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# News Diffusion Model

## 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.

42 Table VI

News Diffusion Model Parameter Estimates

The table reports two-step GMM parameters estimates for the news-diffusion model 𝑟𝑗,𝑡+1= 𝑥′𝑗,𝑡𝛽𝑗+ 𝑢𝑗,𝑡+1,

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

Where 𝑟𝑖,𝑡+1 is the weekly national currency excess return, 𝑥𝑖,𝑡 = (1, 𝑏𝑖𝑙𝑙𝑖,𝑡, 𝑠𝑝𝑟𝑒𝑎𝑑𝑖,𝑡)’, 𝛽𝑖= (𝛽𝑖,0, 𝛽𝑖,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 𝛽𝑖,𝑏 (𝛽𝑖,𝑠) are for testing 𝐻0: 𝛽𝑖,𝑏 = 0 against 𝐻𝐴: 𝛽𝑖,𝑏 < 0 (𝐻0: 𝛽𝑖,𝑠= 0 against 𝐻𝐴: 𝛽𝑖,𝑠 > 0). The t-statistics for Ɵ𝑖,𝑗 (𝜆𝑖,𝑗) are for testing 𝐻0: Ɵ𝑖,𝑗= 1 against 𝐻𝐴: Ɵ𝑖,𝑗<

1 (𝐻0: 𝜆𝑖,𝑗= 0 against 𝐻𝐴: 𝜆𝑖,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 =

¯𝜆𝑗for 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)

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. 𝜆𝑖,𝑗 estimates are reported in column (5) on each panel, the t-statistics for 𝜆𝑖,𝑗 (in parentheses below the coefficient 𝜆𝑖,𝑗) are for testing 𝐻0:⁡𝜆𝑖,𝑗= 0 againts 𝐻0:⁡𝜆𝑖,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 Ɵ𝑖,𝑗 estimates are reported in fourth column of each panel, t-statistics below the estimates are for testing 𝐻0:⁡Ɵ𝑖,𝑗 = 1 againts 𝐻0:⁡Ɵ𝑖,𝑗 < 1. All of Ɵ𝑖,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: 𝛽𝑖,𝑏 = ¯𝛽𝑏, 𝛽𝑖,𝑠 = ¯𝛽𝑠, Ɵ𝑖,𝑗 = ¯Ɵ𝑗, and 𝜆𝑖,𝑗

= ¯𝜆𝑗for all i≠j (j= CAN, GER, UK, US). The GMM estimates of ¯Ɵ𝐶𝐴𝑁⁡(¯𝜆𝐶𝐴𝑁) is 0.67 (0.84), for ¯Ɵ𝐺𝐸𝑅⁡(¯𝜆𝐺𝐸𝑅) the value is 0.75 (0.63), for ¯Ɵ𝑈𝐾⁡(¯𝜆𝑈𝐾) the estimate value is 0.77 (0.80) and estimate values for ¯Ɵ𝑈𝑆⁡(¯𝜆𝑈𝑆) is 0.70 (0.73), which all of them are significantly less than zero (greater than zero). Comparing the ¯𝜆𝑗 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 ¯Ɵ𝐶𝐴𝑁 estimate which is 0.67 is smaller than ¯Ɵ𝐺𝐸𝑅, ¯Ɵ𝑈𝐾, and ¯Ɵ𝑈𝑆 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 𝑟𝑗,𝑡in each panel (for j equals to CAN, GER, UK, and US) which implied by the GMM estimates of Ɵ𝑖,𝑗 and 𝜆𝑖,𝑗 in Table VI with the 𝛽𝑖,𝑗 estimates in Table IV. To facilitate the comparisons, last columns of each panel in Table VI reports 𝛽𝑖,𝑗 = (1-Ɵ𝑖,𝑗)⁡𝜆𝑖,𝑗, the t- statistics in parentheses below the 𝛽𝑖,𝑗 estimates are for testing 𝐻0:⁡𝛽𝑖,𝑗 = 0 againts 𝐻0:⁡𝛽𝑖,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 𝛽𝑖,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 𝛽𝑖,𝑗⁡estimates obtained in Table VI to 𝛽𝑖,𝑗 estimates reported in Table IV, if the estimated values for 𝛽𝑖,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 𝛽𝑖,𝐶𝐴𝑁, 𝛽𝑖,𝐺𝐸𝑅, 𝛽𝑖,𝑈𝐾, and 𝛽𝑖,𝑈𝑆 in Table VI where i equals to Indonesia, Malaysia, Phillipine, Singapore, and Thailand are smaller compare to 𝛽𝑖,𝐶𝐴𝑁, 𝛽𝑖,𝐺𝐸𝑅, 𝛽𝑖,𝑈𝐾 and 𝛽𝑖,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, 𝛽𝑖,𝐶𝐴𝑁, 𝛽𝑖,𝐺𝐸𝑅, 𝛽𝑖,𝑈𝐾, and 𝛽𝑖,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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Outline

관련 문서