Structural Breaks, Dynamic Correlations, and ...

Structural Breaks, Dynamic Correlations, and Hedge and Safe Havens for Stock and Foreign Exchange Markets in Greater China

Xiyong Dong Department of Economics, Pusan National University 2, Busandaehak-ro 63, beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea. Email: [email protected]

Seong-Min Yoon Corresponding author Department of Economics, Pusan National University 2, Busandaehak-ro 63, beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea. Email: [email protected].

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Structural Breaks, Dynamic Correlations, and Hedge and Safe Havens for Stock and Foreign Exchange Markets in Greater China

This study investigates the dynamic relationship and hedging strategy between stock and foreign exchange markets in Greater China for the period July 22, 2005, to November 30, 2015. Using the DCC-GARCH model with and without structural breaks, the results provide strong evidence of negative time-varying correlations between these two markets in Greater China, consistent with the ‘stock-oriented’ models of exchange rates. Moreover, adding structural breaks reduces the degree of volatility persistence for all considered markets and can provide more accurate hedge and safe haven properties of the U.S. dollar against Greater China stock markets. The findings have several important implications for portfolio risk managers, international investors, and policy makers.

Keywords: Greater China; Stock markets; Foreign exchange markets; Dynamic correlations; DCC-GARCH model; Structural breaks. JEL classification: C58; F31; G11; G15

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1. INTRODUCTION During the past few decades, the relationship between stock prices and exchange rates has attracted special attention from economists, portfolio risk managers, and policy makers. These two variables are important and intrinsically linked macro financial variables. Previous literature provides two theoretical aspects to explain the interactions between stock prices and exchange rates. On the one hand, Dornbusch and Fischer (1980) develop the ‘flow-oriented’ models of exchange rates that focus on the current account or the trade balance, and posit that the changes in exchange rates will affect international competitiveness and trade balances directly, thereby influencing countries’ real incomes and output. Since stock prices are generally interpreted as the present values of firms’ future cash flows, they respond to exchange rate changes. This is consistent with the international trading effect (Aggarwal, 1981); thus, changes in exchange rates affect stock prices positively via international competitiveness and trade balances. However, we argue that this model does not account for all cases influenced by exchange rate changes. For example, sometimes, domestic currency depreciation increases the pressure of domestic capital outflow, and domestic investors will change domestic currency into foreign currency or foreign currency-denominated financial products, thereby reducing the domestic money supply and stock prices. In the latter situation, changes in exchange rates affect stock prices negatively via the domestic capital outflow. On the other hand, Branson (1983) and Frankel (1983) present the ‘stock-oriented’ models of exchange rates that focus on the impact of stock prices on exchange rates, 3

which state that changes in stock prices will affect domestic capital inflows and outflows, thereby affecting the demand for money, which then leads to changes in interest rates and exchange rate movements. This is in agreement with the portfolio balance effect (Bahmani-Oskooee and Sohrabian, 1992) and monetarist models of exchange rate determination (Gavin, 1989); thus, changes in stock prices affect exchange rates negatively via capital mobility. The negative relationship between these two markets would have important implications for investors in terms of diversifying benefits. Since the Chinese government opened the Shanghai and Shenzhen stock exchange markets in 1990 and 1991, respectively, the Greater China stock markets (namely, Shanghai, Shenzhen, Hong Kong, and Taiwan) have grown quickly and become increasingly attractive to foreign capital. Moreover, after the 2008-2009 global financial crisis (GFC), these emerging economies applied rapid financial reform to move toward globalisation (Lin and Fu, 2016), thus increasing their interaction with the global economy through capital flows. Richards (2005) indicates that foreign capital flows can increase and decrease stock prices, and capital inflows and stock returns are positively related, particularly in emerging markets. Thus, considerable quantities of international capital flows among the currency markets in Greater China may affect performance in the stock and foreign exchange markets. Johansson and Ljungwall (2009) indicate that geographically and economically close stock markets exhibit significant influence over each other. We argue that selecting highly relevant politically and economically markets for an analysis will yield 4

much useful information. In addition, the Shanghai-Hong Kong Stock Connect and the Shenzhen-Hong Kong Stock Connect programs were launched on November 17, 2014 and December 5, 2016, respectively. Hence, Hong Kong will become an important overseas investment market for mainland investors, and more global money may enter the Chinese mainland stock markets. This study explores the dynamic relationship between stock prices and exchange rates in Greater China and provides a hedging strategy. First, we obtain the dynamic correlations between these two markets using Engle’s (2002) dynamic conditional correlation (DCC) GARCH model. Second, to test whether the U.S. dollar (USD) represents a hedge and safe haven properties against stock markets, we specify a dummy variable regression model. Since major regional and global economic events can bring about structural breaks in financial markets, ignoring structural breaks in the GARCH models may lead to an overestimate of the degree of volatility persistence (Lamoureux and Lastrapes, 1990; Aggarwal et al., 1999). To address this problem, we apply Inclán and Tiao’s (1994) Iterated Cumulative Sum of Squares (ICSS) algorithm to capture structural breaks. Our paper adds to and differs from the related literature in several aspects. First, we analyse the dynamic conditional correlations between stock markets and foreign exchange markets in Greater China. Second, though structural breaks could affect the relationship between financial markets, to our knowledge, no study explores the relationship between stock prices and exchange rates in terms of structural breaks. To fill this gap, we apply Inclán and Tiao's (1994) ICSS algorithm to capture structural 5

breaks, thereby improving the accuracy hedging strategy calculations. Finally, since gold is a traditional hedging asset, many studies examine the hedging role of gold against stocks, few investigate the role of USD as a hedge and safe haven under regular and extreme stock market conditions. The rest of this paper proceeds as follows. Section 2 provides a brief review of the literature. Section 3 describes the methodology. Section 4 defines the data and conducts some preliminary analysis. Section 5 provides the empirical results. Section 6 draws conclusions and policy implications.

2. LITERATURE REVIEW Researchers have adopted two sets of theoretical models to interpret the relationship between stock prices and exchange rates. When international trade plays an important role in a country's economy or considerable quantities of foreign capital will not enter a country’s capital market, researchers could adopt the ‘flow-oriented’ models of exchange rates. Aggarwal (1981) examines the relationship between U.S. stock prices and exchange rates from 1974 to 1978 and finds that these two markets are positively related during this period. Roll (1992) also finds a positive relationship between stock prices and exchange rates for 24 industrial economies from 1988 to 1991. Phylaktis and Ravazzolo (2005) use a co-integration methodology and multivariate Granger causality tests and find that two markets in Pacific Basin countries are positively related from 1980 to 1998.

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On the other hand, international capital flows among the currency markets are increasingly frequent and extensive during the few decades, now implying that the ‘stock-oriented’ models of exchange rates are widely used. In contrast to Aggarwal (1981), Soenen and Hennigar (1988) indicate that the relationship between U.S. stock prices and exchange rates was negative from 1980 to 1986. Kim (2003) finds that the stock and foreign exchange markets are negatively correlated in both the short and long term in the U.S. from 1974 to 1998. Using the quantile regression model, Tsai (2012) finds that the negative relationship between stock and foreign exchange markets is more obvious when exchange rates are extremely high or low in Asian markets. Liang et al. (2013) examine the relationships between the stock and currency markets in the ASEAN-5 countries, and their results support the ‘stock-oriented’ models of exchange rates. More recently, Reboredo et al. (2016) also document a positive relationship between stock prices and currency values in emerging economies. It is well known that Asian emerging economies are more export-dominant. Ma and Kao (1990) indicate that currency depreciation has a positive effect on the domestic stock market for an export-dominant country. However, the quantities of international capital flows among Asian currency markets have affected the performance of these two markets. For example, Fang (2002) provides clear evidence that currency depreciation negatively affected stock returns for the four Asian Tigers and Thailand during the Asian crisis. Lin (2012) indicates that the relationship between exchange rates and stock prices in Asian emerging markets is generally driven by international investment capital flows rather than international trade flows. Furthermore, Moore and 7

Wang (2014) apply the estimated DCC coefficient in a regression on the potential determinants of the correlation and discover increased capital mobility for some Asian emerging markets after the Asian crisis. Despite numerous studies showing mixed results for the relationship between stock prices and exchange rates, 1 more recent studies have consistently confirmed this relationship due to the increasing integration among financial markets over the past twenty years (Do et al., 2015). Thus, the portfolio balance effect is more important in Asian emerging markets during the last two decades. To the best of our knowledge, while some studies focus on the relationship between stock prices and exchange rates in BRICS or BRIC countries, 2 few explore the relationship between these two markets in Greater China. We believe that our study is important since the launch of the Shanghai-Hong Kong and Shenzhen-Hong Kong Stock Connect programs. Additionally, if our empirical results support the ‘stockoriented’ models of exchange rates, constructing a portfolio comprised of stocks and USD is desirable thing for Greater China investors. Thus, we examine the hedge and safe haven property of USD against stocks to provide investors with useful investment information.

3. METHODOLOGY 3.1. The DCC-GARCH Model

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For example, see Ajayi and Mougoué (1996), Granger et al. (2000), Pan et al. (2007), Caporale et al. (2014), and Lin and Fu (2016). 2 For example, see Chkili and Nguyen (2014), Ho and Huang (2015), and Sui and Sun (2016). 8

Prior studies successfully applied Engle’s (2002) DCC-GARCH model to explore the dynamic correlation between the stock and foreign exchange markets because the conditional correlation matrix is designed to be time-varying. We define the multivariate DCC-GARCH model as follows: 𝑟𝑟𝑡𝑡 = 𝜇𝜇 + 𝜀𝜀𝑡𝑡 , 𝜀𝜀𝑡𝑡 |𝐼𝐼𝑡𝑡−1 ~𝑁𝑁 (0, 𝐻𝐻𝑡𝑡 ).

(1)

where 𝑟𝑟𝑡𝑡 = (𝑟𝑟1,𝑡𝑡 , 𝑟𝑟2,𝑡𝑡 , … , 𝑟𝑟𝑛𝑛,𝑡𝑡 )́ is the vector of log returns of n assets at time t, 𝜇𝜇 = (𝜇𝜇1 , 𝜇𝜇2 , … , 𝜇𝜇𝑛𝑛 )́ is the vector of the expected value of the conditional 𝑟𝑟𝑡𝑡 , 𝜀𝜀𝑡𝑡 =

(𝜀𝜀1,𝑡𝑡 , 𝜀𝜀2,𝑡𝑡 , … , 𝜀𝜀𝑛𝑛,𝑡𝑡 )́ is the vector of the conditional residuals, that is, 𝐸𝐸[𝜀𝜀𝑡𝑡 ] = 0, 𝐶𝐶𝐶𝐶𝐶𝐶[𝜀𝜀𝑡𝑡 ] = 𝐻𝐻𝑡𝑡 , and 𝐻𝐻𝑡𝑡 is the (𝑛𝑛 × 𝑛𝑛) conditional covariance matrix. We use the

standardised residuals 𝑧𝑧𝑖𝑖,𝑡𝑡 = 𝜀𝜀𝑖𝑖,𝑡𝑡 ⁄�ℎ𝑖𝑖𝑖𝑖,𝑡𝑡

to estimate time-varying conditional

covariances. In the DCC-GARCH model, we decompose the covariance matrix into 𝐻𝐻𝑡𝑡 = 𝐷𝐷𝑡𝑡 𝑅𝑅𝑡𝑡 𝐷𝐷𝑡𝑡 , where 𝐷𝐷𝑡𝑡 = 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑��ℎ𝑖𝑖𝑖𝑖,𝑡𝑡 �.

(2)

In Eq. (2), 𝑅𝑅𝑡𝑡 is the (𝑛𝑛 × 𝑛𝑛) conditional correlation matrix of standardised

residuals 𝑧𝑧𝑡𝑡 at time t and 𝐷𝐷𝑡𝑡 is the (𝑛𝑛 × 𝑛𝑛) diagonal matrix of conditional standard deviations for each of the return series at time t, which we obtain by estimating a

univariate GARCH model with 𝑑𝑑𝑑𝑑𝑑𝑑𝑔𝑔��ℎ𝑖𝑖𝑖𝑖,𝑡𝑡 � . The standard deviations in 𝐷𝐷𝑡𝑡 are

governed by the following univariate GARCH (1, 1) process: 2 ℎ𝑖𝑖𝑖𝑖,𝑡𝑡 = 𝜔𝜔𝑖𝑖 + 𝛼𝛼𝑖𝑖 𝜀𝜀𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽𝑖𝑖 ℎ𝑖𝑖𝑖𝑖,𝑡𝑡−1 .

(3)

In the second step, we define the structure of the DCC-GARCH model as follows: 𝑄𝑄𝑡𝑡 = (1 − 𝛼𝛼 − 𝛽𝛽)𝑄𝑄� + 𝛼𝛼𝑧𝑧𝑡𝑡−1 𝑧𝑧𝑡𝑡−1 ́ + 𝛽𝛽𝑄𝑄𝑡𝑡−1 , 𝑅𝑅𝑡𝑡 = �𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑(𝑄𝑄𝑡𝑡 )�

−1⁄2

𝑄𝑄𝑡𝑡 �𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑(𝑄𝑄𝑡𝑡 )�

−1⁄2

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.

(4)

where 𝑄𝑄𝑡𝑡 = �𝑞𝑞𝑖𝑖𝑖𝑖,𝑡𝑡 � is the (𝑛𝑛 × 𝑛𝑛) time-varying covariance matrix of standardised

residuals 𝑧𝑧𝑡𝑡 and 𝑄𝑄� = 𝐸𝐸[𝑧𝑧𝑡𝑡 𝑧𝑧𝑡𝑡́ ] is the (𝑛𝑛 × 𝑛𝑛) unconditional correlation matrix of

standardised residuals 𝑧𝑧𝑡𝑡 . 𝛼𝛼 and 𝛽𝛽 are the unknown parameters to be estimated. The

scalar parameters 𝛼𝛼 and 𝛽𝛽 must be non-negative and satisfy 𝛼𝛼 + 𝛽𝛽 < 1 to ensure

positivity of the matrix 𝑄𝑄𝑡𝑡 . The dynamic conditional correlations between two financial

markets, 𝑖𝑖 and 𝑗𝑗, in the DCC-GARCH model are finally given by: 𝜌𝜌𝑡𝑡𝐷𝐷𝐷𝐷𝐷𝐷 = [𝑅𝑅𝑡𝑡 ]𝑖𝑖𝑖𝑖 = 𝑞𝑞𝑖𝑖𝑖𝑖,𝑡𝑡 ⁄�𝑞𝑞𝑖𝑖𝑖𝑖,𝑡𝑡 𝑞𝑞𝑗𝑗𝑗𝑗,𝑡𝑡 ,

𝑖𝑖, 𝑗𝑗 = 1, 2, … , 𝑛𝑛, 𝑎𝑎𝑎𝑎𝑎𝑎 𝑖𝑖 ≠ 𝑗𝑗.

(5)

where 𝜌𝜌𝑡𝑡𝐷𝐷𝐷𝐷𝐷𝐷 is the dynamic correlation coefficient in the DCC-GARCH model. Following Engle (2002), we conduct the estimation using a two-step maximum likelihood estimation method. The likelihood function can be written as: 1

L(𝜃𝜃, 𝜙𝜙) = − ∑𝑇𝑇𝑡𝑡=1( 𝑛𝑛 𝑙𝑙𝑙𝑙𝑙𝑙(2𝜋𝜋) + 2𝑙𝑙𝑙𝑙𝑙𝑙|𝐷𝐷𝑡𝑡 | + 𝑙𝑙𝑙𝑙𝑙𝑙|𝑅𝑅𝑡𝑡 | + 𝑧𝑧𝑡𝑡́ 𝑅𝑅𝑡𝑡−1 𝑧𝑧𝑡𝑡 ). 2

(6)

3.2. The Structural Break Test We apply Inclán and Tiao's (1994) ICSS algorithm to capture the structural breaks in both Greater China stock returns and the foreign exchange rate data series. This test assumes that the data display a stationary variance over an initial period until a sudden change occurs due to a sequence of events. Then, the variance reverts to stationary until another change occurs. This process repeats over time, generating a time series of observations with an unknown number of changes in the variance. To calculate accurate estimates of the model parameters, sudden changes should be incorporated into the GARCH (1,1) model. From Eq. (3), we modified the GARCH model with the multiple breaks identified via ICSS algorithm as follows: 10

2 ℎ𝑖𝑖𝑖𝑖,𝑡𝑡 = 𝜔𝜔𝑖𝑖 + 𝑑𝑑1 𝐷𝐷1 + ⋯ + 𝑑𝑑𝑛𝑛 𝐷𝐷𝑛𝑛 + 𝛼𝛼𝑖𝑖 𝜀𝜀𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽𝑖𝑖 ℎ𝑖𝑖𝑖𝑖,𝑡𝑡−1 .

(7)

where 𝐷𝐷1 , ⋯ , 𝐷𝐷𝑛𝑛 represent dummy variables that are equal to one from each point of change in the variance onwards and zero otherwise.

3.3. The Hedge and Safe Haven Models Following Baur and McDermott (2010), we assume that the relationship between the returns of these two markets is not constant but is non-linear. We assume that the value of USD against Greater China currencies depends on changes in the Greater China stock markets. Hence, to examine the hedge and safe haven characteristics of USD against stock markets during periods of extreme negative stock returns, we adapt Ratner and Chiu’s (2013) dummy variable regression model as follows: 𝜌𝜌𝑡𝑡𝐷𝐷𝐷𝐷𝐷𝐷 = 𝛾𝛾0 + 𝛾𝛾1 𝐷𝐷�𝑟𝑟𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝑞𝑞10 �

+ 𝛾𝛾2 𝐷𝐷�𝑟𝑟𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝑞𝑞5 �

+ 𝛾𝛾3 𝐷𝐷�𝑟𝑟𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝑞𝑞1 �.

(8)

where 𝜌𝜌𝑡𝑡𝐷𝐷𝐷𝐷𝐷𝐷 are the time-varying correlations obtained from Eq. (5), which we regress on three dummy variables representing market turmoil. 𝐷𝐷 represents the dummy

variables that capture extreme stock market movements equal to one if the stock market exceeds the 10%, 5%, and 1% quantile of the negative return distribution. In addition, USD is a weak hedge if 𝛾𝛾0 is zero, or strong hedge if 𝛾𝛾0 is negative and significant for Greater China stock markets. USD is a weak safe haven if the parameters 𝛾𝛾1 , 𝛾𝛾2 , or 𝛾𝛾3

are insignificantly different from zero, or a strong safe haven if they are negative and significant. Similarly, Baur and McDermott (2010) indicate that the relationship between the returns of two financial markets changes with market uncertainty and choose 11

conditional volatility as the proxy for market uncertainty. Hence, to examine the hedge and safe haven characteristics of USD against stock markets during periods of extreme stock volatility, we use a modified version of Eq. (8): 𝜌𝜌𝑡𝑡𝐷𝐷𝐷𝐷𝐷𝐷 = 𝛾𝛾0 + 𝛾𝛾1 𝐷𝐷�𝐻𝐻𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝑞𝑞10 �

+ 𝛾𝛾2 𝐷𝐷�𝐻𝐻𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝑞𝑞5 �

+ 𝛾𝛾3 𝐷𝐷�𝐻𝐻𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝑞𝑞1 �.

(9)

where dummy variable 𝐷𝐷 equals one if the stock market exceeds the 10%, 5%, and 1%

quantile of the conditional volatility distribution and zero otherwise. 𝐻𝐻𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 is the conditional volatility extracted from the DCC-GARCH model, which represents stock

market uncertainty. Since volatility tends to cluster in periods and is auto-correlated, the dummy variables are equal to one for longer and continuous periods than the dummy variables in Eq. (8). Thus, compared to Eq. (8), we will obtain different estimation results from Eq. (9). Finally, we assume that the relationship between the returns of two markets will change in the face of a shock, such as the GFC because it had a great impact on the stock and foreign exchange markets. We use the following model to examine the hedge and safe haven characteristics of USD against stock markets during the GFC: 𝜌𝜌𝑡𝑡𝐷𝐷𝐷𝐷𝐷𝐷 = 𝛾𝛾0 + 𝛾𝛾1 𝐷𝐷(𝐺𝐺𝐺𝐺𝐺𝐺).

(10)

where dummy variable 𝐷𝐷 is equal to one during the GFC and zero otherwise. Moreover, USD is a weak hedge if 𝛾𝛾0 is zero, or a strong hedge if 𝛾𝛾0 is negative and significant

for Greater China stock markets. USD is a weak safe haven if the parameter 𝛾𝛾1 is insignificantly different from zero, or a strong safe haven if 𝛾𝛾1 is negative and

significant.

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4. DATA This study considers daily data for the stock and foreign exchange markets in Greater China. The stock markets of Greater China include the Shanghai Stock Exchange Composite Index (SSE) and the Shenzhen Stock Exchange Component Index (SZSE) of the Chinese mainland, the Hang Seng Index (HSI) of Hong Kong, and the Taiwan Weighted Index (TWII) of Taiwan. 3 We consider the daily exchange rate data for the local currency exchange rate against USD, namely the RMB/USD exchange rate, the Hong Kong dollar (HKD)/USD exchange rate, and the Taiwan dollar (TWD)/USD exchange rate. 4 The full sample covers the period from July 22, 2005 5 to November 30, 2015. We convert the sample prices into daily logarithmic percentage return series, that is, 𝑟𝑟𝑖𝑖,𝑡𝑡 = 100 × ln(𝑃𝑃𝑖𝑖,𝑡𝑡 /𝑃𝑃𝑖𝑖,𝑡𝑡−1 ), where 𝑟𝑟𝑖𝑖,𝑡𝑡 is the return for each sample at time 𝑡𝑡, 𝑃𝑃𝑖𝑖,𝑡𝑡 is the current price, and 𝑃𝑃𝑖𝑖,𝑡𝑡−1 is the price from the previous day.

Table 1 shows the descriptive statistics and the results of the unit root test for all

daily returns data. In Panel A of Table 1, the standard deviations of all stock market returns are much higher, indicating that the relative dispersion is much greater for the stock markers than for the foreign exchange markets. Since Hong Kong adopted the linked exchange rate system on October 17, 1983, t he standard deviation of the HKD/USD exchange rate market is smaller to those of the other two foreign exchange

3

All daily price indices of four stock markets were provided by Yahoo Finance (https://finance.yahoo.com). 4 All daily data for the three exchange rates were provided by the Federal Reserve Bank of St. Louis (https://research.stlouisfed.org). 5 Sui and Sun (2016) indicate that most emerging countries have not implemented a free-floating exchange rate regime, so that the better way to weaken regime impacts is by excluding periods of fixed exchange rate adoption. We thus selected July 22, 2005 as the starting date to reflect the date that China adopted the managed floating exchange rate. 13

markets, suggesting that HKD is a relatively stable asset against USD. The distributions of all return series are negatively skewed, except for the RMB/USD exchange rate, indicating that large negative returns are more frequent than large positive returns. Moreover, all return series show signs of significantly higher levels of excess kurtosis with respect to the normal distribution, indicating the presence of peaked distributions and fat tails. Thus, the Jarque-Bera test statistics (JB; 1980) significantly rejected the assumption of a normal distribution for all return series. The Ljung-Box (1978) 𝑄𝑄

statistics clearly indicate some presence of a serial correlation in the squared return series for all return series, confirming the presence of GARCH effects. In addition, Panel B of Table 1 provides the results of three unit root tests. Overall, the results suggest that all return series are stationary processes at the conventional levels.

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SSE Panel A: Descriptive Statistics Mean 0.0488 Maximum 9.0343 Minimum −12.7636 Std. Dev. 1.8081 Skewness −0.6237 Kurtosis 4.3642 J-B 2097.1*** 819.6*** 𝑄𝑄𝑠𝑠 (12) 1141.9*** 𝑄𝑄𝑠𝑠 (24) Panel B: Unit Root Tests ADF −48.78*** PP −48.84*** KPSS 0.31

SZSE

TABLE 1 Descriptive Statistics and Unit Root Tests. RMB/USD HSI TWII

HKD/USD

TWD/USD

0.0602 10.7526 −12.5313 2.0520 −0.4920 3.0058 1014.5*** 647.6*** 900.2***

0.0158 13.4068 −14.6954 1.6477 −0.0766 11.3284 13450.7*** 1536.9*** 1814.4***

0.0108 6.5246 −7.6489 1.2682 −0.4539 3.5558 1385.5*** 1431.0*** 2220.4***

−0.0098 1.8161 −0.9980 0.1193 1.5739 32.4004 107868.3*** 633.0*** 724.7***

−0.0001 0.2515 −0.2607 0.0321 −0.7344 11.9772 15258.7*** 971.9*** 1232.2***

0.0012 2.4848 −3.4230 0.3204 −0.5298 11.9997 14928.8*** 646.1*** 657.3***

−47.51*** −47.58*** 0.37*

−53.47*** −51.87*** 0.07

−47.04*** −47.03*** 0.05

−50.92*** −50.93*** 1.43***

−51.79*** −51.87*** 0.02

−52.39*** −52.42*** 0.15

Notes: (i) The Jarque-Bera (J-B) corresponds to the test statistic for the null hypothesis of normality in sample returns distribution. (ii) The Ljung-Box statistic, 𝑄𝑄𝑠𝑠 (𝑛𝑛), checks for serial correlation in the squared return series up to the 𝑛𝑛𝑡𝑡ℎ order. (iii) In the case of Panel B, Mackinnon’s 1% critical value is –3.435 for the augmented Dickey and Fuller (ADF; 1979) and Phillips and Perron (PP; 1988) tests. The critical values for the Kwiatkowski et al. (KPSS; 1992) test is 0.739 at the 1% significance level. (iv) *** and * indicate a rejection of the null hypothesis at the 1% and 10% significance levels, respectively.

15

5. EMPIRICAL RESULTS In this section, we present the estimation results obtained from the DCC-GARCH (1,1) model with and without structural breaks for the stock returns and exchange rates. Moreover, we examine the hedge and safe haven properties of USD against Greater China stock markets using the dummy variable regression model.

5.1. The DCC-GARCH (1,1) Model without and with Structural Breaks Figure 1 shows the daily returns behaviour for the stock and foreign exchange markets in Greater China. This indicates the points of structural breaks in the return series, especially during the regional and global economic crises. For this purpose, we apply the ICSS algorithm and identify multiple structural breaks for all markets. According to these figures, we find a number of volatility clusters, which indicates that the use of a GARCH-family model is appropriate and confirms the results in Table 1. Table 2 displays the results for the number of structural breaks and structural breakpoints using the ICSS algorithm. All return series present at least five structural breaks in their unconditional variances over the full sample period. These breakpoints are linked to major economic and political events at the local, regional, and global levels such as the GFC, 2011-2012 European debt crisis, and RMB exchange rate reform announced by the People's Bank of China on August 11, 2015. We also find that the common unexpected event for all markets is the GFC, and all markets have high volatility during this crisis. This finding is consistent with Hammoudeh and Li’s (2008) results, which suggests that global events can lead to more structural breaks compared 16

to local events. Overall, we conclude that major economic and political events can cause the observed regime changes in the unconditional variances. Tables 3 and 4 present the estimation results of the DCC-GARCH (1,1) model without and with structural breaks. We examine a DCC-GARCH (1,1) model that accounts for structural changes in variance to analyse the impact of structural changes on the persistence in variance and for an accurate measure of volatility. Lamoureux and Lastrapes (1990) indicate that when ignoring structural breaks, the GARCH model may produce spurious regressions due to over-estimated volatility persistence. Compared with Table 3, the model with structural breaks in Table 4 reduces the values of all parameters of 𝛽𝛽, which indicates a significant decrease in the degree of volatility persistence for all markets. 6 This finding is consistent with studies by Hammoudeh and

Li (2008), Wang and Moore (2009), Kang et al. (2009), and Mensi et al. (2015), among others. Additionally, if we consider these structural breaks, we obtain a smaller average correlation coefficient (𝜌𝜌𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 ), implying that including structural breaks leads to a higher hedging effect.

6 The sum of the parameters (𝛼𝛼 + 𝛽𝛽) is more than one for the HKD/USD exchange rate (1.0594) in Table 3. If 𝛼𝛼 + 𝛽𝛽 > 1, the GARCH model is mis-specified in measuring the persistence in variance because the GARCH model strictly impose the restriction of 𝛼𝛼 + 𝛽𝛽 < 1 (Bollerslev et al., 1994). In Table 4, when we consider structural breaks, the scalar parameters 𝛼𝛼 and 𝛽𝛽 satisfy 𝛼𝛼 + 𝛽𝛽 < 1, which proves that ignoring structural breaks in the GARCH models leads to an overestimate of volatility persistence. 17

FIGURE 1 Daily Returns Behaviour for the Stock and Foreign Exchange Markets SSE

SZSE

10

12 8

5 4 0

0 -4

-5

-8 -10 -12 -15

-16 05

06

07

08

09

10

11

12

13

14

15

05

06

07

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09

10

HSI

11

12

11

12

13

14

12

13

14

13

14

15

TWII

15

8 6

10

4 5

2

0

0 -2

-5

-4 -10

-6

-15

-8 05

06

07

08

09

15

14

13

12

11

10

05

10

09

08

07

06

15

HKD/USD

RMB/USD 2.0

.3

1.5

.2

1.0

.1 0.5

.0 0.0

-.1 -0.5

-.2

-1.0

-.3

-1.5 05

06

07

08

09

10

11

12

13

14

15

05

06

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11

15

TWD/USD 3 2 1 0 -1 -2 -3 -4 05

06

07

08

09

10

11

12

13

14

15

Note: Bands are at ±3 standard deviations where structural break points occur based on the ICSS algorithm. 18

Series SSE

SZSE

HSI

TWII

RMB/USD

HKD/USD

TWD/USD

TABLE 2 Results for Structural Breaks in Volatility Number of Breaks Time Period 1 2005.07.22 ~ 2006.12.22 2 2006.12.26 ~ 2008.11.19 3 2008.11.20 ~ 2009.11.30 4 2009.12.01 ~ 2012.02.08 5 2012.02.09 ~ 2014.11.26 6 2014.11.28 ~ 2015.11.30 1 2005.07.22 ~ 2006.12.19 2 2006.12.20 ~ 2008.11.19 3 2008.11.20 ~ 2011.02.14 4 2011.02.15 ~ 2014.11.21 5 2014.11.24 ~ 2015.11.30 1 2005.07.22 ~ 2007.07.26 2 2007.07.27 ~ 2008.09.12 3 2008.09.16 ~ 2009.06.02 4 2009.06.03 ~ 2011.12.09 5 2011.12.12 ~ 2015.11.30 1 2005.07.22 ~ 2007.07.25 2 2007.07.26 ~ 2009.06.24 3 2009.06.25 ~ 2011.08.01 4 2011.08.02 ~ 2012.06.11 5 2012.06.12 ~ 2015.06.03 6 2015.06.04 ~ 2015.11.30 1 2005.07.22 ~ 2007.08.15 2 2007.08.16 ~ 2008.12.01 3 2008.12.02 ~ 2010.06.18 4 2010.06.21 ~ 2015.08.10 5 2015.08.11 ~ 2015.11.30 1 2005.07.22 ~ 2007.05.11 2 2007.05.14 ~ 2008.10.09 3 2008.10.10 ~ 2010.05.05 4 2010.05.06 ~ 2011.12.30 5 2012.01.03 ~ 2015.11.30 1 2005.07.22 ~ 2008.10.20 2 2008.10.21 ~ 2009.04.30 3 2009.05.04 ~ 2011.12.01 4 2011.12.02 ~ 2014.12.22 5 2014.12.23 ~ 2015.11.30

Note: The structural break tests are conducted using the ICSS algorithm.

19

Std. Dev. 1.2605 2.7094 1.9862 1.3175 1.0426 2.5717 1.4343 2.9382 2.0177 1.3509 2.8154 0.9407 2.3538 3.7312 1.4785 1.0799 0.9698 2.0412 1.0505 1.5665 0.7403 1.2230 0.0743 0.1753 0.0425 0.1146 0.2918 0.0245 0.0494 0.0171 0.0516 0.0176 0.2734 0.6961 0.3166 0.2071 0.4378

Finally, in Panel C in Tables 3 and 4, we evaluate the accuracy of the model specifications using a common diagnostic test. The insignificance of the Ljung-Box 𝑄𝑄𝑠𝑠 (12) and 𝑄𝑄𝑠𝑠 (24) tests shows no serial correlations in the residual series at the conventional levels, except for few return series, indicating that the DCC-GARCH (1,1)

model with and without structural breaks is well specified. Moreover, the higher values of the log likelihood (log(L)) indicate that the model with structural breaks is superior to its counterpart without structural breaks for all return series. Thus, we conclude that this model specification is the best to capture the volatility between Greater China stock markets and foreign exchange markets. Table 5 presents the estimation and test results for the structural break dummy variables of the DCC-GARCH (1,1) model with structural breaks. We find that almost all dummy variables are statistically significant, highlighting the importance of including this unscheduled news related to structural breaks in modelling time-varying correlations. The Wald test results confirm these findings. Panel B in Table 5, shows that the Wald test strongly rejects the null hypothesis that all coefficients are zero.

20

TABLE 3 Estimation Results of the DCC-GARCH (1,1) Model without Structural Breaks SSE & RMB/USD SSE RMB/USD Panel A: Estimates of the GARCH (1,1) Model 0.0505* −0.0095*** 𝜇𝜇 (0.0289) (0.0021) 0.0156*** 0.0042*** ω (0.0057) (0.0004) 0.0522*** 0.3428*** α (0.0070) (0.0432) 0.9445*** 0.4230*** 𝛽𝛽 (0.0072) (0.0458) 0.9967 0.7658 α + 𝛽𝛽 Panel B: Estimates of the DCC Model 𝜌𝜌𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 −0.0528 (0.0364) 𝐷𝐷𝐷𝐷𝐷𝐷 0.0150 (0.0126) 𝛼𝛼

0.9245*** (0.0745) 𝛽𝛽 𝐷𝐷𝐷𝐷𝐷𝐷 Panel C: Diagnostic Tests 5.991 [0.917] 0.385 [1.000] 𝑄𝑄𝑠𝑠 (12) 13.90 [0.949] 0.589 [1.000] 𝑄𝑄𝑠𝑠 (24) log(L) −4579.6 1948.1

SZSE & RMB/USD SZSE RMB/USD 0.0502** (0.0339) 0.0308*** (0.0107) 0.0540*** (0.0075) 0.9399*** (0.0083) 0.9939

−0.0094*** (0.0021) 0.0041*** (0.0005) 0.3277*** (0.0418) 0.4356*** (0.0507) 0.7633

HSI & HKD/USD HSI HKD/USD 0.0533** (0.0224) 0.0249*** (0.0067) 0.0805*** (0.0100) 0.9092*** (0.0113) 0.9897

−0.0005** (0.0002) 0.0000*** (0.0000) 0.2271*** (0.0205) 0.8323*** (0.0112) 1.0594

TWII & TWD/USD TWII TWD/USD 0.0423** (0.0192) 0.0152*** (0.0044) 0.0687*** (0.0089) 0.9222*** (0.0103) 0.9909

0.0000 (0.0056) 0.0016*** (0.0004) 0.0852*** (0.0113) 0.9044*** (0.0117) 0.9896

−0.0432 (0.0415) 0.0163 (0.0126)

−0.2093* (0.1097) 0.0081*** (0.0025)

−0.3328*** (0.0899) 0.0249** (0.0103)

0.9286*** (0.0611)

0.9889*** (0.0036)

0.9504*** (0.0254)

4.477 [0.973] 10.17 [0.994] −4947.6

28.02[0.006] 34.06 [0.084] −4243.8

7.268 [0.839] 16.93 [0.852] −3747.4

0.404 [1.000] 0.603 [1.000] 1948.8

7.042 [0.855] 14.39 [0.937] 5915.2

18.89 [0.091] 27.20 [0.295] −391.5

Notes: (i) Standard errors are in parentheses and the 𝑝𝑝-values are in brackets. (ii) The Ljung-Box statistic, 𝑄𝑄𝑠𝑠 (𝑛𝑛), checks for serial correlation in the squared return series up to the 𝑛𝑛𝑡𝑡ℎ order. (iii) ***, **, and * indicate a rejection of the null hypothesis at the 1%, 5%, and 10% significance levels, respectively.

21

TABLE 4 Estimation Results of the DCC-GARCH (1,1) Model with Structural Breaks SSE & RMB/USD

SZSE & RMB/USD

HSI & HKD/USD

TWII & TWD/USD

SSE

SZSE

RMB/USD

HSI

HKD/USD

TWII

TWD/USD

0.0597* (0.0328) 2.2268*** (0.0368) 0.0464*** (0.0076) 0.8983*** (0.0072) 0.9447

−0.0028*** (0.0009) 0.0113*** (0.0005) 0.4809*** (0.0538) 0.3908*** (0.0407) 0.8717

0.0550** (0.0229) 1.4288*** (0.0830) 0.0760*** (0.0097) 0.8775*** (0.0092) 0.9535

−0.0002 (0.0003) 0.0006*** (0.0000) 0.2386*** (0.0258) 0.7464*** (0.0241) 0.9850

0.0463** (0.0207) 0.8759*** (0.0586) 0.0593*** (0.0108) 0.8531*** (0.0287) 0.9124

0.0024 (0.0055) 0.0623*** (0.0040) 0.1063*** (0.0193) 0.7900*** (0.0412) 0.8963

RMB/USD

Panel A: Estimates of the GARCH (1,1) Model 0.0578** −0.0028** 𝜇𝜇 (0.0277) (0.0011) 1.7462*** 0.0115*** ω (0.0138) (0.0005) 0.0435*** 0.4816*** α (0.0008) (0.0511) 0.8929*** 0.3870*** 𝛽𝛽 (0.0056) (0.0391) 0.9364 0.8686 α + 𝛽𝛽

Panel B: Estimates of the DCC Model 𝜌𝜌𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 −0.0584* (0.0350) 𝛼𝛼

𝛽𝛽

𝐷𝐷𝐷𝐷𝐷𝐷

𝐷𝐷𝐷𝐷𝐷𝐷

−0.0532 (0.0402)

−0.2107* (0.1135)

−0.3423*** (0.0814)

0.0082 (0.0075)

0.0104 (0.0073)

0.0086*** (0.0026)

0.0217** (0.0088)

0.9672*** (0.0368)

0.9596*** (0.0296)

0.9884*** (0.0039)

0.9521*** (0.0251)

10.890 [0.538] 21.491 [0.610] −4919.1

23.078 [0.027] 29.656 [0.196] −4226.7

8.745 [0.725] 20.468 [0.670] −3719.1

Panel C: Diagnostic Tests 11.235 𝑄𝑄𝑠𝑠 (12) [0.509] 29.175 𝑄𝑄𝑠𝑠 (24) [0.213] log(L) −4545.9

15.916 [0.195] 31.103 [0.151] 2442.8

20.063 [0.066] 33.577 [0.092] 2440.8

Note: See the notes of Table 3.

22

4.796 [0.964] 10.313 [0.993] 5980.5

8.981 [0.705] 15.308 [0.911] −338.2

TABLE 5 Estimation and Test Results for the Dummy Variables of the DCC-GARCH (1,1) Model with Structural Breaks Time Period in Table 2 SSE & RMB/USD SZSE & RMB/USD HSI & HKD/USD TWII & TWD/USD SSE RMB/USD SZSE RMB/USD HSI HKD/USD TWII TWD/USD Panel A: Estimation Results of Dummy Variables 1 −1.6479*** −0.0104*** −2.1145*** −0.0103*** −1.3820*** −0.0005*** −0.7917*** −0.0600*** (0.0203) (0.0004) (0.0394) (0.0005) (0.0839) (0.0000) (0.0550) (0.0033) 2 −1.2463*** −0.0018 −1.7127*** −0.0017 −1.1347*** −0.0003*** −0.5203*** −0.0006 (0.0714) (0.0013) (0.0461) (0.0014) (0.1012) (0.0001) (0.1010) (0.0137) 3 −1.5216*** −0.0112*** −2.0064*** −0.0111*** −0.9731*** −0.0006*** −0.7778*** −0.0511*** (0.0475) (0.0005) (0.0413) (0.0005) (0.1551) (0.0000) (0.0561) (0.0034) 4 −1.6316*** −0.0081*** −2.1216*** −0.0081*** −1.3412*** −0.0003*** −0.6751*** −0.0573*** (0.0184) (0.0004) (0.0376) (0.0005) (0.0841) (0.0001) (0.0703) (0.0034) 5 −1.6724*** 0.0558*** −1.8498*** 0.0558*** −1.3712*** −0.0006*** −0.8258*** −0.0362*** (0.0160) (0.0103) (0.0748) (0.0121) (0.0830) (0.0000) (0.0553) (0.0065) 6 −1.3614*** − − − − − −0.7463*** − (0.0876) (0.0626) Panel B: Test for Significance of Dummy Variables in Model with Structural 16962.5*** 914.5*** 4565.7*** 906.0*** 320.6*** 405.2*** 230.4*** 317.1*** 𝑥𝑥 2 Statistic of Wald Test

Notes: (i) Standard errors are in parentheses. (ii) The null hypothesis of the Wald test is that all dummy variables in each model are zero. (iii) *** indicates a rejection of the null hypothesis at the 1% significance level.

23

Figure 2 displays a visual representation of the shifts in the DCC between the stock and foreign exchange markets using the DCC-GARCH (1,1) model with and without structural breaks over the daily sample period. Figure 2 shows significant variability in the conditional correlations across all pairs. This indicates that investors should frequently adjust their active portfolio strategies based on variable conditional correlations. Moreover, the results of the dynamic conditional correlations show that the relationship between the stock and foreign exchange markets increased slightly in the Shanghai and Shenzhen markets, and increased greatly in the Hong Kong market during the GFC. This finding indicates that investors had limited opportunities to diversify their assets during this period, especially in the Hong Kong market, consistent with studies by Lin (2012) and Caporale et al. (2014). Additionally, we find small differences in the dynamic conditional correlations for the model with and without structural breaks. Hence, considering structural breaks improves the accuracy of hedging strategy computations. The sign of most of correlation coefficients is negative, indicating a negative relationship between these two markets and supporting the ‘stock-oriented’ models of exchange rates. This means that USD would serve as an effective hedge asset against risk in all stock markets. Taking a close look at these figures, we find that the means of the absolute values of the correlation coefficients between the two Chinese mainland stock markets and the foreign exchange market is relatively smaller than those in the other two cases. Thus, once stock prices in Greater China go down, the USD presents a lower recovery rate for the Chinese mainland stock markets and a higher recovery 24

rate for the Hong Kong and Taiwan stock markets. This distinct finding is due to the different degrees of market opening to global markets. Now, Chinese mainland and Hong Kong investors will be able to trade eligible shares listed on the other region’s market through the Shanghai-Hong Kong and Shenzhen-Hong Kong Stock Connect programs, so investors in Chinese mainland could hold more Hong Kong stock assets to get a higher recovery rate on USD assets.

FIGURE 2 Time-varying DCC with and without Structural Breaks .12

.20

DCC of SSE-USD without SB DCC of SSE-USD with SB Diff of DCC of SSE-USD

.08 .04

.10 (b) SZSE - USD

(a) SSE - USD

DCC of SZSE-USD without SB DCC of SZSE-USD with SB Diff of DCC of SZSE-USD

.15

.00 -.04 -.08 -.12

.05 .00 -.05 -.10

-.16

-.15

-.20 05

06

07

08

09

10

11

12

13

14

05

15

.1

06

07

08

09

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11

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15

11

12

13

14

15

.1 .0

.0

-.1 (d) TWII - USD

(c) HSI - USD

-.1

-.2

-.3

-.2 -.3 -.4

DCC of HSI-USD without SB DCC of HSI-USD with SB Diff of DCC of HSI-USD

-.4

DCC of TWII-USD without SB DCC of TWII-USD with SB Diff of DCC of TWII-USD

-.5

-.5

-.6 05

06

07

08

09

10

11

12

13

14

15

05

06

07

08

09

10

5.2. Hedge and Safe Haven Analyses Following Ratner and Chiu (2013), we use three models to test USD as a hedge and safe haven against stock market risk. The first and the second models examine the hedge and safe haven characteristics of USD during periods of extreme negative stock 25

returns and extreme stock volatility. The third model focuses on the hedge and safe haven properties of USD during the GFC. If the relationship between the returns of two assets is not constant but non-linear, then investors act differently in specific and extreme market conditions compared to normal market conditions. Table 6 provides the estimation results for the role of USD as a hedge and safe haven asset during extreme stock returns. Panels A and B show the results of the DCCGARCH (1,1) models without and with structural breaks, respectively. The Wald tests are reported in the last column. The null hypothesis of the Wald test is that the three dummy variables (𝐷𝐷�𝑟𝑟𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝑞𝑞10 �,

𝐷𝐷�𝑟𝑟𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝑞𝑞5 �,

𝐷𝐷(𝑟𝑟𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝑞𝑞1 ))

in each model are zero.

Comparing the values of the Wald test for the model without and with structural breaks, the stock quantile regression coefficients (𝛾𝛾1 , 𝛾𝛾2 , 𝛾𝛾3 ) from the DCC-GARCH (1,1)

model with structural breaks are better than those from the model without structural

breaks, indicating that incorporating structural breaks can provide more accurate hedge and safe haven properties of USD. Hence, we will interpret mainly the estimation results obtained from the model with structural breaks. The DCC coefficients (𝜌𝜌𝑡𝑡𝐷𝐷𝐷𝐷𝐷𝐷 ) are regressed on a constant (𝛾𝛾0 ) and three dummy

variables representing extreme movements in the underlying stock markets at the 10%,

5%, and 1% quantiles of the most negative stock returns. Negative coefficients in the hedge column show a negative relationship between the USD and all stock markets at the 1% level of significance, suggesting that USD is a strong hedge asset against stock markets. Despite strong hedging properties for all pairs of markets, the benefits of USD vary among markets. As seen in Panel B of Table 6, the Chinese mainland stock markets 26

have a lower negative hedge value (𝛾𝛾0 =−0.0593 and −0.0530) compared to the Hong Kong and Taiwan stock markets, which have even lower values (𝛾𝛾0 =−0.2164 and −0.3416), which Figure 2 confirms.

TABLE 6 USD as a Hedge and Safe Haven against Greater China Stock Markets during Extreme Stock Returns Stock Markets

Hedge (𝛾𝛾0 )

Safe Haven 10% (𝛾𝛾1 )

Panel A: without Structural Breaks

5% (𝛾𝛾2 )

1% (𝛾𝛾3 )

Wald Test

SSE

−0.0534*** 0.0025

0.0061

−0.0032

2.0355

SZSE

−0.0429*** −0.0089**

0.0143**

−0.0084

2.4169*

HSI

−0.2145*** 0.0283***

0.0339**

0.0665***

24.2040***

TWII

−0.3322*** −0.0003

−0.0139

0.0062

0.8406

Panel B: with Structural Breaks SSE

−0.0593*** 0.0036

0.0086*

0.0007

5.0483***

SZSE

−0.0530*** −0.0079**

0.0146***

−0.0073

2.4725*

HSI

−0.2164*** 0.0306***

0.0365**

0.0765***

27.3075***

TWII

−0.3416*** 0.0023

−0.0164

−0.0005

1.4232

Notes: (i) Darker shades indicate strong hedge/safe haven. Lighter shades indicate weak hedge/safe haven. Unshaded areas indicate no hedge/safe haven cases. (ii) ***, **, and * indicate a rejection of the null hypothesis at the 1%, 5%, and 10% significance levels, respectively. Model: 𝜌𝜌𝑡𝑡𝐷𝐷𝐷𝐷𝐷𝐷 = 𝛾𝛾0 + 𝛾𝛾1 𝐷𝐷�𝑟𝑟𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑞𝑞10 � + 𝛾𝛾2 𝐷𝐷�𝑟𝑟𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑞𝑞5 � + 𝛾𝛾3 𝐷𝐷(𝑟𝑟𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑞𝑞1 )

The stock quantile regression coefficients represent the safe haven properties of USD against stock markets during extreme stock returns. In the 10% stock return quantile, the regression coefficient of the Shenzhen stock market (𝛾𝛾1 =−0.0079) is negative and significant at the 5% level. A negative and significant coefficient indicates that USD is a strong safe haven asset against stock markets and will provide an extra benefit to stock investors during periods of extreme negative Shenzhen stock returns. 27

The regression coefficients of the Shanghai and Taiwan stock markets are insignificant, meaning that USD is a weak safe haven asset in these two stock markets. Conversely, the regression coefficient of the Hong Kong stock market is positive and significant at the 1% level, indicating that USD is not a safe haven asset in this stock market. In the 5% stock return quantile, the regression coefficient of the Taiwan stock market is insignificant, indicating that the USD is a weak safe haven asset in the Taiwan stock market. Conversely, the regression coefficients of the Shanghai, Shenzhen, and Hong Kong stock markets are positive and significant, and thus USD is not a safe haven asset in these stock markets. In the 1% stock return quantile, USD is a weak safe haven asset in the Shanghai, Shenzhen, and Taiwan stock markets, while it is not a safe haven asset in the Hong Kong stock market. These diverse findings may be due to the different features and investors of each stock market, as well as the different foreign exchange policies. Table 7 provides the estimation results for the role of USD as a hedge and safe haven asset during extreme stock volatility. Comparing the values of Wald test for the model without and with structural breaks, we observe that the results from the model with structural breaks are better than those from the model without structural breaks are. As before, we limit our discussion to the estimation results obtained from the model with structural breaks.

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TABLE 7 USD as a Hedge and Safe Haven against Greater China Stock Markets during Extreme Stock Volatility Stock Markets

Hedge (𝛾𝛾0 )

Safe Haven 10% (𝛾𝛾1 )

Panel A: without Structural Breaks

5% (𝛾𝛾2 )

1% (𝛾𝛾3 )

Wald Test

SSE

−0.0534*** 0.0130***

−0.0143*** −0.0103

SZSE

−0.0437*** 0.0106***

−0.0045

−0.0337*** 6.9531***

HSI

−0.2228*** 0.0822***

0.0977***

0.0422*

TWII

−0.3308*** −0.0032

−0.0176

−0.0838*** 12.8297***

5.9934*** 161.9978***

Panel B: with Structural Breaks SSE

−0.0600*** 0.0202***

−0.0092*

−0.0038

15.8930***

SZSE

−0.0544*** 0.0177***

−0.0066

−0.0226**

10.3496***

HSI

−0.2290*** 0.1495***

TWII

−0.3423*** 0.0331***

Notes: See the notes of Table 6. Model: 𝜌𝜌𝑡𝑡𝐷𝐷𝐷𝐷𝐷𝐷 = 𝛾𝛾0 + 𝛾𝛾1 𝐷𝐷�𝐻𝐻𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝑞𝑞10 �

0.0578*** 0.0414*

268.6479***

−0.0523*** −0.0707*** 19.1820***

+ 𝛾𝛾2 𝐷𝐷�𝐻𝐻𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝑞𝑞5 �

+ 𝛾𝛾3 𝐷𝐷(𝐻𝐻𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝑞𝑞1 ).

The results in the hedge column are very similar to those in Table 6, so we will not describe them here. The stock quantile regression coefficients represent the safe haven properties of USD against stock markets during extreme stock volatility. In the 10% stock volatility quantile, the positive and significant regression coefficients indicate that USD is not a safe haven asset in all stock markets. In the 5% stock volatility quantile, the USD is a strong safe haven asset in the Shanghai and Taiwan stock markets (𝛾𝛾2 =−0.0092 and −0.0523); in the 1% stock volatility quantile, USD is a strong safe haven asset in the Shenzhen and Taiwan stock markets (𝛾𝛾3 =−0.0226 and −0.0707). Based on these findings, we find that USD exhibits different safe haven properties with different levels of stock market uncertainty. For the Hong Kong stock market, USD is 29

not a safe haven asset during extreme negative stock returns and extreme stock volatility in the 10%, 5%, and 1% quantiles in Tables 6 and 7. This finding is not surprising because degree of internationalisation of the Hong Kong stock market is quite high, so major global events influence it easily. During global events, it is possible that the Hong Kong stock price and USD often simultaneously dropped, and thus USD is not a safe haven asset in the Hong Kong stock market. Table 8 provides the estimation results for the role of USD as a hedge and safe haven asset during the GFC. As before, we interpret only the estimation results from the model with structural breaks. During the GFC, a negative and significant coefficient indicates that USD is a strong safe haven asset in the Taiwan stock market (𝛾𝛾1 =−0.0985). Conversely, positive and significant coefficients indicate that USD is not a safe haven

asset in the Shenzhen and Hong Kong stock markets. It is worth noting that the GFC period starting in September 15, 2008 that continued for 20 trading days 7 was associated with high degree of financial stress. Meanwhile, the USD also faced serious depreciation pressure. As Figure 2 shows, the dynamic relationship between the stock and foreign exchange markets becomes stronger in the Shanghai, Shenzhen, and Hong Kong markets during the GFC. Hence, though Shenzhen and Hong Kong stock prices went down during the GFC, USD did not exhibit safe haven properties against risk. This finding differs from those of Arouri et al. (2015), who suggest that gold was a safe haven for Chinese mainland stock markets during the GFC. Since gold is a traditional

7

Baur and McDermott (2010) define the starting date of the U.S. financial crisis with the collapse of Lehman Brothers on September 15, 2008, and assume that most of the crisis and its effects occurred in the first 20 trading days (approximately one month) after the start date. 30

safe haven asset for investors, the safe haven properties of USD were weaker than those of gold during the GFC were.

TABLE 8 USD as a Hedge and Safe Haven against Greater China Stock Markets during the GFC Stock Markets

Hedge (𝛾𝛾0 )

Safe Haven (𝛾𝛾1 )

Panel A: without Structural Breaks SSE

−0.0528***

−0.0084

SZSE

−0.0432***

0.0036

HSI

−0.2109***

0.2095***

TWII

−0.3321***

−0.0916***

Panel B: with Structural Breaks SSE

−0.0586***

0.0119

SZSE

−0.0533***

0.0166*

HSI

−0.2125***

0.2363***

TWII

−0.3415***

−0.0985***

Notes: See the notes of Table 6. Model: 𝜌𝜌𝑡𝑡𝐷𝐷𝐷𝐷𝐷𝐷 = 𝛾𝛾0 + 𝛾𝛾1 𝐷𝐷(𝐺𝐺𝐺𝐺𝐺𝐺).

6. CONCLUSIONS AND POLICY IMPLICATIONS This study investigates the time-varying correlations and the conditional volatilities persistence between stock markets and foreign exchange markets in Greater China. For this purpose, we apply the DCC-GARCH model with and without structural change dummies to the four Greater China stock indices and the three foreign exchange rate prices for the period spanning July 22, 2005 to November 30, 2015. Moreover, to determine the implications of the results for investors and policy makers, we use the

31

results from the DCC-GARCH model to discuss the hedge and safe haven properties of USD against Greater China stock markets. Our analysis provides strong evidence of negative time-varying correlations between stock markets and foreign exchange markets in Greater China, which is consistent with the model prediction and supports the ‘stock-oriented’ models of exchange rates. More importantly, due to the different levels of market opening to global markets, USD presents a lower recovery rate for both of mainland China’s stock markets and a higher recovery rate for the Hong Kong and Taiwan stock markets. Moreover, using Inclán and Tiao's (1994) ICSS algorithm to avoid the possibility of volatility overestimation, we find that considering structural breaks reduces the degree of volatility persistence for all considered markets. Additionally, if we consider these structural breaks to evaluate the hedge and safe haven properties, we will obtain better results. Thus, incorporating structural breaks in our models can provide more accurate hedging strategies. The findings have several important implications for portfolio risk managers, international investors, and policy makers. First, as capital globalisation intensifies, seeking benefits causes considerable quantities of foreign capital to enter or leave the Greater China stock markets based on each market’s recent situation. We find significant variability in the conditional correlations across all pairs, which indicates that investors should frequently adjust their active portfolio strategies. Since our results support the ‘stock-oriented’ models of exchange rates in which stock prices lead exchange rates with a negative correlation, corresponding to that during financial 32

turbulence in the stock market, international investors will withdraw their capital. To prevent downward pressure on the currency, governments should stimulate economic growth and stock markets to attract capital inflow. Second, diversified portfolios comprised of Greater China stock assets and USD can reduce investors’ risk efficiently. The results of the analysis of USD as a safe haven against stock markets are mixed, the USD could reduce investors’ overall losses in the face of extreme market conditions in Shanghai, Shenzhen, and Taiwan. However, USD provided limited opportunities for investors to diversify their assets during the GFC. Overall, our results show that the hedging effect of USD is strong under regular market conditions, whereas the safe haven effect of USD is weakened during the GFC. Hence, investors should have more USD in regular stock market conditions and have more the other safe haven assets (e.g., gold and credit default swaps (CDS)) during the GFC, which can assist investors in reducing risk and in gaining higher returns. Investors should possess the necessary information on the relationship between portfolio’s constituent assets when they build this dynamic hedging strategy, highlighting the importance of exploring dynamic correlations and hedging role for stock prices and exchange rates. Moreover, once stock prices in Greater China go down, the Chinese mainland stock markets gained a relatively low recovery rate from USD compared with the other two markets. Investors in Chinese mainland could hold more Hong Kong stock assets through the Shanghai-Hong Kong and Shenzhen-Hong Kong Stock Connect programs. Thus, it is necessary to increase the degrees of market opening of

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both of mainland China’s stock markets to global financial markets, which will improve the effectiveness of hedging between Chinese mainland stock assets and USD.

ACKNOWLEDGMENT The first author (X. Dong) is grateful for financial support from the China Scholarship Council (CSC No. 201608260112). The second author (S.-M. Yoon) is grateful for financial support from the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2016S1A3A2924349).

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