Search

The booming real estate sector has been regarded as the “gray rhino” risk emerging in China over the past decade. Yet, the house price volatility per se has not been thoroughly examined. Filling the gap in the literature, this paper explores the house price volatility and its determinants for 70 large and medium-sized cities in China, using an extensive monthly data set from 2005 to 2019. We find evidence of significant geographical disparities in both the GARCH effects and the best-fitted volatility specification. Significant GARCH effects are found in 57 cities, among which 40% of cities show a persistent volatility pattern. We also find that both the house price volatility pattern and the associated volatility value are affected significantly by education and healthcare amenities. [ABSTRACT FROM AUTHOR]

This study analyzes the impact of Covid-19 on stock market liquidity of China and four worst hit countries by the pandemic. Using daily data for the stock market illiquidity spanning over July 1, 2019 to July 10, 2020 and the data for new cases and deaths over the period from December 31, 2019 to July 10, 2020, the results of our GARCH analysis show that liquidity in stock markets of all the sampled countries hit hard by the news of the Covid-19 outbreak. We find that for all sampled countries increase in illiquidity due to temporary shocks reverts to long term trend shortly, suggesting that the liquidity shocks due to the incidence of Covid-19 were short lived. The findings of our VAR analysis show an absence of any short-term relationship between Covid-19 new cases or deaths and illiquidity. Since the series are not integrated at same level, long-term relationship between Covid-19 and stock market illiquidity do not exist as well suggesting no evidence of the effect of Covid-19 on stock market liquidity. [ABSTRACT FROM AUTHOR]

Evolving geopolitical relationships between countries (especially between China and the United States) in recent years have highlighted dynamically changing trade patterns across the globe, all of which elevate risk and uncertainty for transport service providers. In order to mitigate risks, shipowners and operators must be able to estimate risks appropriately; one potentially promising method of doing so is through the value‐at‐risk (VaR) method. VaR describes the worst loss a portfolio is likely to sustain, which will not be exceeded over a target time horizon at a given level of confidence. This article proposes a copula‐based GARCH model to estimate the joint multivariate distribution, which is a key component in VaR estimation. We show that the copula model can capture the VaR more successfully, as compared with the traditional method of calculation. As an empirical study, the expected portfolio VaR is examined when a shipowner chooses among Panamax soybean trading routes under a condition of reduced trade volumes between the United States and China due to the ongoing trade turmoil. This study serves as one of the very few papers in the literature on shipping portfolio VaR analysis. The results have significant implications for shipowners regarding fleet repositioning, decision making, and risk management. [ABSTRACT FROM AUTHOR]

This study evaluates the downside tail risk of coal futures contracts (coke, coking coal and thermal coal) traded in the Chinese market between 2011 and 2021, measured by value at risk (VaR). We examine the one‐day‐ahead VaR forecasting performance with a hybrid econometric and deep learning model (GARCH‐LSTM), GARCH family models, extreme value theory models, quantile regression models and two naïve models (historical simulation and exponentially weighted moving average). We use four backtesting techniques and the model confidence set to identify the optimal models. The results suggest that the models focusing on tail risk or utilising long short‐term memory generate more effective risk management. [ABSTRACT FROM AUTHOR]

America's decoupling‐from‐China debate started after July 2018, reached its peak in August 2020, and is likely to continue even if it may not be a high priority for the Biden administration. Many studies have examined various aspects of this issue, especially the potential economic impacts on the US economy. Unlike previous research, this study looks at the response of stock markets. Using Google Trends data, this study created a weekly dataset from January 2020 to June 2021 to measure investor sentiment towards the US decoupling from China. Employing the generalised autoregressive conditional heteroskedasticity (GARCH) models, the study finds that concern over decoupling is associated with significant variations in stock market prices. From this we can infer that the overall effects of decoupling on the US economy are likely to be considerable. [ABSTRACT FROM AUTHOR]

Can investors use technical analysis to generate positive risk-adjusted returns by observing historical transaction data? The study investigates whether technical indicators (TIs) are beneficial to the returns and risk management of China's stock market investors. It is conducted from the perspective of a distribution forecast rather than a traditional point forecast. The study investigates the TIs' predictability on the distribution of returns. It also examines the TIs' impact on risk management. A high-dimensional-same-frequency information distribution forecasting model, the LASSO-EGARCH model, is built. The LASSO regression's results show that the TIs have limited 'explanatory power' for the return prediction. However, the adaptive moving average and turnover rate show significant and robust effects. The statistical evaluation and economic evaluation show that the TIs information's integration cannot improve the direction forecast's accuracy, nor does it have excess profitability. However, it enables the return distribution to have a better calibration. The above conclusion reveals that the usefulness of the analysis for China's stock market lies in its risk management when the stock price plunges, rather than in excess profits. This may provide a reference for investors who prefer the TIs' analysis. [ABSTRACT FROM AUTHOR]

This research investigates the role of trading volume and data frequency in volatility forecasting by evaluating the performance of Generalized Autoregressive Conditional Heteroskedasticity Mixed‐Data Sampling (GARCH‐MIDAS), traditional GARCH, and intraday GARCH models. We take trading volume as the proxy for information flow and examine whether the Sequential Information Arrival Hypothesis (SIAH) is supported in the China stock market. The contributions of this study are as follows. (1) We provide a more consistent comparison to evaluate the forecasting ability of the MIDAS approach. (2) We extend the literature on the forecasting performance of trading volume to the GARCH‐MIDAS approach. (3) We present clear evidence to support that forecasting ability strongly relies upon data frequency. The empirical results show that: (1) GARCH‐MIDAS is not able to beat the traditional GARCH method when both are estimated by the same predictor sampled at different frequencies; (2) there is a positive relation between trading volume and volatility, but no clear evidence appears that SIAH holds in the China stock market; and (3) high‐frequency data are highly recommended for daily realized volatility (RV) forecasting, whereas intraday GARCH could significantly outperform traditional GARCH and GARCH‐MIDAS in volatility forecasting. [ABSTRACT FROM AUTHOR]

This research investigates the role of high-frequency data in volatility forecasting of the China stock market by particularly feeding different frequency return series directly into a large number of GARCH versions. The contributions of this research are as follows. 1) We provide clear evidence to support that the superiority of traditional time series models in volatility forecasting remains by taking advantage of high-frequency data. 2) We incorporate different distribution assumptions in GARCH models to capture the stylized facts of high-frequency data. The result shows that: 1) data frequency in GARCH application substantially influence the accuracy of volatility forecasting, as the higher the frequency is of the return series, the better are the forecasts provided; 2) non-normal distributions such as skewed student-t and generalized error distribution are more capable at reproducing the stylized facts of both intraday and daily return series than normal distribution; and 3) GARCH estimated by 5-min returns not only outperforms other GARCH alternatives, but also considerably beats RV-based models such as HAR and ARFIMA at volatility forecasting. [ABSTRACT FROM AUTHOR]

With the RMB becoming the fifth international payment currency and its inclusion in the SDR currency basket, coupled with the opening of China's capital market, RMB-related exchange rates have attracted increasing attention because of China's RMB internationalization strategy. Using the DCC-GARCH model, we investigate how domestic (China) and international (USA, EU, UK, and Japan) policies and situations, including RMB internationalization, U.S. QE, European Debt Crisis, Brexit, and Abenomics, influence co-movement of the USD/CNY and SDR/CNY exchange rates from 2010 to 2017. We analyze the co-movement and provide explicit explanations for distinct areas (unrelated, long-term negative, and abruptly positive co-movement areas). Further, we find that RMB-related exchange rates remain largely influenced by domestic policies, while because of China's opening-up policies they are also influenced by the international situations. Both US and EU policies exert a remarkable influence, but the effects of UK and Japan policies are decreasing. [ABSTRACT FROM AUTHOR]

This paper explores the dynamic volatility spillovers among five major futures in China, including rebar, hot rolled coils, iron ore, cooking coal and coke. We employ Dynamic Conditional Correlation (DCC) GARCH model to examine the volatility spillover effects among the markets with considering of structural breaks in variance. What's more, in this study we use modified Iterated Cumulative Sum of Squares (ICSS) algorithm to detect the structural breaks. The empirical results show there are strong correlations across black series futures market. Especially, the relation between rebar and hot rolled coils, coke and cooking coal are more closely than other pairs. This research provides the insight to the information transmission in black series futures market which are meaningful to market participants make hedging and trading strategies. [ABSTRACT FROM AUTHOR]

No consensus exists in the literature on whether stock prices can be predicted, with most existing studies employing point forecasting to predict returns. By contrast, this study adopts the new perspective of distribution forecasting to investigate the predictability of the stock market using the model combination strategy. Specifically, the Shanghai Composite Index and the Shenzhen Component Index are selected as research objects. Seven models – GARCH-norm, GARCH-sstd, EGARCH-sstd, EGARCH-sstd-M, one-component Beta-t-EGARCH, two-component Beta-t-EGARCH, and the EWMA-based nonparametric model – are employed to perform distribution forecasting of the returns. The results of out-of-sample forecasting evaluation show that none of the individual models is "qualified" in terms of predictive power. Therefore, three combinations of individual models were constructed: equal weight combination, log-likelihood score combination, and continuous ranked probability score combination. The latter two combinations were found to always have significant directional predictability and excess profitability, which indicates that the two combined models may be closer to the real data generation process; from the perspective of economic evaluation, they may have a predictive effect on the conditional return distribution in China's stock market. [ABSTRACT FROM AUTHOR]

This article investigates the dependence structure between China's emissions allowances and energy (coal, oil, and natural gas) markets using autoregressive conditional heteroskedasticity (GARCH) and mixed Copula functions. The dependence structures between China's eight emissions allowances and energy markets demonstrate significant regional heterogeneity; an increase (decrease) in coal, crude oil, and natural gas prices induces an increase (decrease) in eight regional emissions allowance prices and confirms regional divergence and market heterogeneity. The Beijing, Guangdong, Hubei, and Chongqing emissions allowances and coal markets demonstrate significant left-tail dependence structures, while the Shanghai, Tianjin, Shenzhen, and Fujian emissions allowances and coal markets demonstrate complex mixed dependence structures. Seven of China's emissions allowances and crude oil markets confirm the existence of complex asymmetric dependence structures, except for the symmetric dependence structure between the Fujian emissions allowances and crude oil markets. However, eight of China's emissions allowances and natural gas markets demonstrate complex asymmetric dependence structures. [ABSTRACT FROM AUTHOR]

Since 2013, China has become the world's largest gold producer and consumer. To gain the corresponding global pricing power in gold, many actions have been taken by China in recent years, including the International Board at Shanghai Gold Exchange, Shanghai-Hong Kong Gold Connect and Shanghai Gold Fix. Our work studies the dependence structure between China's and international gold price and examines whether these moves are changing the dependence structure. We use GARCH-copula models to detect the dynamic dependence and tail dependence. The research period is set to contain the Financial Crisis in 2008, the dramatical plunge of gold price in 2013 and a series of black swan events in 2016. The empirical study shows that some event driven dependence structure breaks are statistically insignificant. And the time-varying Symmetrized Joe-Clayton copula is the best copula to model the dependence structure based on AIC value. Finally, an example of applications of this dependence structure is given by estimating the VaR of an equally weighted portfolio with a simulation-based method. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic dependence structure between the Chinese stock market and the real exchange rate of the Chinese renminbi (RMB) with unconditional and conditional copula models for the period July 22, 2005, to December 31, 2017. The results show that the crisis induced significant structural breaks, and the relationship is weak before the global financial crisis but substantially stronger after the financial crisis, regardless of whether the correlation is positive or negative. Our findings have important implications for global portfolio diversification, risk management, and China's exchange rate policy. [ABSTRACT FROM AUTHOR]

From 2010 to 2017, with interest rate liberalization and capital market development in China, the impact of monetary policies on China's financial markets underwent continuous evolution. Using the DCC-GARCH model, this study investigates the transmission process of monetary policies from the money market to capital markets (stock and bond markets). The results show that in the early stage the instability of the money and stock markets and the downturn in the bond market are primarily caused by the block of monetary policy transmission and the paucity of fund sources in the capital markets. Subsequently, the outbreak of the 2013 money shortage and the 2015 stock market crash are also closely related to monetary policies. In the later periods, the money and stock markets maintain a low degree of correlation for a long time, reducing the impact of destabilizing factors on the stock market. By contrast, with the advancement of interest rate reform and the optimization of bond market structure, the bond market is highly relevant to the money market. The central bank regulates the bond market more effectively using both traditional and innovative monetary policy tools. [ABSTRACT FROM AUTHOR]

Flow of information and volatility coming from stock markets of other countries have significant impact on the stock market of a country. Volatility is even higher in the case the countries enjoy good economic conditions among themselves. The present manuscript aims to probe into the spread of impacts over a large range of returns and volatility in four major equity markets of Asia viz. India, China, Hongkong and Japan for a period of 18 years ranging from 2000 to 2017. The study uses VAR based GARCH model to determine the volatility spillover among the chosen countries for the period under assessment. The empirical outcomes of the study present that all selected markets have responded to their own lag of conditional volatility along with news shocks. The impact of conditional variance is higher in comparison to shocks which is an indication that markets fundamentals are stronger than corrections or shocks. The results of cross country spillover show that volatility of Shanghai Stock Exchange of China and shocks from Japan and Hongkong markets assert a significant effect over volatility of Indian equity market. Volatility of stock markets of Japan and China is not affected by the cross market volatility and shocks spillover from India. In contrast, volatility of Hongkong market is affected by shocks and volatility of Indian equity markets. Findings of the research have meaningful insights for the Governments and regulators, academicians, researchers, investors and fund managers in framing investment strategies in the chosen markets. [ABSTRACT FROM AUTHOR]

From the 1994 bailout policies to the 2015 Shanghai-Hong Kong Stock Connect, the policy impact on the Chinese stock market has changed over time. By May 2015, global investors can directly invest in a more legalized and normalized Chinese stock market, whereas they are still concerned about the policy-oriented market and its attendant risks. In this study, we employ the family of GARCH models to investigate the structural changes in risks with the implementation of a series of policies. Our results show that although many policies improve or stabilize the stock market, certain policies lead to substantial volatility. Among them, macro-control policies and transaction cost adjustments are a double-edged sword, which should be used with caution. Furthermore, with opening-up policies being launched recently, the Chinese stock market has entered a new stage in which it affects international capital markets. However, the increased risks, which may result in a sharp turnaround, cause worry. [ABSTRACT FROM AUTHOR]

This study compares the forecast performance of volatilities between two types of hybrid ANN and GARCH-type models. The findings show that EGARCH-ANN model performs better than other models to forecast the volatilities of log-returns series in Chinese energy market, and there are significant leverage effects in Chinese energy market. [ABSTRACT FROM AUTHOR]

Analyzing the statistical features of fluctuation is remarkably significant for financial risk identification and measurement. In this study, the asymmetric detrended fluctuation analysis (A-DFA) method was applied to evaluate asymmetric multifractal scaling behaviors in the Shanghai and New York gold markets. Our findings showed that the multifractal features of the Chinese and international gold spot markets were asymmetric. The gold return series persisted longer in an increasing trend than in a decreasing trend. Moreover, the asymmetric degree of multifractals in the Chinese and international gold markets decreased with the increase in fluctuation range. In addition, the empirical analysis using sliding window technology indicated that multifractal asymmetry in the Chinese and international gold markets was characterized by its time-varying feature. However, the Shanghai and international gold markets basically shared a similar asymmetric degree evolution pattern. The American subprime mortgage crisis (2008) and the European debt crisis (2010) enhanced the asymmetric degree of the multifractal features of the Chinese and international gold markets. Furthermore, we also make statistical tests for the results of multifractatity and asymmetry, and discuss the origin of them. Finally, results of the empirical analysis using the threshold autoregressive conditional heteroskedasticity (TARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models exhibited that good news had a more significant effect on the cyclical fluctuation of the gold market than bad news. Moreover, good news exerted a more significant effect on the Chinese gold market than on the international gold market. [ABSTRACT FROM AUTHOR]

In this article, we investigate the impacts of futures and options markets on the volatility of the underlying market. Unlike earlier studies, the focus is on their persistence over time. Tests on the Hang Seng index yield several interesting results that often contrast with previous findings. Empirical results suggest that the quality of new information generated by derivative trading determines the impacts on the spot market volatility. The futures market provides new, material information reducing spot market volatility. The Options market, on the other hand, generates noisy information and distorts price, which is followed by an increase in volatility and a decrease in its sensitivity to price change. While the impact of futures persists, that of options mostly disappears as the market matures. Our conjecture is that the futures market is mainly driven by informed, experienced participants, while the options market attracts new, inexperienced investors. [ABSTRACT FROM PUBLISHER]

This paper studies the impact of China’s new energy policies on expected changes and volatility in new energy stock prices. Considering different kinds of policies (energy legislation, binding targets for new energies, economic incentives and technological research and development) and several new energy indexes (China’s new energy sector and the solar, wind, nuclear and lithium battery subsectors), we used a regression model with a GARCH specification and dummy variables to differentiate policy pre- and post-announcement effects on expected returns and volatility. Our evidence indicates that pre- and post-announcement energy legislation policies dampened price volatility in all subsector indexes and that economic incentives had a positive policy announcement effect on all subsector index prices. Other new energy policies had no significant impact on either the mean or the volatility of the new energy assets. The potential implications for policy makers and investors are discussed. [ABSTRACT FROM AUTHOR]

To curb the excessively rapid rise in housing prices in China, the State Council of China promulgated the New National Ten, which restrains speculative investment behavior and has a linkage effect on the stock market. In this study, we use a series of GARCH models to analyze the effect of the New National Ten on the volatility and asymmetry of the Shanghai Composite Index. Specifically, we investigate how changes in investors’ expectations due to regulations affect the stock market. The result clearly illustrates that this policy is effective in stabilizing the stock market. Investors expect a bullish future stock market and only care about the continuing execution of the policy. Finally, policy implications of our findings are discussed. [ABSTRACT FROM PUBLISHER]

This paper used data from the Shenzhen and Shanghai stock markets to simulate the adjusted volatility, and applied time series methods to realize the relationships of the volatilities between the two markets. The unit root test, and co-integration analysis to show whether it exists equilibrium relationship. The result showed that it presented the co-integrated vectors between the volatilities of Shanghai and Shenzhen Stock Exchanges during the research period, and it made the regression more meaningful. Finally, it also showed that the volatility exerted one way influence between these two markets. It significantly rejected for a null hypothesis of Shanghai stock market does not granger caused Shenzhen stock market, and the results of simulated volatilities were consistent with the results in reality. [ABSTRACT FROM PUBLISHER]

Abstract: The objective of this paper is to employ the GARCH (generalized autoregressive conditionally heteroskedastic) methodology to investigate the impact of interest rate on the information flow interpretation. The results show that the information flow interpretation exhibits significant difference in rising period and fluctuating period of interest rate. This difference would suggest that the components of volatility are subject to macroeconomic factor. Some tentative explanations are given. [Copyright &y& Elsevier]

Abstract: From the point of view of no-arbitrage pricing, what matters is how much volatility the stock has, for volatility measures the amount of profit that can be made from shorting stocks and purchasing options. With the short-sales constraints or in the absence of options, however, high volatility is likely to mean arbitrage from stock market. As emerging stock markets for China, investors are increasingly concerned about volatilities of Chinese two stock markets. We estimate volatility''s models for Chinese stock markets’ indexes using Markov chain Monte Carlo (MCMC) method and GARCH. We find that estimated values of volatility parameters are very high for all data frequencies. It suggests that stock returns are extremely volatile even at long term intervals in Chinese markets. Furthermore, this result could be considered that there seems to be arbitrage opportunities in Chinese stock markets. [Copyright &y& Elsevier]

Abstract: This paper uses CVaR as the risk measure and applies EVT to model the tails of the return series so as to estimate risk of assets more accurately. This paper also applies pair Copula to capture the inter-dependence structure between assets and constructs pair Copula-GARCH-EVT model; then, we combine it with Monte Carlo Simulation and Mean-CVaR model to optimize portfolio. Finally, an empirical study of four Indexes from Chinese Stock Market is performed and the result suggests that pair Copula can better characterize the inter-dependence structure between assets and the performance of pair Copula-GARCH-EVT-CVaR model is better than that of multivariate t Copula-GARCH-EVT-CVaR model in portfolio optimization. [Copyright &y& Elsevier]

The aim of this paper is to add to the literature on volatility forecasting using data from the Hong Kong stock market to determine if forecasts from GARCH based models can outperform simple historical averaging models. Overall, unlike previous studies we find that the GARCH models with non-Normal distributions show a robust volatility forecasting performance in comparison to the historical models. The results indicate that although not all models outperform simple historical averaging, the EGARCH based models, with non-normal conditional volatility, tend to produce more accurate out-of-sample forecasts using both standard measures of forecast accuracy and financial loss functions. In addition we test for asymmetric adjustment in the Hang Seng, finding strong evidence of asymmetries due to the domination of financial and property firms in this market. [ABSTRACT FROM AUTHOR]

We investigate the effectiveness of two recent regulatory policy changes on market efficiency in the Chinese A- and B-share markets. Overall, the opening of the B-share market to domestic Chinese investors and the limited opening of the A-share market to foreign investors increase market efficiency. The opening of the B-share market significantly reduces the price differential between A- and B-shares. Furthermore, there is no longer feedback in returns between the two markets in recent years. Our results provide evidence that there is no detrimental effect to market efficiency by integrating Chinese investors to international markets and foreign investors to the Chinese stock markets. [ABSTRACT FROM AUTHOR]

This paper studies the model risk; the risk of selecting a model for estimating the Value-at-Risk (VaR). By considering four GARCH-type volatility processes exponentially weighted moving average (EWMA), generalized autoregressive conditional heteroskedasticity (GARCH), exponential GARCH (EGARCH), and fractionally integrated GARCH (FIGARCH), we evaluate the performance of the estimated VaRs using statistical tests including the Kupiec's likelihood ratio (LR) test, the Christoffersen's LR test, the CHI (Christoffersen, Hahn, and Inoue) specification test, and the CHI nonnested test. The empirical study based on Shanghai Stock Exchange A Share Index indicates that both EGARCH and FIGARCH models perform much better than the other two in VaR computation and that the two CHI tests are more suitable for analyzing model risk. [ABSTRACT FROM AUTHOR]

This paper mainly investigates the asymmetry of the conditional volatility of Chinese stock market by using the GARCH models. We collect the daily data of Shanghai composite index to analyze the volatility asymmetry. The empirical results show that there exists a distinct volatility asymmetry for return. In addition, we extend GJR model to the case with the information flow that is represented by the volume, and the results imply that the volume can't substitute the information flow to account for the conditional volatility asymmetry. [ABSTRACT FROM AUTHOR]

In 1994–1995, Jardine Matheson Group delisted its five major group members from the Stock Exchange of Hong Kong, so that their trading was transferred to Singapore. We document that the trading volume of these five stocks fell after the delisting, and that they became less correlated with the Hong Kong market. We use a multivariate GARCH framework, which also allows us to present the correlations dynamically. We argue that this is evidence in favor of international market segmentation, since the delisting was not associated with a change in the business strategy of the Group. [ABSTRACT FROM AUTHOR]