1 − 𝜃𝑖,𝑗)𝜆𝑖,𝑗𝑢𝑗,𝑡+ 𝑢𝑖,𝑡+1

Where 𝑟_{𝑖,𝑡+1} is the weekly national currency excess return, 𝑥_{𝑖,𝑡} = (1, 𝑏𝑖𝑙𝑙_{𝑖,𝑡}, 𝑠𝑝𝑟𝑒𝑎𝑑_{𝑖,𝑡})’, 𝛽_{𝑖}= (𝛽_{𝑖,0}, 𝛽_{𝑖,𝑏},
𝛽𝑖,𝑠)’, and 𝑏𝑖𝑙𝑙𝑖,𝑡 (𝑠𝑝𝑟𝑒𝑎𝑑𝑖,𝑡) is the short term interest rate (term spread) for country i. t-statistics are
reported in parentheses;. The t-statistics for 𝛽_{𝑖,𝑏} (𝛽_{𝑖,𝑠}) are for testing 𝐻_{0}: 𝛽_{𝑖,𝑏} = 0 against 𝐻_{𝐴}: 𝛽_{𝑖,𝑏} < 0
(𝐻_{0}: 𝛽_{𝑖,𝑠}= 0 against 𝐻_{𝐴}: 𝛽_{𝑖,𝑠} > 0). The t-statistics for Ɵ_{𝑖,𝑗} (𝜆_{𝑖,𝑗}) are for testing 𝐻_{0}: Ɵ_{𝑖,𝑗}= 1 against 𝐻_{𝐴}: Ɵ_{𝑖,𝑗}<

1 (𝐻_{0}: 𝜆_{𝑖,𝑗}= 0 against 𝐻_{𝐴}: 𝜆_{𝑖,𝑗}> 0). (**) & (*) indicate significance at 5% and 10% respectively. “Pooled

“ estimates impose the following homogeneity restrictions: 𝛽𝑖,𝑏 = ¯𝛽𝑏, 𝛽𝑖,𝑠 = ¯𝛽𝑠, Ɵ𝑖,𝑗 = ¯Ɵ𝑗, and 𝜆𝑖,𝑗 =

¯𝜆_{𝑗}for all *i≠j.*

(i) βi,b βi,s θi,CAN ^{λi,CAN} ^{βi,CAN} ^{(i)} βi,b βi,s θi,GER ^{λi,GER} ^{βi,GER}

Indonesia -6.47 0.37 0.70** 1.04** 0.31** Indonesia -15.82* 7.23* 0.84** 0.64** 0.10*

(-0.75) (0.10) (19.13) (10.47) (5.93) (-1.68) (1.77) (12.30) (7.01) (1.95)

Malaysia -36.28** -17.17* 0.65** 0.55** 0.19** Malaysia -53.59** -19.89* 0.76** 0.41** 0.10**

(-2.61) (-2.08) (14.64) (10.80) (5.33) (-3.50) (-1.85) (18.98) (10.46) (4.25)

Philippine -3.12 -1.31 0.61** 0.66** 0.26** Philippine -0.95 1.39 0.76** 0.48** 0.12**

(-1.02) (-0.22) (14.73) (12.07) (7.11) (-0.29) (0.22) (14.79) (7.68) (3.51)

Singapore -28.50** -19.50 0.72** 1.01** 0.28** Singapore -25.57** -1.95 0.81** 0.74** 0.14**

(-3.18) (-2.33) (23.17) (16.51) (6.40) (-2.73) (-0.24) (24.90) (13.39) (4.76)

Thailand -36.54** -32.29** 0.69** 0.94** 0.29** Thailand -46.68** -38.57** 0.81** 0.60** 0.11**

(-4.08) (-4.04) (19.41) (13.27) (7.03) (-5.03) (-4.13) (14.09) (7.76) (2.50)

Vietnam 2.93 7.88 0.56** 0.72** 0.35** Vietnam 5.70 13.65 0.65** 0.58** 0.20**

(0.43) (0.88) (7.76) (6.10) (4.37) (0.72) (1.26) (8.05) (5.60) (3.18)

Pooled -1.39 1.32 0.67** 0.84** 0.27** Pooled -3.54 3.43 0.75** 0.63** 0.16**

(-0.28) (0.29) (15.88) (7.95) (4.37) (-0.75) (0.77) (17.18) (9.25) (4.28)

(i) βi,b βi,s θi,UK ^{λi,UK} ^{βi,UK} ^{(i)} βi,b βi,s θi,US ^{λi,US} ^{βi,US}

Indonesia -17.54** 7.25* 0.85** 0.84** 0.13* Indonesia -10.85 1.90 0.77** 0.82** 0.19**

(-2.29) (1.93) (12.26) (9.13) (1.90) (-1.37) (0.59) (12.61) (9.34) (3.05)

Malaysia -52.17** -26.30** 0.79** 0.50** 0.10** Malaysia -52.02** -30.27** 0.70** 0.46** 0.14**

(-3.65) (-2.68) (16.98) (12.52) (3.42) (-4.27) (-3.49) (16.08) (12.49) (5.19)

Philippine -4.66* -1.71 0.74** 0.69** 0.18** Philippine -3.44 -4.77 0.68** 0.58** 0.18**

(-1.74) (-0.30) (17.37) (11.45) (4.78) (-1.18) (-0.80) (15.88) (10.24) (5.34)

Singapore -26.90** -6.15 0.83** 0.99** 0.17** Singapore -31.80** -25.23** 0.77** 0.92** 0.21**

(-3.26) (-0.83) (23.63) (18.49) (4.17) (-4.04) (-3.67) (22.54) (19.47) (5.29)

Thailand -46.09** -38.73** 0.82** 0.87** 0.15** Thailand -42.10** -45.90** 0.74** 0.83** 0.21**

(-6.07) (-5.01) (19.93) (11.79) (3.59) (-5.45) (-5.88) (20.30) (11.93) (5.21)

Vietnam 5.69 12.60 0.64** 0.71** 0.25** Vietnam 3.13 8.66 0.62** 0.65** 0.25**

(0.78) (1.26) (7.26) (6.87) (3.52) (0.43) (0.92) (8.13) (7.59) (3.90)

Pooled -3.45 2.40 0.77** 0.80** 0.18** Pooled -2.77 -0.12 0.70** 0.73** 0.22**

(-0.73) (0.55) (18.35) (9.12) (4.01) (-0.55) (-0.03) (14.82) (8.13) (4.00)

Canada Germany

United Kingdom United States

Panel C: News-diffusion model : country* j* = United States
Panel C: News-diffusion model : country* j* = United Kingdom

Panel A: News-diffusion model : country *j* = Canada Panel B: News-diffusion model : country *j* = Germany

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Panel A, B, C, and D show the news diffusion model for country *j *= Canada (CAN),
Germany (GER), United Kingdom (UK), and United States (US), respectively. 𝜆_{𝑖,𝑗}
estimates are reported in column (5) on each panel, the t-statistics for 𝜆_{𝑖,𝑗} (in
parentheses below the coefficient 𝜆_{𝑖,𝑗}) are for testing 𝐻_{0}:𝜆_{𝑖,𝑗}= 0 againts 𝐻_{0}:𝜆_{𝑖,𝑗}> 0
and all of them are significant at 5% significance level. These results indicate that an
economically significant links exist between each country in ASEAN with Canada,
Germany, U.K, and U.S market. The Ɵ_{𝑖,𝑗} estimates are reported in fourth column of
each panel, t-statistics below the estimates are for testing 𝐻_{0}:Ɵ_{𝑖,𝑗} = 1 againts
𝐻_{0}:Ɵ_{𝑖,𝑗} < 1. All of Ɵ_{𝑖,𝑗} for each country *j* in each panel are significantly less than one
which consistent with international information frictions.^{16}

Table VI also reports pooled estimates of news-diffusion model parameter
based on following homogeneity restrictions: 𝛽_{𝑖,𝑏} = ¯𝛽_{𝑏}, 𝛽_{𝑖,𝑠} = ¯𝛽_{𝑠}, Ɵ_{𝑖,𝑗} = ¯Ɵ_{𝑗}, and 𝜆_{𝑖,𝑗}

= ¯𝜆_{𝑗}for all *i≠j (j= CAN, GER, UK, US)*. The GMM estimates of ¯Ɵ_{𝐶𝐴𝑁}(¯𝜆_{𝐶𝐴𝑁}) is 0.67
(0.84), for ¯Ɵ_{𝐺𝐸𝑅}(¯𝜆_{𝐺𝐸𝑅}) the value is 0.75 (0.63), for ¯Ɵ_{𝑈𝐾}(¯𝜆_{𝑈𝐾}) the estimate value
is 0.77 (0.80) and estimate values for ¯Ɵ_{𝑈𝑆}(¯𝜆_{𝑈𝑆}) is 0.70 (0.73), which all of them are
significantly less than zero (greater than zero). Comparing the ¯𝜆_{𝑗} estimate between
four *j *countries, I find that total impact of lagged Canada returns shock on ASEAN
countries is larger compare to lagged Germany returns, lagged United Kingdom returns,

16 The t-statistics reported in Table VI are significant based on GMM p-values. For all of the test of overidentifying restrictions that we used to estimate the news-diffusion model, we failed to reject the null hypothesis of invalid over-identifying model because the p-value of J-statistic are higher than 10% significant level which means the over-identifying model are valid for estimating news-diffusion model.

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and lagged United States returns. The informations frictions also more severe between
Canada market and ASEAN markets compare to other three countries, since the ¯Ɵ_{𝐶𝐴𝑁}
estimate which is 0.67 is smaller than ¯Ɵ_{𝐺𝐸𝑅}, ¯Ɵ_{𝑈𝐾}, and ¯Ɵ_{𝑈𝑆} estimates which are 0.75,
0.77, and 0.70 respectively. Evidence from Table VI show that ASEAN countries
returns are underreact to industrialized countries shocks, which consistent with
information frictions in international equity markets.

The news-diffusion model does not claim that information frictions are
responsible for predictive ability of lagged return of country *j*, solely. To measure the
importance of information frictions on this lead-lag relationship between ASEAN
equity markets and four industrialized countries, I compare the coefficient of 𝑟_{𝑗,𝑡}in each
panel (for* j* equals to CAN, GER, UK, and US) which implied by the GMM estimates
of Ɵ_{𝑖,𝑗} and 𝜆_{𝑖,𝑗} in Table VI with the 𝛽_{𝑖,𝑗} estimates in Table IV. To facilitate the
comparisons, last columns of each panel in Table VI reports 𝛽_{𝑖,𝑗} = (1-Ɵ_{𝑖,𝑗})𝜆_{𝑖,𝑗}, the t-
statistics in parentheses below the 𝛽_{𝑖,𝑗} estimates are for testing 𝐻_{0}:𝛽_{𝑖,𝑗} = 0 againts
𝐻_{0}:𝛽_{𝑖,𝑗}> 0 (for* j* equals to CAN, GER, UK, and US), standard errors used to compute
t-statistics are calculted via delta method. In this case, I reject 𝛽_{𝑖,𝑗}= 0 in Table VI at 5%

and 10% level for all ASEAN equity markets, implying that information frictions give
rise to predictive power of lagged country *j* returns (for* j* equals to CAN, GER, UK, and
US).

Under the comparison between the 𝛽_{𝑖,𝑗}estimates obtained in Table VI to 𝛽_{𝑖,𝑗}
estimates reported in Table IV, if the estimated values for 𝛽_{𝑖,𝑗} in Table VI are smaller

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than in Table IV means that information frictions do not account for all the predictive
ability of country *j* which *j* equals to lagged returns of Canada, Germany, United
Kingdom, and United States.

Estimated values for 𝛽_{𝑖,𝐶𝐴𝑁}, 𝛽_{𝑖,𝐺𝐸𝑅}, 𝛽_{𝑖,𝑈𝐾}, and 𝛽_{𝑖,𝑈𝑆} in Table VI where *i *equals
to Indonesia, Malaysia, Phillipine, Singapore, and Thailand are smaller compare to
𝛽_{𝑖,𝐶𝐴𝑁}, 𝛽_{𝑖,𝐺𝐸𝑅}, 𝛽_{𝑖,𝑈𝐾} and 𝛽_{𝑖,𝑈𝑆} estimates in Table IV, which indicate that information
frictions can not describe all the preditive ability of lagged returns Canada, Germany,
United Kingdom, and United States on ASEAN equity markets (except Vietnam). Apart
with the results from the rest of ASEAN countries, 𝛽_{𝑖,𝐶𝐴𝑁}, 𝛽_{𝑖,𝐺𝐸𝑅}, 𝛽_{𝑖,𝑈𝐾}, and 𝛽_{𝑖,𝑈𝑆}
estimates for Vietnam (country *i*) reported in Table VI are bigger than in Table IV which
means existence of information frictions between Vietnam and Canada, Germany,
United Kingdom also United States are fully account for all predictive ability of those
countries to Vietnam equity markets. According to the results I obtained from news-
diffusion model, they suggest that information frictions as one source for predictive
ability of lagged industrialized countries on ASEAN equity markets.

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