چگونه شوک های سیاسی و اطلاعاتی بر روند حرکات اوراق قرضه T و سهام بازارهای چین تأثیر می گذارد؟

We investigate the impacts of policy and information shocks on the correlation of China’s T-bond and stock returns, using originally the asymmetric dynamic conditional correlation (DCC) model that allows for the coexistence of opposite-signed asymmetries. The co-movements of China’s capital markets react to large macroeconomic policy shocks as evidenced by structural breaks in the correlation following the drastic 2004 macroeconomic austerity. We show that the T-bond market and the bond–stock correlations bear more of the brunt of the macroeconomic contractions. We also find that the bond–stock correlations respond more strongly to joint negative than joint positive shocks, implying that investors tend to move both the T-bond and stock prices in the same direction when the two asset classes have been hit concurrently by bad news, but tend to shift funds from one asset class to the other when hit concurrently by good news. However, the stock–stock correlation is found to increase for joint positive shocks, indicating that investors tend to herd more for joint bullish than joint bearish stock markets in Shanghai and Shenzhen.

During 2003 and early 2004, China experienced an excessive investment boom. To cool this economic overheating, in April–May 2004 the government put into practice a series of tight policy measures. Included in these policy measures were the following. The central bank raised the reserve requirements and tightened credit lines. The China Banking Regulatory Commission required commercial banks to nix investment projects deemed to be ill-planned, low quality, and unconformable to the government’s industrial policies. The State Development and Reform Commission ordered local authorities to control the debut of price-hiking projects within their jurisdictions. According to the news media, following the austerity programs, the Chinese stock and bond markets simultaneously underwent drastic drops, which subsequently had contagious effects on financial markets in Hong Kong, the US, Japan, London, Australia and so on (for example, Japan’s stock price indexes reportedly fell by 400–500 points). A metaphor went: “As the Chinese economy is having an injection for allaying fever, the world’s financial markets suffer a shivering fit” (http://news.xinhuanet.com/fortune/2004-05/14/content_1468420.htm). These observations and anecdotes seem to suggest that, in China, drastic policy changes have begun to impact domestic financial markets (as well as international financial markets), which then motivated the present paper to attempt a serious investigation on some related issues into which anecdotes do not and cannot provide insights. However, we are not interested in how individual market returns, but rather how correlations between them, respond to policy shocks. We have chosen to focus on the correlation between T-bond and stock returns for three main reasons as follows. First, to reduce portfolio risk via diversification, a key input required by risk managers to hold efficient portfolios is the correlation between assets included in the portfolio.1 Portfolios that contain stocks and government bonds have become popular among investors, as the two asset classes are believed to have different risk-return characteristics and their correlations to be low or even negative. Because the correlation between T-bonds and stocks plays a vital role in portfolio risk management and dynamic asset allocation for investors, it has been extensively studied in the literature. For example, an earlier study by Barsky (1989) looks at price co-movements between stocks and bonds, and finds that “when investors are scared, they look for safety. They adjust their portfolios to include more safe assets and fewer risky assets. This kind of movement is usually referred to as a ‘flight to quality”’. A recent study by Ilmanen (2003) on the US stock–bond correlation reports that the correlation between stock market and government bond returns was positive through most of the 1900s, but negative in the early 1930s, the late 1950s, and recently. A negative correlation implies that investors have benefited from the bond market upswing, offsetting some of their losses in stock markets. However, this combination may have severe implications for pension funding ratios, as both equities and discount rates decline, sending assets and liabilities in opposite directions. Second, correlations between the stock and bond markets are important to policymakers. Since China entered the WTO in 2000, the Chinese government has endeavored to reform its financial system including capital markets, in order to transform the conduct of macroeconomic policies from being administrative to being market oriented in nature (as required by becoming a WTO member). In the latter context, the central bank cannot set specific price targets for stocks and bonds, and so has to utilize the information contained in the co-movements between the freely adjusted prices of these assets to gauge, for example, market participants’ expectations about growth and inflation. In other words, the stock–bond return correlation estimates may provide policymakers with useful complementary information to determine whether market participants are changing their views on inflation or economic activity prospects. Quantifying contemporaneous relations between the stock and bond markets also helps policymakers to estimate and control the unintended consequences that policies directed primarily at one market could have for the other. To our best knowledge, the existing literature lacks such a study as ours for China, despite the important policy implications of the issues examined in the present paper. Third, correlations between asset returns have been viewed as an integral aspect of inter-financial market integration, in the literature. Kim et al., 2006 and Berben and Jansen, 2005 examine the dynamic or time-varying correlation between stock and T-bond returns of several European countries to infer the state and progress of their financial integration, taking into account the influence of the European Monetary Union as a possible cause of structural change. Kim et al. (2005) also conducted a similar study for stock market integration in Europe. In these studies, the authors use return correlations to gauge the degree of integration between financial markets: a high/low correlation implies a high/low level of integration. High, not just low, stock–bond correlations have also been established. For example, Kim et al. (2006) document that the inter-bond–stock correlations for each of their sample Euro zone countries and the weighted average of these for Euro countries and also non-Euro zone countries once reached very high levels, although they have been falling since the mid 1990s. The authors take these results to imply a falling financial integration since the mid 1990s. Apart from correlation analysis, the cointegration framework is also a useful tool in studying the degree of market integration, and a recent application of cointegration analysis to the long-run equilibrium relationship among China’s money, stock and T-bond markets has appeared in Yin (2005). However, a long-run relationship detected by cointegration tests is credible only if the “true” relationship is constant or experiences few breaks over time. The fact that China has been reforming its financial systems with frequent debuts of new reform programs allows one to argue that the number of “breaks” is too large to permit a meaningful cointegration analysis. Such an unstable, time-varying relationship among financial markets ought to be modeled more appropriately within a time-varying correlation framework. By doing so, our investigation into the correlation between China’s T-bond and stock returns will shed new light on inter-stock–bond market integration that may vary across different points in time. However, we use the previously reported estimates of the European stock–bond return correlations as a reference point with which to compare the estimates of the Chinese stock–bond return correlations, in order to infer the relative degree of stock–bond market integration in China. Our work contributes to the existing literature in at least two aspects. One is the link of stock–bond correlations to information shocks or macroeconomic factors. Recent studies on these issues include Chordia et al., 2005 and Li, 2002. In the former article that uses the US data, the authors find that innovations to stock and bond market liquidity and volatility are significantly correlated, and attribute this observation to the possibility that common factors such as monetary shocks and money flows drive liquidity and volatility in these markets. The latter paper shows that the major trends in stock–bond correlation in G7 countries can be explained by their common exposure to macroeconomic factors, such as expected inflation, unexpected inflation and the real interest rate. Our study focuses on China as the largest emerging economy, and links stock–bond and stock–stock correlations to macroeconomic austerity measures of an administrative nature and to the peculiar behavior of Chinese investors in response to information shocks. Exploring these unique characteristics of the Chinese financial markets represents our attempt to fill the void in the literature. The other aspect is related to the technical front for empirically investigating time-varying return correlations. Scruggs and Glabadanidis (2003) introduces flexibility into their specification for the time-varying covariance matrix of stock and bond returns, by assuming that conditional second moments follow an asymmetric dynamic covariance (ADC) process proposed by Kroner and Ng (1998). The use of the ADC model enables them to examine how return shocks and volatility are transmitted between the stock and bond markets. Cappiello et al. (2003) employ an asymmetric version of the dynamic conditional correlation (DCC) model which allows for a structural break to investigate asymmetric dynamics in the correlations of international equity and bond returns. They are able to find strong evidence of asymmetries in conditional covariance of equity and bond returns, and significant evidence of a structural break in conditional asset correlation upon the creation of the Euro. Connolly et al., 2005 and Connolly et al., 2007 estimate rolling correlations over time to examine whether the time-variation of stock–bond and stock–stock return comovements can be linked to stock implied volatility. It appears from the above-cited studies that those different approaches taken serve, and depend on, different objectives. Our research objectives (to be detailed below) determine that the asymmetric version of the DCC model with structural change as employed by Cappiello et al. (2003) seems to be a more appropriate econometric tool than those employed by Scruggs and Glabadanidis, 2003, Connolly et al., 2005 and Connolly et al., 2007. However, to adapt to the reality of the Chinese financial markets, we modify the model employed by Cappiello et al. (2003) in the following innovative manners. First, we propose a new version of the model which is termed as the “mixed asymmetric DCC” (MADCC) model, and apply it for the first time in the literature. Our MADCC model is capable of capturing concurrent responses, if any, of the conditional covariance of standardized return residuals to positive and negative information shocks. Second, we allow for multiple structural breaks in the conditional correlation. This enables us to test the strength and duration of policy impacts on the correlation. Third, we fit a Skew-t-GARCH model ( Jondeau and Rockinger, 2005) in filtering return volatility, so that the notorious problems of non-normality and skewness in the return distribution can be properly addressed. The objectives of this paper are twofold. One is to explore the general question of how the correlation responds to policy shocks. Specifically, we ask: (1) In what direction did the 2004 macroeconomic austerities affect the stock–stock and bond–stock correlations? (2) How long did the impacts last for? (3) Were there any differences in the correlation patterns between the pre- and post-episode periods. (4) What are the possible implications of such differences for financial market integrations, compared to the stock–bond market integration in Europe as investigated in previous studies (e.g., Kim et al., 2006)? Answers to these questions should carry useful complementary information for policymaking. The second objective of this paper is to use the “news impact surfaces” of Kroner and Ng (1998) to study asymmetry in the impact of joint information shocks on correlation. Previous studies have only examined possible asymmetry in the reaction of univariate return volatility to information shocks for China, and evidence is mixed in terms of the sign of asymmetry (see, for example, Li, 2003a and Li, 2003b). We extend the literature and see whether correlation between two asset returns responds asymmetrically to joint bad news (represented by their last period’s standardized return residuals being both negative) and joint good news (represented by their last period’s standardized return residuals being both positive), and whether in the same way as developed economies’ financial market correlations. Joint bad/good news means that two assets’ prices fall/rise together at a particular point in time. If, say, joint bad news (two prices falling together) increases the correlation between two assets, the probability that their prices will further fall together is high relative to the probability that they will further rise together following their previous rises together. This correlation asymmetry has important financial implications. For example, the standard mean-variance investment theory advises portfolio diversification, but the value of this advice might be questioned if all the assets tend to fall as they have already fallen. Also, the presence of asymmetric correlations can potentially cause problems for hedging effectiveness, since hedging relies crucially on the correlation between assets hedged (and the financial instruments used). For these reasons, asymmetry related to joint information shocks is of more concern than asymmetry related to mixed information shocks (i.e., two assets moving in the opposite directions). The paper is organized as follows. Section 2 describes the data used in this study, and provides their descriptive statistics. Econometric methods are presented in Section 3, followed by empirical results in Sections 4 and 5. Section 6 offers our conclusions.

We now summarize the main findings of this study that are believed to have important implications for policymakers, market participants and investors in China. The co-movements of the Chinese capital markets do react to large macroeconomic policy shocks as evidenced by structural breaks in the correlation following the drastic 2004 macroeconomic austerity measures whose impact lasted for about 5 months. The T-bond market and its correlations with the Shanghai and Shenzhen stock markets are found to have borne more of the brunt of the macroeconomic contractions than the two stock markets and their correlation. Overall, however, the level of China’s bond–stock market integration is still low, at least lower than that in Europe at some points in time, although China’s stock–stock market integration has reached a quite high level. In addition, the relatively small volatility in T-bond returns implies that investors could reap diversification benefits via “flight to quality” (i.e., by moving their capital out of riskier equities and into safer government securities). Such a portfolio-rebalancing strategy seems to have started to be considered by Chinese investors after the episode of macroeconomic contractions in 2004. As a methodological innovation, we propose a new version of the asymmetric DCC model, MADCC, which seems to have been quite helpful, as it enables us to successfully capture the coexistence of positive-signed and negative-signed asymmetries in the correlation structure, a unique characteristic of the Chinese capital markets. The news impact surfaces based on the MADCC provide “intuitionalized” evidence that the bond–stock correlations tend to increase only when their returns have both been hit by bad news, but the stock–stock correlations tend to increase only when their returns have both been hit by good news.