What drives dynamic comovements of stock markets in the Pacific Basin region?: A quantile regression approach

Accepted Manuscript What drives dynamic comovements of stock markets in the Pacific Basin region?: A quantile regression approach Hyunchul Lee, Seung Mo Cho PII:

S1059-0560(17)30400-8

DOI:

10.1016/j.iref.2017.05.005

Reference:

REVECO 1425

To appear in:

International Review of Economics and Finance

Received Date: 27 March 2015 Revised Date:

27 March 2017

Accepted Date: 16 May 2017

Please cite this article as: Lee H. & Cho S.M., What drives dynamic comovements of stock markets in the Pacific Basin region?: A quantile regression approach, International Review of Economics and Finance (2017), doi: 10.1016/j.iref.2017.05.005. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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What Drives Dynamic Comovements of Stock Markets in the Pacific Basin

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Region?: A Quantile Regression Approach

Hyunchul Lee† and Seung Mo Cho‡*1

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†Division of Business Administration, Chosun University, Gwangju, South Korea

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‡ School of Economics and Finance, Yeungnam University, Gyeongsan, South Korea

Abstract

In this paper, we show that pairwise similarities of a set of macroeconomic variables among major countries in the Pacific Basin region can account for the stock market comovements in the region. We first suggest a simple theoretical argument why pairwise similarities of macroeconomic variables can

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derive stock market comovements. We then apply the conditional nonlinear quantile regression on the pairwise realized stock return correlations for the stock markets in the Pacific Basin region from 1990 to 2012 to empirically justify the argument. As a result, we find evidence that smaller pairwise

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differences or larger pairwise similarities of a set of macroeconomic variables significantly drive the

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stock market comovements in the region in a nonlinear way.

Keywords: Stock market comovements; Macroeconomic performances; Realized correlations; Nonlinearity; Conditional quantile regression

JEL Classification: E00, F36, G15

*Corresponding to: [email protected] / Tel. +82 10 4630 6743 1

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What Drives Dynamic Comovements of Stock Markets in the Pacific Basin

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SC

RI PT

Region?: A Quantile Regression Approach

Abstract

In this paper, we show that pairwise similarities of a set of macroeconomic variables among major countries in the Pacific Basin region can account for the stock market comovements in the region. We first suggest a simple theoretical argument why pairwise similarities of macroeconomic variables can

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derive stock market comovements. We then apply the conditional nonlinear quantile regression on the pairwise realized stock return correlations for the stock markets in the Pacific Basin region from 1990 to 2012 to empirically justify the argument. As a result, we find evidence that smaller pairwise

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differences or larger pairwise similarities of a set of macroeconomic variables significantly drive the

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stock market comovements in the region in a nonlinear way.

Keywords: Stock market comovements; Macroeconomic performances; Realized correlations; Nonlinearity; Conditional quantile regression

JEL Classification: E00, F36, G15

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1. Introduction As is well known from the classical portfolio theory, investors can be better off with a larger investible asset class because a larger investible asset class can expand the efficient frontier to the upper left side of the σ R - E (R ) plane enabling investors to attain a higher utility level.

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Naturally, a larger investible asset class can be attained either through domestic capital market development or through international capital market comovements. Integration of international capital (stock) markets allows the linked countries to enjoy a variety of

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economic benefits such as efficient allocation of global capital, development of financial

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markets, and economic growth (see Pagano, 1993; Devereux and Smith, 1994; Levine and Zervos, 1998; Obstfeld, 1998; Bekaert et al., 2001, 2005; Prasad et al., 2003 among others). This also provides global investors with an opportunity for risk sharing. On the other hand, internationally integrated capital markets also have some dark sides that may lead to joint market crashes during severe financial turmoil (e.g., Bekaert et al., 2009; Beine and Candelon,

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2011; Christoffersen et al., 2012; Longin and Solnik, 1995, 2001). From the perspective of domestic economies and domestic investors, integration of international capital markets may reduce the welfare of domestic investors holding inefficient portfolios and the wealth of

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capital markets in underdeveloped countries with nominal price rigidity (Pang, 2013; Kim et

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al., 2014). Despite the dark aspects mentioned above, the aforementioned benefits of stock market integration are not insignificant at all. Thus, the issue of stock market comovements in the Pacific Basin region is crucial for policymakers, international investors, and academic researchers alike.

Following the Asian financial crisis in 1997-1998, ASEAN+3 finance ministers in the region effectively proposed various regional initiatives such as the Chiang Mai Initiative (CMI) launched in May 2000 and the Asian Bond Markets Initiatives (ABMI) launched in

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ACCEPTED MANUSCRIPT August 2003 to strengthen financial cooperation and comovements (Yu et al., 2010). 1 In addition, inspired by the recent monetary union in Europe, ideas of an Asian currency basket or Asian currency union have been meaningfully suggested as a long-term policy objective for the intra-regional exchange rate stability in the region (Institute for International Monetary

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Affairs, 2006). Apart from these initiatives, this region has experienced significant financial globalization, which has accelerated financial market comovements in the region based on active international trades and economic and financial interactions within the region (Yu et

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al., 2010). The importance of this region in the global economy has greatly increased in recent decades. Specifically, the Asia-Pacific region makes up approximately 37% of global GDP in

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purchasing power parity terms (European Central Bank, 2008). Furthermore, the region encompasses some of the world’s most dynamic economies, such as China, Malaysia, Taiwan, South Korea and advanced economies such as Australia and Japan, with GDP per capita of around $40,000 at market rates of their local currencies to the US dollar in 2007,

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respectively (European Central Bank, 2008). From the financial perspective, most stock markets in the Pacific Basin region have been highly ranked in terms of market capitalization of listed companies (in US dollars). Specifically, the stock markets analyzed in this study are

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all within the top 30: China (2nd), Japan (3rd), Australia (8th), South Korea (11th), Hong Kong (12th), Malaysia (21st), Singapore (22nd), Thailand (24th), and Philippines (29th) (see the

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Standard & Poor’s Global Stock Markets Factbook and Supplemental Data in 2012). The high stock market rankings in this region reflect a significant increase in capital flows (investments) of global investors to these markets. Therefore, proper understandings of economic drivers and the nature of comovements among the stock markets in the Pacific

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The major aim of CMI is to systematically establish a network of bilateral swap arrangements among

ASEAN+3 countries for effectively overcoming short-term liquidity difficulties of the region and the aim of ABMI is to build efficient and liquid bond markets in the region to set up the financial systems in a stable manner (Yu et al., 2010).

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ACCEPTED MANUSCRIPT Basin region are commonly significant for international investors, policymakers, and researchers. Despite its importance, there have been few studies on stock market convergence in this region, which contrasts to a substantial body of studies on stock market comovements in other

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regions (e.g., Europe, North America, etc.). Moreover, studies exploring drivers of stock market comovements in this region are limited, although the existing studies primarily focus on measuring the extent of comovements across markets. However, it is crucial to detect

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drivers of stock market comovements because we can utilize them to promote the comovements. Thus, our work sheds light empirically on relevant drivers of stock market

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comovements in the Pacific Basin region.

Importantly, the existing literature on stock market comovements depends mostly on a linear approach and therefore does not effectively account for the nonlinearities that the effects of the drivers might have on stock market comovements. Thus, our study also

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investigates the nature of time-varying comovements of stock markets in the Pacific Basin region by a nonlinear approach. To this end, we employ the conditional nonlinear quantile regressions in analyzing pairwise realized correlations, a proxy for stock market

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comovements in the region. Focusing on the conditional median and different conditional quantiles, the nonlinear quantile regressions provide deeper insights than the classical linear

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OLS regression in case there substantially exists a nonlinear relation between the pairwise realized correlations and their drivers. The nonlinearities in financial markets may imply that market participants behave in different ways with different information in the macroeconomic factors affecting stock market comovements. Individual investors may not take advantage of overseas investment while institutional investors do so with superior information acquisition capabilities. Institutional investors from more advanced economies may have better profit-generating skills with the

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ACCEPTED MANUSCRIPT same information. If so, we can see that at least the strong form efficient market hypothesis does not hold, although semi-strong or the weak form efficiency can be attained in reality. With respect to global investors’ diversification, nonlinear natures imply that investors heterogeneously price the discount factors of macro-economic (similarity) drivers by different

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extents of comovements among stock markets in this region when they construct their global portfolios.

Applying the nonlinear quantile regression and the realized correlation concept of stock

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returns, this study is among the first to systematically capture nonlinearities between comovements of stock markets in the Pacific Basin region and pairwise (macro)economic

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similarity effects of the sample countries. Studies by Mensi et al. (2014) and Rejeb and Arfaoui (2016) have also used the quantile regression technique to study emerging stock markets. Specifically, the former investigates economic influences of a variety of global risk factors (e.g., the S&P 500 index, commodity markets, global stock market uncertainty, and

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US economic policy uncertainty) on individual emerging stock markets in BRICS (i.e., Brazil, Russia, India, China, South Africa). The latter explores structures of volatility transmission on comovements among Asian and Latin American emerging and developed US and Japanese

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stock markets. However, neither of them mentions any magnitudes nor do they offer any

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economic and financial interpretations on both linear and nonlinear relationships between stock market interdependence and the pairwise economic similarity effects of the sample countries. Our principal findings can be summarized as follows. Lower pairwise differences in monetary performance such as inflation and interest rates among the sample countries have turned out to crucially increase time-varying pairwise stock market comovements in the Pacific Basin region. Pairwise differences in industrial production among the sample countries have shown negative relations with stock return convergence. Lower pairwise differences in exchange rate volatility among the countries have proven to be significantly

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ACCEPTED MANUSCRIPT associated with an increase in market comovements. Regarding the impact of market development, smaller pairwise differences of the stock market sizes proxied by the market capitalization of the individual country have turned out to promote the stock market comovements of the region. The 3-month U.S. Treasury bond return, a proxy for the global

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risk free rate of return, has formed a negative relationship with the market comovements. In addition, we have also found evidence that the three recent worldwide economic crises (i.e., Asian crisis in 1997, U.S. subprime crisis in 2008, European fiscal crisis in 2010) had positive

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effects on dynamic convergence of the stock markets in this region. In particular, we have observed significant nonlinearities for the effects of our economic drivers on the market

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comovements.

The paper is organized as follows. Section 2 reviews the literature related to time-varying comovements of stock markets in the Pacific Basin region. Section 3 presents the theoretical background and methodology issues for the study. Section 4 explains data issues. Section 5

2. Literature Review

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discusses our empirical findings. Section 6 briefly concludes.

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Over the past 20 years or so, a substantial body of research has examined comovements of Asian stock markets, showing mixed results, most commonly employ the cointegration

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technique as the main methodology. For example, Chan et al. (1992) and DeFusco et al. (1996) find no cointegrating relationship among the U.S. stock market and some major East Asian equity markets over the 1980s and/or early 1990s, which suggests the effectiveness of international diversification among the markets in question at the times the studies were conducted. Similarly, Majid et al. (2008) find no cointegrating vectors across five East Asian stock markets in Indonesia, Malaysia, Philippines, Singapore, and Thailand during the pre1997 Asian crisis period. In the post-crisis period, the authors show a long-term relationship of the five ASEAN countries’ stock markets, the U.S. stock market, and the Japanese stock 5

ACCEPTED MANUSCRIPT market. On the contrary, Masih and Masih (1997, 1999) present a cointegrating relation among some major East Asian emerging markets and major developed markets. Along a similar vein, Chung and Liu (1994) produce evidence of some meaningful comovement relations among some East Asian stock returns and US stock returns. Including information

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from the 1997-1998 Asian financial crisis, Ghosh et al. (1999) and Sheng and Tu (2000) report some significant comovement relationships among the U.S., Japan, and some other major East Asian equity markets.

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These mixed results have naturally promoted subsequent studies on what might potentially drive stock market coupling or decoupling in the Asia Pacific region or in the

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world. In particular, the literature suggests that the Asian stock market crash in 1997 substantially strengthened Asian stock market linkages. For instance, recent studies by Mukherjee and Bose (2008) and Awokuse et al. (2009) provide evidence of more than one cointegrating vector among major Asian stock markets in India, Japan, Hong Kong, Korea,

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Malaysia, Singapore, and Taiwan with an increased number of vectors after the crisis. Some other recent studies explore nature of dynamic comovements of East Asian stock markets using the Kalman filter technique. For example, Khan and Park (2009) obtain time-varying

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pairwise cross-country residual correlations among the stock market return for five East Asian countries – Thailand, Malaysia, Indonesia, Korea, and the Philippines – after controlling for

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major macroeconomic fundamentals and global shocks in a multivariate regression using the Kalman filter technique to capture the contagion effects among these stock markets. They show a significant increase in these residual correlations during the crisis period compared with those during the stable period, which confirms a herding contagion in the region during the crisis. Similarly, Baur and Fry (2009) report that interdependence of 11 Asian stock markets substantially increased during the Asian crisis. Overall, the aforementioned studies indicate that stock markets in East Asian economies significantly converged during or after

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ACCEPTED MANUSCRIPT the Asian financial crisis, thus supporting the notion that a financial crisis significantly alters the relationship across stock markets in the region. Another stream of research heavily explores the extent of stock market comovements in Asia in comparison to that in other regions. Specifically, Danareksa Research Institute (2004)

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reports that financial comovements in East Asia are still far behind the ones in Europe even before its unification in the 1990s. Cavoli et al. (2004) also report that financial market comovements in East Asia have not obviously deepened despite the governments’ conscious

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efforts aimed at financial and monetary cooperation within the region, especially since the 1997 Asian financial crisis. Meanwhile, some studies address a higher degree of financial

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comovements in the Pacific Basin region. For instance, Jeon et al. (2006) suggest that financial comovements in East Asia have increased overall, although the financial markets in the region have been integrated more with global markets than with themselves. Guillaumin (2009) also shows strong comovements among financial markets of nine East Asian

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economies (i.e., China, Korea, Hong Kong, Indonesia, Japan, Malaysia, the Philippines, Singapore, and Thailand) with stronger comovements after the 1997 Asian financial crisis. More recently, Yu et al. (2010) reveal that stock markets in Asia have shown greater financial

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comovements over the years and that the comovements resumed in 2007-08 after its slow phase from 2002 to 2006 and is still in progress. Some studies explore a facet of stock market

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comovements in the Pacific Basin region within the scope of emerging market comovements (e.g., Bekaert and Harvey, 1995; Phylaktis and Ravazzolo, 2002; Pretorius, 2002; Frijns et al., 2012). Specifically, Bekaert and Harvey (1995), analyzing time-varying comovements of 11 emerging stock markets in the world to global capital markets, show that the Asian stock markets in their sample such as Korea, Malaysia, Taiwan, and Thailand are all substantially integrated to global capital markets despite their differences in foreign ownership restrictions. Analyzing the covariance of excess returns of major Pacific Basin emerging stock markets,

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ACCEPTED MANUSCRIPT which, they report, account for nearly half of the entire market capitalization of all emerging markets in the world, at the regional and global levels, Phylaktis and Ravazzolo (2002) suggest that economic comovements work as a channel for financial comovements at both levels. Targeting 10 emerging stock markets in Asia, Latin America, Africa, and Europe,

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Pretorius (2002) finds that pairwise correlations proxied for the interdependence of emerging stock markets can be substantially explained by the countries’ economic fundamentals such as bilateral trade and industrial production differentials. This suggests that there remains some

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room for stock market diversification. With a sample of 25 developing economies for a sample period of 15 years, Beine and Candelon (2011) empirically show that both trade and

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financial liberalizations have positive impacts on the time-varying pairwise correlations of stock market returns among the sample economies. An interesting study of Frijns et al. (2012) investigates the impacts of a variety of political crises for explaining the extent of stock market comovements in 19 emerging economies in South and East Asia, Latin America, and

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Central and Eastern Europe over the periods 1991–2006. They suggest that crises with certain characteristics are significantly associated with a decrease in stock market comovements in

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these regions.

3. Methodology

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3.1 Measuring Time-Varying Comovements In order to measure the dynamic aspect of stock market comovements in the Pacific Basin region, we employ the concept of realized correlation. In particular, it is important to note that the use of pairwise correlations of stock returns across sample countries over the sample period is effective in addressing the issue of synchronization to explore drivers of stock market comovements (Dellas and Hess 2005; Pretorius 2002). The realized moments approach has been employed by financial econometricians such as Andersen et al. (2001, 2003) and Barndorff-Nielsen and Shephard (2002), among others, as a good alternative to the 8

ACCEPTED MANUSCRIPT traditional parametric one such as the GARCH (family) models. The empirical studies of Beine and Candelon (2011) and Cipollini et al. (2015) use the realized moment method to examine comovements of emerging stock markets and those of European sovereign bond markets, respectively.

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The realized correlation approach facilitates a consistent estimate of low frequency correlations by summing the squares and the cross-products of high frequency returns given the availability of higher frequency data (Cipollini et al., 2015). Correlations so constructed

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are model-free and, in theory, free from measurement errors as the sampling frequency of the returns approaches to the positive infinity (Andersen et al., 2001). Such an econometric merit

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of realized correlations has motivated us to use this method. Andersen et al. (2003) use intraday data to measure daily realized correlations while Beine and Candelon (2011) and Cipollini et al. (2015) use daily observations to measure annual realized correlations. In this paper, we follow the approach of Beine and Candelon (2011) and Cipollini et al. (2015) to

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compute annual estimates of the realized correlations using daily data of stock returns. According to the realized correlations method by Andersen et al. (2001, 2003), BarndorffNielsen and Shephard (2002), Beine and Candelon (2011) and Cipollini et al. (2015), based

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on the daily stock return defined to be ri ,t ,d = ln( pi ,t ,d / pi ,t ,d −1 ) × 100 where pi ,t ,d is the stock

σ

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index value for country i at day d in year t , the realized variance is measured by 2 t ,i

=

D

∑

t

d =1

[ ri ,t , d ] 2

(1)

where Dt denotes the number of business days in year t while the number of years in our analysis is 23. Following Andersen et al. (2003) and Cipollini et al. (2015), we also assume E (ri ,t , d ri ,t ,d −1 ) = 0 implying that stock markets are efficient. Then, the realized correlations method by Andersen et al. (2001, 2003), Barndorff-Nielsen and Shephard (2002), Beine and Candelon (2011) and Cipollini et al. (2015) measures the pairwise realized

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ACCEPTED MANUSCRIPT covariance between the annual stock index returns for country i and country j with the formula

ij , t

=

Dt

∑

d =1

[ ri ,t ,d × r j ,t , d ]

(2)

and the pairwise realized correlation ρ ij ,t with the formula

ρ ij , t =

σ

σ

ij , t

2 i ,t

×σ

2 j ,t

.

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σ

(3)

ρ

ij , t

= ln(

1 + ρ

ij , t

1 − ρ

ij , t

) .

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transformation to ρ ij ,t obtaining our final formula

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Finally, like Beine and Candelon (2011) and Cipollini et al. (2015), we also take the Fisher-Z

(4)

This allows us to exempt the panel regression from boundaries on the predicted realized

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correlations (Cipollini et al., 2015).

3.2 Theoretical Background for Drivers of Stock Market Comovements In this subsection, we briefly show how macroeconomic similarities (or differentials) between

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pairwise sample countries lead to higher stock return correlation between them using the factor model proposed by Ross (1976).

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Given that there are K factors F1 , F2 , L , FK affecting the stock returns RA and RB for country A and country B , respectively, we can consider the following factor models by Ross (1976):

K

R A = α A + ∑β Ai Fi + ε A

(5)

i =1

and K

RB = α B + ∑β Bi Fi + ε B

(6)

i =1

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ACCEPTED MANUSCRIPT where ∀i ≠ j , Cov (Fi , F j ) = 0 , ∀i, Cov(Fi , ε A ) = 0 , ∀i, Cov(Fi , ε B ) = 0 , and Cov(ε A , ε B ) = 0 . Then, we have K

Cov(RA , RB ) = ∑β Ai β BiVar (Fi )

(7)

i =1

K

ρ A,B = ∑β Ai β Bi i =1

σ i2 σ Aσ B

σ Aσ B

.

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Then, we have

∀k ∈ N K ,

Cov(R A , RB )

(8)

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where σ i2 = Var (Fi ), σ A = Var (R A ), σ B = Var (RB ), and ρ A,B =

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or

∂ρ A, B > 0, if β Ak β Bk > 0; 2σ k = β Ak β Bk = σ Aσ B < 0, if β Ak β Bk < 0. ∂σ k

(9)

We can see that the same change of the common risk (the future uncertainty of the common factor) can either increase or decrease the stock return correlation between the two

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countries depending on whether the common factor has similar or opposite effects on the stock returns of the two countries.

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Of course, this is only based on the factor model for stock returns. Since there may be some additional factors influencing only the covariance or the correlation of the two different

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stock returns not influencing the corresponding individual stock returns, we can build up our regression model as follows: K

L

i =1

j =1

Cov(RA , RB ) = α AB + ∑β Ai β BiVar (Fi ) + ∑β j G j + ε AB

(10)

or

ρ A, B =

K L Gj α AB σ i2 ε + ∑β Ai β Bi + ∑β j + AB σ Aσ B i=1 σ Aσ B j =1 σ Aσ B σ Aσ B

where σ i2 = Var (Fi ), σ A = Var (R A ), σ B = Var (RB ), and ρ A,B =

(11) Cov(R A , RB )

σ Aσ B

.

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ACCEPTED MANUSCRIPT Here, the G j s represent those additional factors influencing only the covariance of the two different stock returns other than the Var (Fi ) s. The problem is that we do not exactly know what those common international risk factors

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are. And even if we know those factors Fi s correctly, we cannot easily measure their future uncertainty σ i due to data availability. We need at least weekly data for those (assumed-tobe-known) risk factors to calculate the annual σ i s because a year usually consists of 52 weeks

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and we usually need at least 30 observations to compute any statistics.

Fortunately, however, it is highly probable that if the two countries have similar economic

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structures, then they will have a higher chance of being affected by those unknown common international risk factors’ future uncertainty in a similar manner. That is, if country A and country B have similar industrial structures consuming massive oil, then both of them will be affected by the future uncertainty of the international oil price (a common risk factor) in the

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same direction. Then, the rise of the future oil price uncertainty will increase the stock return correlation. This implies that similar macroeconomic variables of two countries will result in higher stock return correlation between the two. Thus, our study will focus on the similarity

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of which macroeconomic variables between any two countries in the Pacific Basin region will

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influence the stock return correlation between them.

3.3 The Conditional Nonlinear Quantile Regression To examine macroeconomic drivers for stock market comovements in the Pacific Basin region, we employ the conditional nonlinear quantile regression developed by Koenker and Bassett (1978) based on a median value approach and the linear OLS regression for comparison. Using the conditional nonlinear quantile regression technique, we can effectively estimate the entire probability distribution of the dependent variable given independent variables. This 12

ACCEPTED MANUSCRIPT merit then produces very robust and informative outcomes even for data embracing large heterogeneity with extreme outliers. To be specific, for the sample data that include large and non-normal disturbances, applying the conditional mean estimator to the regression might substantially produce biased estimates because these are prone to departure from normality.

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Thus, the OLS estimates might be seriously inefficient and biased. On the contrary, the quantile regression adopting the conditional median estimation is robust to departure from normality. For further details on this issue, see Mata and Machado (1996) and Fattouh et al.

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(2005), among others.

We briefly describe the estimation procedure of the quantile regression and principal

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properties of the estimator following Mata and Machado (1996) and Fattouh et al. (2005). Given any real random variable X , its distribution function characterizes this variable as F ( x ) = Pr( X ≤ x) .

(12)

Q (τ ) = inf{ x : F ( X ) ≥ τ } .

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Here, the τ th quantile, for 0 < τ < 1 , is defined as

(13)

Letting ( y i , xi ), i = 1,2,3,..., n, be a sample where yi is a dependent variable of interest and xi is

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a vector of regressors. Under an assumption that the τ th quantile of the conditional distribution of yi is linear in xi , the conditional quantile regression model could be constructed as

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yi = xi' βτ + uτi ,

(14)

Quantτ ( y i | xi ) ≡ inf{ y : Fi ( y | x)τ } = xi' β τ ,

(15)

Quant τ (uτi | x i ) = 0

(16)

where Quantτ ( y i | xi ) denotes the τ th conditional quantile of yi given the regressor vector xi ,

β τ is unknown vectors of parameters to be estimated for different values of τ ∈ (0,1) , uτ is the error term having a continuously differentiable c.d.f. Fuτ and a density function f uτ (. | x) .

Fi (. | x) denotes the conditional distribution function of y . Shifting the value of τ from 0 to 13

ACCEPTED MANUSCRIPT 1 enables us to trace the entire distribution of y given x . Thus, the quantile regression estimator for β τ can be obtained as the solution to the minimization problem below: n

min ∑ ρτ ( y i − xi' β τ )

(17)

i

τ u where ρ τ ( u ) =   (τ − 1) u

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if , u ≥ 0 if , u < 0

(18)

Linear programming techniques can be usefully applied for a solution to the resulting

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minimization problem (Koenker and Bassett, 1978; Mata and Machado, 1996; Koenker and Hallock, 2001; Fattouh et al., 2005). To obtain the standard errors for the coefficients from

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each quantile regression via the bootstrap method, two alternative approaches are generally considered: the design matrix bootstrap technique and the error bootstrap technique (Buchinsky, 1998; Fattouh et al., 2005). According to Buchinsky (1998), both are based on quite different assumptions with respect to the form of the asymptotic covariance matrix of

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β τ . The former has a merit of producing a consistent estimator of the asymptotic matrix under free conditions but the latter does a demerit of producing a consistent estimator exclusively under a strict one of independence (Buchinsky, 1998). Thus, these discussions

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allow us to use the former method. See Buchinsky (1995, 1998) for more details on this issue.

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3.4 The Conditional Quantile Regression Specified for Its Drivers Our conditional quantile regression with panel data of pairwise realized correlations among stock market returns in the Pacific Basin region is specified as follows:

Quant τ (Yit ( = ρ ij , t ) | X it ) = α τ 0 + βτ 1 InfDif ij , t −1 + βτ 2 IntDif ij , t −1 + βτ 3 IP _ Dif ij , t −1 + βτ 4 FX _ VolDif ij , t −1 + βτ 5 MarkCapDif

ij , t −1

+ + βτ 6USBill + βτ 7 1997 Dummy

+ βτ 8 2008 Dummy + βτ 9 2010 Dummy + ε it (19)

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ACCEPTED MANUSCRIPT where Quant τ (Yit ( = ρ sb ,t ) | X it ) represents the τ th conditional quantile of Y it ( = ρ sb ,t ) , the pairwise realized correlations among the sample stock market index returns at year t . The parameter α 0τ is an overall intercept for the individual quantile regressions. And the regressors

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of InfDif ij ,t −1 , IntDif ij ,t −1 , IP _ Dif ij ,t −1 , FX _ VolDif ij ,t −1 , MarkCapDif ij ,t −1 , and USBill t −1 respectively denote the exogenous macroeconomic variables chosen to account for the stock market comovements. The dummy variables of 1997 Dummy , 2008Dummy , and

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2010Dummy denote the intercept dummies for various regional and/or global financial crises in East Asia, the US, and Europe, respectively, employed to examine the influences of those

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financial crises on the stock market comovements in the region. ε it represents the error terms for cross-sectional units.

For our empirical model with the panel data on the pairwise realized correlations of the 9×8 ), 2

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sample stock market index returns, the cross-sectional dimension (N ) equals 36 ( =

which is the number of all possible pairs of the nine different countries in the sample. And because we have annual observations of the realized correlations among our stock market

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returns in the region from 1990 to 2012, the panel time series dimension (T ) is 23 (years). Thus, the final size of our panel data for the pairwise realized correlations among the sample

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stock market index returns in the Pacific Basin region is 828 ( = 36 × 23) .

4. Data Issues

4.1 Stock Returns

To compute the annual pairwise realized correlations among the nine stock market index returns in the Pacific Basin region, we use the daily stock index returns for our sample countries over the full sample period from January 1, 1990 to the end of December 2012. Our sample countries are the nine economies in the Pacific Basin region: Australia, China, Hong 15

ACCEPTED MANUSCRIPT Kong, Japan, Korea, Malaysia, Philippines, Singapore, and Thailand.2 All the daily stock index data have been obtained from the Datastream International. The U.S. dollar has been employed as the reference currency. The daily stock index return (continuously compounded) for country i at day d of year t has been computed as the log of the first difference for the closing index

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level p between a trading day d of year t and the previous trading day as in

ri ,t ,d = ln( p i ,t ,d / pi ,t ,d −1 ) × 100 .

Figure 1 shows the plots of the pairwise (Z Fisher transformed) realized correlations. We

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can see overall upward trends for all the plots in Figure 1, especially after the second half of the

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1990s, with peaks around the subprime mortgage crisis in the U.S. approximately 2008 and 2009. This implies that the (sample) stock markets in this region converged significantly after the Asian turmoil in 1997. In particular, we can observe that the stock market pair of the mainland China and Hong Kong has converged since Hong Kong returned to the Chinese

the sample pairs in general.

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government on July 1, 1997 showing the highest pairwise realized correlation series among all

Figure 1 around here

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4.2 Exogenous Independent Variables

Here we describe the exogenous independent variables to explain dynamic comovements of

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the Pacific Basin -stock markets in more detail. The exogenous variables are mostly thought to be related to macroeconomic performance and/or capital market characteristics of target countries whose existing studies on stock market comovements commonly employ (e.g., Pretorius, 2002; Kim et al., 2005; Beine and Candelon, 2011; Bekaert et al., 2013 among others). Specifically, to find drivers of dynamic integration of international advanced stock markets, Kim et al. (2005) adopt a variety of macroeconomic (monetary) performance 2

Taiwan, one of the major economies in the region, was excluded from the sample because some

variables of our interest for Taiwan are unavailable or only partially available for the sample period.

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ACCEPTED MANUSCRIPT variables, such as inflation, short term interest rate, industrial production, and exchange rate volatility. Focusing on interdependence of emerging stock markets, Beine and Candelon (2011) and Pretorius (2002) substantially investigate the economic effects of the macroeconomic performance difference variables for pairs of countries as drivers of stock market

macroeconomic

performance

difference

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interdependence. Following Beine and Candelon (2011) and Pretorius (2002), our study uses variables

denoted

by InfDif ij ,t −1 , IntDif ij ,t −1 , IP _ Dif ij ,t −1 and FX _ VolDif ij ,t −1 . These variables represent the 1st

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lagged values of pairwise inflation differentials, pairwise 3-month short-term interest rate differentials, pairwise industrial production differentials, and pairwise exchange rate volatility

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differentials between the sample countries, respectively. Here, the lagged inflation differentials ( InfDif ij ,t −1 ) has been computed as differences of the CPIs (consumer price indices) for pairs of our sample countries. The variable MarkCapDif ij ,t −1 , employed to

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examine the effect of differences in capital market development across our sample countries on the stock market comovements in the region, has been computed as the first lag of market capitalization differentials across the sample countries. In association with this variable, an

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empirical study by Kim et al. (2005) suggests that this driver contributes to an increase in integration of advanced international stock markets. In a similar vein, Bekaert et al. (2013)

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also use the absolute difference of the market capitalization to GDP for pairs of their sample countries as an important independent variable. The first lagged short term interest rate of 3month U.S. Treasury bill denoted by USBill t −1 , a common proxy for the global risk-free rate of return, has been adopted to control for its worldwide effect on comovements of stock markets in this region. The data frequency for the whole exogenous independent variables is all annual to match the frequency of the dependent variable, the annual pairwise realized correlations among the sample stock markets. The raw data for these variables has been extracted from the Datastream International. 17

ACCEPTED MANUSCRIPT 4.3 Crisis Dummies for Global Risks We use the three intercept dummies of the 1997dummy, the 2008dummy, the 2010dummy to control for the effects of various regional and global financial crises in East Asia, the US, and Europe, respectively. By definition, these dummies take a value 1 for the years 1997, 2008,

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and 2010 and 0 for the other years.

Table 1 presents the descriptive statistics for the exogenous variables over the full sample period. Most of the variables show positive skewness while the 3-month U.S. Treasury bill

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rate shows negative skewness. Likewise, the variables, overall, show high values of kurtosis ranging from 13.827 to 4.285 except for the 3-month US Treasury bill rate. The substantially

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skewed and leptokurtic natures of the distributions of the whole variable suggest the necessity of using a quantile regression technique that allows us to focus on the median estimation rather the mean one to rigorously examine economic drivers for dynamic comovements of stock markets in this region. Furthermore, from the perspective of robust statistics, the median

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estimation approach is more robust than the mean estimation one (Portnoy and He, 2000, Stigler, 2010 among others).

[Table 1 around here]

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5. Empirical Results

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In this section, we present the empirical results for the OLS and quantile regressions at the 5th, 10th, 25th, 50th, 75th, 90th, and 95th quantiles of the entire distribution for the pairwise realized correlations among the sample stock market index returns in the Pacific Basin region. Prior to the actual analyses, we examine the pairwise correlations of our independent variables in Table 2 to check any potential multicollinearity. Overall, the low correlation estimates in Table 2 suggest that there is no serious multicollinearity problem even if all the variables are simultaneously inclusive in the regression models. The panel LLC (Levin et al., 2002) and IPS (Im et al., 2003) tests, which are commonly used in the unitroot test of panel data units,

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ACCEPTED MANUSCRIPT for all the level variables in this study significantly reject the null hypothesis of unitroots at standard levels, respectively. This result suggests that all the exogenous variables are stationary over the years. 3

[Table 2 around here]

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Table 3 presents the estimating results for Eq. (19), the quantile regression model at the seven conditional quantiles ( τ ) of the distributions on the pairwise realized correlations among the stock market index returns of the sample countries over the full sample periods.

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For comparison, the OLS estimates are also reported in column 1 of the table.

[Table 3 around here]

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Let us first examine the effects of monetary performance similarities on stock market comovements in the region. For the inflation differentials (InflDif), a pairwise monetary performance difference proxy, the OLS model exhibits a significantly negative coefficient of 0.816. This suggests that lower price differences (i.e., higher price similarities) between any

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two countries in the region contribute to stronger stock market comovements between the two on average. For the same variable, however, the conditional quantile regression models exhibit significantly negative coefficients of -1.102 and -1.564 (whose absolute values are

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greater than that of their OLS counterpart) only at the middle ( τ 50 ) and the higher ( τ 75 )

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quantiles showing insignificant coefficients at all the other quantiles in the analysis. This implies that the negative effects of price differences on stock market comovements only hold at the middle and the mid-high quantiles of the pairwise realized correlation distribution for the stock index returns.

For the (short-term) interest rate differentials (InterDif), another proxy for pairwise monetary performance differences, both the OLS regression and the quantile regressions, all show significantly negative coefficients. This implies that lower interest rate differences (i.e.,

3

The details are omitted to save space but are available upon request.

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ACCEPTED MANUSCRIPT higher interest rate similarities) lead to greater stock market comovements in the Pacific Basin region in general. However, compared to the quantile regression results with their OLS counterpart, we can observe a nonlinear pattern across the quantiles: weaker effects for lower quantiles and stronger effects for higher quantiles in general with a peak of -0.041 at the τ 90

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quantile. This suggests that this variable has a nonlinear impact on stock market comovements depending on different conditional quantiles of the pairwise realized correlations among the stock returns of the region. Based on the results above, it can be said that higher pairwise

stock market comovements in the region.

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convergence of monetary performances among the sample countries is associated with greater

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The industrial production differentials (IP_Dif), a proxy for growth differences, also show a significant negative coefficient (-0.141) in the OLS model. This reflects that lower IP differences are associated with stronger stock market comovements on average. The conditional quantile regressions also produce significantly negative coefficients at all

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quantiles except for quantile τ 75 . However, they show relatively stronger effects (negative coefficients with bigger absolute values) for higher quantiles and relatively weaker effects (negative coefficients with smaller absolute values) for lower quantiles. This provides

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evidence of a nonlinear way in which similarity of industrial production contributes to stock

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market comovements in the region depending on different quantiles of the stock return correlation distribution.

To examine the effect of exchange rate volatility on stock market comovements, we employed the pairwise exchange rate volatility differentials (FX_VolDiff) among sample countries as an independent variable of our analyses. The variable returns significantly negative estimates for the OLS regression and most of the quantile regressions, as expected, except for the quantile τ 75 showing an insignificant coefficient. Here again, the quantile regressions exhibit significantly stronger effects for upper quantiles and significantly weaker 20

ACCEPTED MANUSCRIPT effects for lower quantiles. This also shows the nonlinearity by which this variable makes different effects on stock market comovements in the region conditional on different quantiles of the pairwise realized correlation distribution. To analyze the effects of market size differences on stock market comovements, we used

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the stock market size differences proxied by the pairwise market capitalization differentials among the sample countries as an independent variable. The variable turns out to form a negative relationship with the market comovements with a significantly negative coefficient

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in the OLS regression. However, the quantile regressions reveal increasingly stronger significantly negative effects between the quantiles τ 5 − τ 75 but insignificant ones at the

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highest quantiles τ 90 − τ 95 of the realized stock return correlation distribution. This result suggests that market size differentials have a different negative effect on stock market comovements across various quantiles of the pairwise realized correlation distribution forming a nonlinear relationship with the dependent variable (i.e., the pairwise realized

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correlation). This also goes with our theoretical expectation that higher macroeconomic similarities will lead to stronger market comovements. Overall, the empirical findings discussed so far firmly support our theoretical explanation on stock market comovements:

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macroeconomic similarities promote stock market comovements between two countries.

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For the other control variables, let us first examine the 3-month U.S. Treasury bill rate (USBill), our proxy for the global risk-free rate of return. Table 3 shows that the global riskfree rate of return also makes negative impacts on the market comovements for both the OLS regression and the quantile regressions all exhibiting highly significant negative coefficients at the 1% significance level. Like the aforementioned variables, this variable also shows generally greater negative impacts at the higher quantiles representing a nonlinear relationship with the pairwise realized correlation conditional on the quantiles of the correlation distribution. In addition, we have tested possible effects of both trade relation differences 21

ACCEPTED MANUSCRIPT proxied by trade openness differentials and political stability differences proxied by political corruption freedom differentials between pairwise countries on the market comovements. Our linear OLS regression estimates insignificant coefficients for the two variables. Nonlinear quantile regressions also do not estimate significant values for both at standard levels, except

τ 95 ) on the political corruption freedom differentials. The results

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for the lowest quantile (

address no economic impacts of the two economic difference variables on stock market comovements in this region. The specific results are not reported in this paper. 4

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As for the three crisis dummies, the OLS regression returns all significantly positive

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coefficients of 0.205, 0.471, and 0.182, respectively, for these dummies. This implies that all three economic crises in Asia, the U.S., and Europe during the sample period contributed to an increase in stock market comovements in this region. The empirical result supports our theoretical expectation discussed in Eq. (9) that if both markets are affected by a global crisis in the same direction, the stock market correlation between two countries increase because

similar ways.

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our sample economies are known to be mostly affected by each of the economic crises in

In case of the conditional quantile regressions, the three dummy variables form a variety

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of features across the quantiles of the realized correlation distribution. As to the 1997Dummy,

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the quantile regressions estimate significantly positive coefficients of 0.296, 0.275, 0.301, and 0.235 at the lower and the middle quantiles (i.e., τ 5 , τ 10 , τ 25 , τ 50 ) but insignificant

4

The raw data for the two variables are gathered from the Heritage Foundation (the 2016 INDEX of

Economic Freedom) and the World Bank Database, respectively. The data of the political corruption freedom cover only the period of 1995-2012 due to limited data availability. Note that for the trade openness differentials, we use the first difference variable because the panel LLC (Levin et al., 2002) and IPS (Im et al., 2003) tests for this variable do not reject the null hypothesis of unitroots at all. Due to insufficient availability of the data over the full sample period and to save space, the detailed results are not reported but are available upon request.

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ACCEPTED MANUSCRIPT coefficients at the other higher quantiles. Regarding the 2008Dummy of the U.S. subprime mortgage crisis in 2008, the quantile regressions show highly significant positive coefficients only at the quantiles of τ 5 , τ 10 , τ 25 , τ 50 , τ 75 with smaller values for higher quantiles. This suggests a bigger impact of the 2008 crisis from the U.S. on the stock market comovements at

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lower quantiles of the realized correlation distribution among the sample stock markets. Lastly, regarding the effect of the European fiscal crisis in 2010, we can see significantly positive estimates by the quantile regression only at the three quantiles of τ 10 , τ 25 , τ 75 , which

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are bigger than the one (0.182, also significant) by the OLS regression. This reflects the

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different impacts of this crisis on the stock market comovements across various quantiles of the realized correlations. Shortly, the results for these three dummies imply that financial crises have different impacts on the stock market comovements in the Pacific Basin region across different quantiles of their pairwise realized correlations forming some nonlinear relationships with the realized correlation distribution.

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Such nonlinear relations between the stock market comovements and the determinants may arise due to the existence of different investor groups having heterogeneous expectations on the future state of affairs of their assets, dividends, and/or the stock market comovements

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determinants. For instance, one group of investors might be fundamentalists who believe asset

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prices will return to their fundamental equilibrium prices while another group might be chartists who try to predict the future asset prices by analyzing the patterns of their past prices. In addition, these nonlinearities reflect that investors have heterogeneous pricing mechanisms for the discount factors of macro-economic (similarity) effects by different extents of comovements among the stock markets for their effective diversification in the region. That is, the nonlinearities suggest that stock markets in pairs of countries with lower macroeconomic similarities are partially integrated or segmented while ones in pairs of countries with higher similarities are fully integrated. Generally, market participants do not enjoy international 23

ACCEPTED MANUSCRIPT diversification effects in integrated markets but vice versa in segmented markets. As we can see from the results on the nonlinearities between the stock market comovements and its determinants, whatever the true reason for such nonlinearities may be, it is not too much to say that we should use the quantile regression to accurately study the stock market

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comovements in this region.

To test for whether the coefficients of the same variables actually differ across the quantiles in the quantile regression, we additionally performed an F test on the null

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hypotheses that the coefficients of the quantile regression for quantile τ a is statistically equal

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to those for quantile τ b . Extending the bootstrap procedure to construct a joint distribution, we conduct the F test for various pairs of the quantiles in analysis. We use the bootstrapped standard errors from 1,000 replications (See Arias et al. (2001), Fattouh et al. (2005), Fattouh et al. (2008), and Fang et al. (2010) for a similar application.). Table 4 shows the F statistics and the associated values for the equality of the estimated coefficients for various quantile

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pairs over the full sample period.

[Table 4 around here]

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The F statistics for most pairs, except for a few pairs such as τ 5 − τ 10 , τ 10 − τ 25 , τ 25 − τ 50 ,

τ 25 − τ 75 , τ 75 − τ 90 , and τ 90 − τ 95 , have turned out to significantly reject the null hypotheses of

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homogeneous coefficients at the standard significance levels. This statistically confirms the different impacts of our variables on the stock market comovements, which we have discussed up to now, at most of the pairs. Moreover, we have also applied the F test to the null hypothesis that the coefficients of each variable are all equal across the different quantiles for the quantile regression. As we can see, the F statistic (5.94) strongly rejects the null hypothesis of homogeneous coefficients across all the quantiles. This suggests different impacts of the variables across different quantiles. Overall, the results in Table 3 on the nonlinear relationships between the stock market comovements and its determinants justifies 24

ACCEPTED MANUSCRIPT our use of the quantile regression in this study, implying that the results from the OLS regression may not be entirely solid. Figure 2 depicts the quantile-wise variations of the coefficient estimates by the quantile regression compared with the estimates by the OLS regression over the full sample period. In

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Figure 2, we can graphically see that the coefficients of the exogenous variables in this study clearly form nonlinear relationships with the pairwise realized correlation across the quantiles of the realized correlation distribution.

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[Figure 2 around here]

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6. Conclusions

This paper aims to investigate economic drivers for stock market comovements in the Pacific Basin region. To this end, we have employed the conditional quantile regression, which allows us to capture the nonlinear relationships of stock market comovements and its determinants, and compared the results with those of the OLS regression.

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The principal findings of our study are as follows: First, lower differentials (higher similarities) of monetary performance, such as pairwise differences in inflation rates and

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interest rates among the sample countries, seem associated with time-varying comovements of the stock markets in this region. Second, the pairwise growth differentials of industrial

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production among the sample countries have turned out to have a negative relation with stock market comovements. Third, higher similarities (lower differentials) of exchange rate volatility between two countries have proved to promote market comovements. Fourth, with respect to the impact of capital market development, smaller differences of stock market sizes proxied by the stock market capitalization have been shown to contribute to stock market comovements in this region. And fifth, the 3-month U.S. Treasury bill rate, our proxy for the global risk-free rate of return, has shown a negative effect on market comovements. Finally, our study further shows that the three recent global economic crises (i.e., the Asian crisis in 25

ACCEPTED MANUSCRIPT 1997, the U.S. subprime crisis in 2008, the European fiscal crisis in 2010) had positive effects on the time-varying comovements of stock markets in the Pacific Basin region. In short, smaller differences of macroeconomic variables across the countries contribute to stock market comovements in this region.

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Importantly, by comparing these results by the quantile regression with the ones by the OLS regression or with themselves, we showed that these impacts of our macroeconomic variables on the stock market comovements are surely nonlinear depending on the quantiles

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of the pairwise realized correlations of the stock indices, our measure for stock market comovements. Such nonlinearities detected in this study justifies our use of the quantile

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regression in studying the determinants of the stock market comovements in the region. Our study has invaluable implications for international investors and academics alike. First of all, the comovements dynamics among stock markets in the Pacific Basin region is an important concern for global investors who pursue internationally diversified investments.

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Referring to this study, policymakers and investors can narrow their attention to only some significant drivers of international stock market comovements. For academics, our study can provide a deeper understanding on the workings of the global stock market interdependence.

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Moreover, our study offers a ground to build up a theoretical framework for impacts of a variety of economic drivers on stock market comovements.

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However, despite such contributions, there are some limitations: Our theoretical framework cannot account for the nonlinearities captured in our empirical analyses. This challenging work to theoretically explain the nonlinear relationships would offer meaningful room for a more rigorous study on stock market comovements. Exploring comovements of stock markets in the Pacific Basin region at the firm and/or industry levels would also be interesting. In particular, it would be meaningful to examine the economic impacts of firm characteristic variables, such as corporate governance or dividend payout differences among

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ACCEPTED MANUSCRIPT firms, which are crucial drivers on corporate finance for stock market comovements in this

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region. These are beyond the scope of this study, so we leave them for future research.

Acknowledgement:

The authors are grateful to Prof. C. R Chen, the Editor and anonymous reviewer(s) for valuable comments to improve the paper but are responsible for any remaining errors.

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The study was supported by research fund from Chosun University, 2015.

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Figure 1. Time varying (Z- Fisher transform) realized correlations between stock market returns in the Pacific Basin region 3

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11

12

13

14

15

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

34

35

36

TE D

M AN U

16

0 1 2 3 4

33

1990 1995 2000 2005 2010

1990 1995 2000 2005 2010

1990 1995 2000 2005 2010

1990 1995 2000 2005 2010

Year

AC C

EP

1990 1995 2000 2005 2010

Graphs by Panel Code

SC

0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4

Z_Correlations

1990 1995 2000 2005 2010

RI PT

2

0 1 2 3 4

1

Note. Panel Code: 1. Australia-China, 2. Australia-Hong Kong, 3. Australia-Japan 4. Australia-Korea, 5. Australia-Malaysia, 6. Australia-Philippines, 7. AustraliaSingapore, 8. Australia-Thailand, 9. China-Hong Kong, 10. China-Japan, 11. China-Korea, 12. China-Malaysia, 13. China-Philippines, 14. China-Singapore, 15. China.-Thailand, 16. Hong Kong-Japan, 17 Hong Kong-Korea, 18. Hong Kong-Malaysia, 19. Hong Kong-Philippines, 20. Hong Kong-Singapore, 21. Hong Kong-Thailand, 22. Japan-Korea, 23. Japan-Malaysia, 24. Japan-Philippines, 25. Japan-Singapore, 26. Japan-Thailand, 27. Korea-Malaysia, 28. KoreaPhilippines, 29. Korea-Singapore, 30. Korea-Thailand, 31. Malaysia-Philippines, 32. Malaysia-Singapore, 33. Malaysia-Thailand, 34. Philippines-Singapore, 35. Philippines-Thailand, 36. Singapore-Thailand.

34

ACCEPTED MANUSCRIPT Table 1. Descriptive statistics for the exogenous variables

Variables

Mean

InflDif

0.033

0.032

InterDif

3.649

Min

Max

Skewness

Kurtosis

Obs

0.024

0

0.270

2.520

13.060

828

3.0440

2.838

0

15.632

1.256

4.285

828

0.429

0.079

0

2.111

3.007

11.328

828

0.231 0.088

0.103

0.057

0

0.698

MarkCapDif

0.327

0.332

0.231

0

2.451

USBill

3.098

2.153

3.22

0.010

6.741

2.549

10.951

828

2.260

10.447

828

-0.161

1.647

828

SC

FX_VolDif

RI PT

IP_Dif

Std. Dev. Median

InflDiff

InterDiff

InflDif

1

InterDif

0.355

1

IP_Dif

0.133

0.217

FX_VolDif

0.151

MarkCapDif

0.135

USBill

0.308

IP_Diff

FX_VolDiff

MarCapDiff

USBill

1

TE D

Variable

M AN U

Table 2. Correlation matrices across the exogenous variables

-0.005

1

0.180

0.079

0.287

1

0.345

-0.065

0.171

0.132

1

AC C

EP

0.222

35

ACCEPTED MANUSCRIPT Table 3. Results of quantile regression for the pairwise realized correlations among the stock market index returns (Obs. = 828) OLS Quantile regressions

τ5

τ 10

τ 25

τ 50

τ 75

τ 90

τ 95

1.310a

0.502a

0.615a

0.915a

1.197a

1.635a

1.977a

2.164a

(0.064)

(0064)

(0.060)

(0.054)

(0.049)

(0.054)

(0.056)

(0.091)

b

c

-2.053

-1.938

(1.351)

(1.772)

-0.816

(0.475)

IP_Dif

FX_VolDif MarkCapDif

1997Dummy

(0.519)

(0.362)

(0.434)

(0.512)

-0.017a

-0.027a

-0.036a

(0.005)

(0.005)

(0.005)

(0.007)

(0.006)

-0.141a

-0.121 a

-0.090c

-0.099c

-0.058

(0.029)

(0.045)

(0.050)

(0.053)

-0.636a

-0.440a

-0.558a

-0.578a

(0.178)

(0.157)

(0.124)

(0.175)

-0.131b

-0.106a

-0.149a

(0.058)

(0.031)

a

a

-0.063

-1.564

(0.938)

-0.031c

-0.041a

-0.030c

(0.011)

(0.010)

(0.016)

-0.154a

-0.230a

-0.353a

(0.040)

(0.035)

(0.035)

(0.049)

-0.630b

-0.385

-0.667c

-1.077a

(0.321)

(0.393)

(0.387)

(0.394)

-0.168b

-0.177b

-0.185c

-0.058

-0.001

(0.028)

(0.059)

(0.076)

(0.115)

(0.117)

(0.152)

a

a

a

a

a

-0.076a

-0.059

-0.073

-0.056

-0.078

-0.079

(0.009)

(0.015)

(0.012)

(0.012)

(0.011)

(0.013)

(0.016)

(0.022)

0.205a

0.296a

0.275a

0.301a

0.235b

0.027

0.166

0.069

(0.078)

(0.094)

(0.077)

(0.114)

(0.137)

(0.151)

(0.185)

0.336

0.281

a

(0.099) 0.182b (0.089)

(0.094)

(0.094)

(0.108)

(0.072)

(0.098)

(0.157)

(0.221)

(0.657)

0.179

0.256b

0.256a

0.177

0.266b

0.099

0.151

(0.119)

(0.122)

(0.098)

(0.148)

(0.122)

(0.174)

(0.313)

0.569

a

EP

2010Dummy

-1.102

-0.014a

0.471

2008Dummy

-0.592

-0.034a

-0.076

USBill

-0.058

TE D

InterDif

0.546

SC

InflDif

c

M AN U

Intercept

RI PT

Variables

0.647

a

0.515

a

0.478

a

0.392

a

Notes. The figures in parentheses for the OLS regression are robust standard errors while those for the

AC C

quantile regressions are bootstrapped ones based on 1,000 replications. a, b, and c respectively indicate significance at the 1%, 5%, 10% levels.

36

ACCEPTED MANUSCRIPT Table 4. F statistics for coefficients on the exogenous variables across the conditional quantile regressions

τ5 τ 10

τ 10

τ 25

τ 50

τ 75

τ 90

0.88

τ 75 τ 90

1.35

(0.029)

(0.207)

2.87***

2.22**

0.87

(0.002)

(0.019)

(0.549)

2.80***

2.22**

1.08

(0.003)

(0.019)

(0.377)

3.68 (0.002)

τ 95

3.35 ***

***

(0.000)

4.52***

4.39***

(0.000)

(0.00)

(0.107) 2.51***

1.20

(0.007)

(0.292)

2.93***

3.70***

2.31**

1.17

(0.002)

(0.000)

(0.014)

(0.312)

1.87

**

1.61*

SC

τ 50

2.08**

(0.053)

M AN U

τ 25

RI PT

(0.542)

Homogeniety F statistic on the variables across all the quantiles: 5.94***(0.000) Notes. The figures in parentheses are p-values based on the bootstrap method of 1,000 simulations. ***, , and * respectively indicate significance at the 1%, 5%, and 10% levels.

AC C

EP

TE D

**

37

ACCEPTED MANUSCRIPT

.25

.5 Quantile

.75

.9 .95

Market CapitalisationDifferentials

-0.50 -0.40 -0.30 -0.20 -0.10 0.00

.75

.9 .95

.5 Quantile

.75

.9 .95

TE D 0.00

.1

.25

Industrial ProductionDifferentials

RI PT SC .5 Quantile

-0.40 -0.20

EP

AC C

FX_Volatility Differentials

-1.50 -1.00 -0.50 0.00

.25

US-3MonthBill

.1

.1

.1

.25

.5 Quantile

.75

.9 .95

.1

.25

.5 Quantile

.75

.9 .95

-0.14 -0.12 -0.10 -0.08 -0.06 -0.04

.9 .95

M AN U

.75

-0.08-0.06 -0.04 -0.02 0.00 0.02

Interes tRateDifferentials .5 Quantile

0.40

.25

0.20

.1

0.50

InflationDifferentials

-6.00 -4.00 -2.00 0.00 2.00 4.00

Figure 2. Variations in coefficient estimates for the exogenous variables

Notes. The conditional quantiles on the x-axis for the distribution of the pairwise realized correlations given the first lagged exogenous variables including the intercept range from 0 for the lowest to 1 for the highest. The 95% confidence intervals (bootstrapped) are up to two standard errors away from the means in both directions. The horizontal lines are the OLS estimates with 95% confidence intervals.

38

ACCEPTED MANUSCRIPT

What Drives Dynamic Comovements of Stock Markets in the Pacific Basin Region?: A Quantile Regression Approach

AC C

EP

TE D

M AN U

SC

RI PT

Highlights
Macroeconomic drivers of stock market comovements in the Pacific Basin region are detected.
A simple theoretical argument is proposed on this issue.
The conditional nonlinear quantile regression is applied.
Pairwise realized stock return correlations in the region are analyzed.
Macroeconomic similarities turn out to nonlinearly drive stock market comovements.