سرریز نوسانات از بازار سهام چین به کشورهای همسایه اقتصادی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|17758||2013||20 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Mathematics and Computers in Simulation, Volume 94, August 2013, Pages 238–257
This paper examines whether there is evidence of spillovers of volatility from the Chinese stock market to its neighbours and trading partners, including Australia, Hong Kong, Singapore, Japan and USA. China's increasing integration into the global market may have important consequences for investors in related markets. In order to capture these potential effects, we explore these issues using an Autoregressive Moving Average (ARMA) return equation. A univariate GARCH model is then adopted to test for the persistence of volatility in stock market returns, as represented by stock market indices. Finally, univariate GARCH, multivariate VARMA–GARCH, and multivariate VARMA–AGARCH models are used to test for constant conditional correlations and volatility spillover effects across these markets. Each model is used to calculate the conditional volatility between both the Shenzhen and Shanghai Chinese markets and several other markets around the Pacific Basin Area, including Australia, Hong Kong, Japan, Taiwan and Singapore, during four distinct periods, beginning 27 August 1991 and ending 17 November 2010. The empirical results show some evidence of volatility spillovers across these markets in the pre-GFC periods, but there is little evidence of spillover effects from China to related markets during the GFC. We undertook some additional analysis for this period featuring an exploration of whether there was any spillover effect in the mean equations as well as in the variance equations. We used a bimean equation to model the conditional mean in the individual markets plus an ARMA model to capture volatility spillovers from China to the five markets considered. This augmented model showed much greater evidence of spillovers. We also suspected that the correlations were not constant and applied a moving window of 120 days of daily observations to explore time-varying conditional and fitted correlations. There was evidence of non-constant correlations and even a period of negative correlations between the US and China at the height of the GFC. This is presumably because the GFC was initially a US phenomenon, before spreading to developed markets around the globe and it was not a Chinese phenomenon.
Over the past two decades, China has established itself as one of the world's leading economic powers. Its strong economic growth has seen it become one of the world's industrial superpowers. This growth has had a significant impact on other economies around the world through Chinese imports and exports. One economy that has been particularly affected by the strong Chinese growth is the Australian economy, as China relies heavily on Australia's rich mining and resources sector for its growing industries. The Chinese stock market has also grown significantly since its inception in 1991 and has gone through many changes, both regulatory and operational. This paper examines whether there is evidence of spillovers of volatility from the Chinese stock market to its neighbours and trading partners, including Australia, Hong Kong, Singapore, Japan and USA. The paper will use the GARCH(1,1), VARMA–GARCH and VARMA–AGARCH models to estimate volatility and determine evidence of volatility spillovers. 1.1. Literature review In 2003, approximately 1.1% of the world's 3.2% growth was attributed to China based on Purchasing Power Parity (PPP). China has been said to be the “manufacturing breadbasket of the world” [1, p. 2]. China has demonstrated strong growth, which is among the highest in the world, and has maintained it for over a decade. The Gross Domestic Product at the end of 2004 was more than 8 times its size in 1978, the year in which major economic reforms made a turning point in the previously struggling economy. Such growth has led to China's GDP being the 6th largest in the world in 2004. Once the figures have been adjusted for PPP, China was the second largest in the world, after the USA . It had also contributed to the fact that China's GDP has grown by at least 7% per year since 1991 . This economic reform was carried out by the then newly appointed leader, Deng Xiaoping, following the death of the Communist Party leader, Mao Zedong, in 1976, whose economic reform failures since 1949 (including collectivisation of farms and focus on heavy industry) had led to impoverishment and left China isolated from the global market . Deng's 1978 economic reform has been described as “a watershed in Chinese economic policy” [1, p. 1]. Barth et al. [1, p. 1] state that 1978 was the year in which “China began taking its first tentative steps away from a centrally-planned communist economy towards a mixed socialist-market system”, and as a result “has produced rapid growth in both GDP and exports, and has been supported by large flows of foreign private direct investment rather than external official assistance”. In other words, China has opened itself to international trade more than ever before, and was richly rewarded for doing so. In 2002, after several reforms, banks controlled approximately 80% of total financial assets. In addition, shares only accounted for about 15% of total financial assets (compared with 46% for USA), and approximately two-thirds of the shares listed on the Shenzhen and Shanghai stock exchanges, China's only two stock exchanges at the time, were government owned and non-tradable. Corporate bonds accounted for less than 1% [1. p. 4]. Such financial systems are often very sensitive to institutional failure and contagion, particularly as the ‘big four’, China's largest 4 banks, all state-owned, accounted for 59% of banking assets and about one-half of the total of national financial assets [1, p. 7], which would make funds difficult to obtain in the event of institutional failure. To make matters worse, it is estimated that prior to the 1998, more than 20% of total loans in the banking system were bad or non-performing . In addition, the lack of efficient capital markets implies that the allocation of capital to firms, and their investment decisions, is at the discretion of banks rather than markets. As a result, firms could obtain funds for sub-optimal investments, whereas in more efficient capital markets, the securities issued by those firms might suffer if their investments were sub-optimal, non-performing, or if a misuse of capital were to occur (see ). Green  claims that, if China's economic growth is to be sustained in the long run, it must create effective financial institutions. There have been several reforms made by the Chinese government to try and develop a financial system that is better adjusted and secure. Green  identifies three distinct periods or phases of institutional change: 1. The first period is from the end of 1990 to the end of 1992. In a move to try and create a more balanced financial system, the government opened two stock exchanges: Shanghai in late 1990, and Shenzhen in mid-1991. Barth et al.  state that this was one of the major reforms or innovations to the Chinese financial system in recent years. Nevertheless, although the exchanges were formed on “the status of a legal-person organization and not a government bureau” [12, p. 9], and were generally at the senior staff's discretion, the People's Bank Of China still maintained control of operational and policy issues. Green [12, p. 9] claims this was simply a “proxy for control by municipal leaders”, who were inexperienced at managing operations with such a capitalist nature and, due to the strong presence of communism in China, were fearful of its political and social implications. In 1992, Deng and the politburo had reassured the people of China that this was a necessary reform, which led to thousands of state-owned enterprises attempting to become share-holding companies. This, in turn, led to an escalation in prices and demand for IPO shares, with the number of listed companies more than tripling between 1992 and 1993 . Unfortunately, officials had sold all IPO application forms on the black market. This led to the disturbances in Shenzhen known as ‘8.10’ (short for 10th of August), which were considered to be some of the most serious social disturbances in China at the time. As a result, the issuing and listing of shares was suspended while the government tried to find a new way of regulating the stock market [12, pp. 10–11]. 2. The second period Green identifies is between the end of 1992 and 1996. Towards the end of 1992, the China Securities Regulatory Commission (CSRC) was established as the “capital market watchdog” [29, p. 2]. As it was not established as a government body, it had not been included in the government budget and was not allowed to reprimand offenders and wrongdoers or publicize administrative regulation. This is confirmed by Yao [29, p. 2], who states that “the stock market used to be highly speculative and government manipulated …government policy was the dominant factor (in the) China stock market and directly triggered a bull market”. This resulted in the market becoming highly volatile . Another important change during this period was the change in the foreign exchange rate system in China. The Chinese Yuan (RMB) was under the government's control, and was not fully convertible in the global market. When the government relaxed regulations and allowed the introduction of “Swap shops” [5, p. 6], an inconsistency was found between the official exchange rate of RMB5.76 per US dollar, and the swap shop rate which was closer to RMB9 per US dollar. A government initiative to align the two in 1994 resulted in the official exchange rate settling at RMB8.61 per US dollar, a depreciation of about 50%. This depreciation of the RMB made Chinese exports more attractive and increased the flow of foreign funds into China . 3. The third period identified by Green  stretches from mid-1996 to the present. Green  claims that: “In 1997, radical institutional change occurred, resulting in the empowerment of the CSRC and its effective take-over of the exchanges. Given its new powers, the CSRC has been able to reduce market instability and orient development towards the central government leadership's priorities.” [12, p. 1] In 1996 and 1997, there have been a large number of new regulations put in place to reduce the volatility of the market and to reduce the incentives of government officials to manipulate it. Green [12, p. 20] suggests these changes followed as the leaders of China have “become convinced of the deleterious effects of local regulation and …the wider dangers to the stability of the financial system that came from the stock market actions of local leaders”. In 1998, the CSRC has finally taken over “supervisory responsibility of securities market regulation from the PBOC” [1, p. 17]. It also controlled all aspects of market development such as the introduction and research of new products and securities, the results of which are not all positive, as the exchanges “cannot innovate in a way that an exchange should ideally be allowed to…(They) have been operated more as divisions of the CSRC than as independent business” [12, p. 24]. One can claim that, despite the latter, the Chinese stock market operates more efficiently today than 10 years ago. Another important event was that China's ‘big four’ have also received over $33 billion of capital from the government in order to help them eliminate non-performing loans and to minimize the impact of the Asian Financial Crisis [1, p. 17]. China has become an important player in international markets, as demonstrated by Moon and Yu [26, p. 20], who state that: “As the first and second largest economies in terms of purchasing power in the world, the US and China's integration and competition in global capital market will be important in global portfolio management, hedging and trading”. However, integration into the global market can involve spillover effects of returns and volatility across markets. This has been found to occur between China and several of its trading partners. Using a symmetric and asymmetric spillover GARCH approach, Moon and Yu  find evidence of volatility spillover effects between the USA and China, claiming that ‘good news’ from the USA will reduce the variance in China's stock returns. They also find evidence of symmetric volatility spillover effects from China to the USA, and through the USA into international markets. Yi et al. , who use a Fractionally Integrated Vector Error Correction Model with a multivariate GARCH model, and Johansson and Ljungwall  find evidence that the Chinese stock market has stronger ties with the neighbouring Hong Kong market then it does with the USA, despite the size of the US economy, and that there are significant spillover effects for both returns and volatility among China, Hong Kong and Taiwan. This is supported by So and Tse , who assert that Asian markets are becoming increasingly integrated, and that there is evidence to suggest that their co-movements during periods of financial distress are becoming increasingly strong. Moon and Yu [26, p. 20] state that “China's stock market has more information influence on the international stock market transmission since December 2005 as its stock exchanges became more liquid, open and influential”. This is also confirmed by Yilmaz , suggesting that China's stock market is one of significant importance within Asia and international markets. The focus of our paper is on the impact of China's integration into the global market, and the extent to which it may involve spillover effects of returns and volatility across markets. This is of significance for several reasons: 1. It may affect the selection of shares for investors and fund managers who are interested in international equity, especially those interested in the Pacific-Basin area. 2. It may have an effect on portfolio optimization for investors and fund managers alike. 3. It may have implications for Australian markets. Australia's strong economic growth over the past decade (and of Western Australia, in particular), as well as its relative resilience during the recent GFC, has often been attributed to the corresponding growth in China. This is due to the fact that China has relied significantly on Australia's rich resource sector to fuel its industrial growth. Understanding the relationship of the volatilities of the two markets may have significant implications for Australian investors. 4. It may also have an effect on the pricing of financial assets.
نتیجه گیری انگلیسی
Overall, the three models give fairly similar results. The GARCH (1,1) demonstrated evidence of a volatility spillovers in period 1 for three markets, but only for Japan in period 2, nothing in period 3, and only for Singapore in period 4. Furthermore, on the few occasions where the spillover proved to be statistically significant, the size of the spillover (as indicated by the size of the β2 coefficient) and the predictive power of the equations (as indicated by the value of R-squared) were very small. However, all of the markets demonstrated a significant GARCH effect, revealing that their current volatility can be explained to some degree by their lagged volatility. The degree of impact of lagged volatility on current volatility changed from market to market, but in most cases the β1 coefficient remained around 0.9. The VARMA–GARCH model produced similar results. The model is a multivariate model, where the equation relates a market's volatility to its own lagged volatility and lagged residual, as well as those of the China. The VARMA–GARCH revealed that, although all of the markets tested, with the exception of the USA, showed some evidence of volatility spillovers from China during periods 1 and/or 2, none showed any evidence of spillovers during periods 3 and 4. However, all of the markets showed significant GARCH effects, meaning that their volatility did depend on their own lagged squared residual and volatility. The VARMA–AGARCH produced different results yet again. The VARMA–AGARCH is a multivariate model, which is an extension of the VARMA–GARCH model. However it is an asymmetric model – it relates a market's volatility to it's own lagged volatility, but has two separate coefficients for negative and positive returns, as well as the lagged squared residual and lagged volatility for the China. All of the markets showed a significant GARCH effects for each period. The different markets showed different relationships for positive and negative returns throughout the period. However, one common theme was the positive relationship between negative returns and volatility in period 3 and an even greater consistency in period 4. A common prior would be that ‘bad news’ increases volatility in the market while ‘good news’, despite being significant in HK, SNG and JPN in period 2 and significant in most markets in period 3, should not have the same impact on volatility as ‘bad news’. This seems to be the case, as ‘bad news’ had universal significance in periods 3 and 4.