基于多重分形方法的我国期货市场特征研究

本文运用多重分形消除趋势波动分析方法,对之前较少被研究的中国铜和小麦两个期货品种的价格收益序列进行实证研究。结果表明,两种期货价格收益序列不服从正态分布且具有尖峰态特征,因此二者均存在明显的多重分形特征,单一的标度指数无法对其充分的描述。对其多重分形成因进行分析后发现,收益序列的波动相关性导致其多重分形特征,并引起价格的有偏随机游走,市场未达到弱式有效。     

基于MF-DFA的中国股指期货价格的多重分形实证研究

本文通过应用多重分形谱分析法和多重分形消除趋势波动分析（MF-DFA）法,研究了新产生的中国股指期货市场的多重分形性。通过对2942个股指期货最后十分钟结算价格的分析,我们发现中国股指期货的收益率具有长程相关性和多重分形性,期货价格波动并不能用单一的标度指数进行充分描述。进一步通过将原始序列和转换后的收益序列进行比较,转换过程包括重排以及相位随机化,我们发现导致中国股指期货市场多重分形性的两种不同成因。研究结果表明,虽然厚尾分布是造成多重分形性的一个方面,但长程相关性才是引起中国股指期货市场多重分形的主要原因。     

我国基金风格资产收益长记忆性分析——基于修正R/S方法的研究

证券市场是个典型的非线性复杂系统。本文运用修正R/S分析法对我国基金风格资产收益单一分形的基本统计特征进行检验,并与经典R/S方法进行对比分析。研究结果表明:在日、周、月等三种时间标度下Hurst指数均显著大于0.5,表现为持久相关性特征,说明股市风格具有长记忆性;从经典R/S分析结果看,我国股市风格具有显著的分形结构特征,风格资产指数收益率序列具有长记忆性,不同风格资产的业绩具有不同的周期性。     

中国沿海煤炭运价衍生品市场多重分形特性研究

本文采用多重分形理论对上海航交所推出的中国沿海煤炭(秦沪线和秦广线)运价指数及其衍生品的市场风险特性进行实证研究.通过基本的R/S分析和多重分形交叉降趋脉动分析方法(MF-X-DFA)研究该市场的日度收益率序列,研究结果发现:中国沿海煤炭:秦沪和秦广两条航线的期货市场均具有多重分形特性;然而,沿海煤炭现货市场的多重分形特性还没有形成.     

互联网货币基金的多重分形分析——以余额宝、理财通为例

本文以余额宝、理财通为互联网货币基金代表,选取两种货币基金2013-2020年的七日年化收益率数据为研究区间,运用多重分形分析方法研究了两种货币基金的对数日收益率序列的多标度行为及其之间的非线性动态关系。研究结果表明,余额宝与理财通两收益率序列均存在时变的多重分形特征。比较而言,理财通收益率序列的多重分形强度更大,而余额宝收益率序列的正持续性更强;在小波动下,两收益率序列均表现出强持续性;两种互联网货币基金之间的交互相关关系是正相关的,具有多重分形性。同时,讨论了新冠肺炎疫情对互联网货币基金收益率的影响,并针对当前新冠肺炎疫情形势,给出投资理财建议。     

基于多重分形的我国股票波动特征研究

本文对上证指数收盘指数的收益率进行统计性描述,发现其呈现"尖峰胖尾"现象;采用Hurst指数检验法,运用软件分析出上证指数存在多重分形波动特征。并采用多重分形区趋势法(MF-DFA法),做出上证指数收益率序列的多重分形谱f(α)与奇异指数α的关系图,得出我国股票存在多重分形现象,有着显著长记忆性,且效果图优于文[5],进而说明时间的选取影响着分形效果。     

中日证券市场几个重要问题的比较研究

本文系统地比较研究了中国和日本证券市场的多个问题，从数据挖掘和计量经济学角度分别针对价格序列、价格波动性和周内效应三方面对中日证券市场进行分析，得出指数收盘价时间序列比较方面，中国和日本两个证券市场的确存在一定的相似性，但中国市场的短期波动要大于日本市场；中国证券市场中上海和深圳股市的波动具有很强的波动聚类性和持续性，日本证券市场除了过去的方差记忆，还存在其他未知的影响市场变动的因素；周内效应在两国的体现不同等一系列重要结论。     

上海股票、债券和基金市场的联动性——基于DCC-MIDAS的实证

运用DCC-MIDAS模型和GARCH-MIDAS模型,深入研究了上海股票、债券和基金市场间的联动性及宏观不确定性对收益率波动的影响。结果表明,股市与基市具有高度的长期和短期正相关性;债市与股、基两个市场的长期相关性较小,短期相关远大于长期相关且呈现大幅波动和频繁的正负转换;货币供应量和工业生产指数对三个市场的收益率长期波动产生正向影响,经济政策不确定性指数的影响则是负向。且宏观不确定性变量对股市和基市的影响显著大于债市。     

确定股票时间序列的多重分形类型

为了探索股票时间序列的无标度性,我们应用多重分形消除趋势涨落分析的方法(MF-DFA)来研究沃尔玛股票指数(WMT)日收盘价.研究结果表明沃尔玛股票指数日收盘价的变化具有多重分形的特性.然后,随机打乱时间序列的次序,用MF-DFA方法分析打乱序列的多重分形性质,得出沃尔玛股票指数的多重分形主要是由概率密度函数产生的,分布多重分形占主导地位.比较原始序列和打乱序列的多重分形性质,得出打乱序列的多重分形性弱于原始序列的多重分形性.这将为多重分形在金融理论方面的应用提供重要的理论基础.     

上海股票市场的分形特征研究

本文对有效市场假说和分型市场假说的基本理论做了介绍,对上海证券市场的分形结构进行了研究,并对上证指数收盘价的日数据和周数据进行R/S分析并计算Hurst指数,通过对比发现,上海股票市场不属于EMH所描述的有效市场,而是具有自相似性、状态持续性、长期记忆周期等明显的分形特征。     

Dynamic Modeling of Tail Risk: Applications to China, Hong Kong and Other Asian Markets

In this paper, we study the extreme dependence between the markets in Hong Kong, Shanghai, Shenzhen, Taiwan and Singapore. The tail dependence coefficient (TDC), which measures how likely financial returns move together in extreme market conditions, is modeled dynamically using the Multivariate Generalized Autoregressive Conditional Heteroscedasticity model with the time-varying correlation matrix of Tse and Tsui (Journal of Business & Economic Statistics, 20(3):351–363, 2002). The time paths of the TDC indicate that Hong Kong stocks had the highest extreme dependence during the Asian financial crisis and their TDCs have followed an increasing trend since 2006. The results in this paper also show that the TDC pattern of Singapore with the other markets is very similar to the TDC pattern of Hong Kong with the other markets. An increasing trend in the extreme dependence between Shanghai A Share Index and Shanghai B Share Index and between the Hang Seng Index and the Hong Kong China Enterprise Index is observed from 2002 to 2007. A substantial rise in the TDC between Shenzhen A Share Index and Shenzhen B Share Index was recorded after the China market reforms in 2005. Our TDC modeling with Asian market data provides evidence that Asian markets are becoming integrated and their extreme co-movements during financial turmoil are becoming stronger.     

Nonlinear interdependence of the Chinese stock markets

The methodologies and assumptions in financial integration studies are problematic and may lead to spurious empirical results. Using surrogate data analysis and the mutual prediction method of testing for nonlinear interdependence, it is feasible for an analyst, with a scant knowledge of the underlying dynamics of two dynamical systems, to show whether or not the systems are interdependent. This study applies these techniques in testing for nonlinear interdependence of three Chinese stock markets: Shanghai, Shenzhen, and Hong Kong. The empirical results of the present study indicate that the stock market series are nonlinear and that the Chinese stock exchanges are nonlinearly interdependent. Specifically, the evidence indicates that Shanghai and Shenzhen markets are bi-directionally interdependent, while Shanghai and Hong Kong as well as Shenzhen and Hong Kong markets are unidirectionally interdependent, with the direction of interdependence going from the mainland's markets to the Hong Kong market.     

Red chips or H shares: which China-backed securities process information the fastest?

This study examines the information flow between China-backed securities, namely H shares, red chips, Shanghai and Shenzhen listed common shares. We document several findings. We find that an exponential generalized autoregressive conditional heteroscedasticity in mean (EGARCH-M) model appears to describe adequately the return process of the China-backed securities. Our empirical findings show that both H shares and red chips (which are listed in Hong Kong) are more sensitive to ‘good’ news than ‘bad’ news, while stocks listed in the China market are more sensitive to ‘bad’ news than ‘good’ news. Using a multivariate EGARCH-M model, we have found significant return and volatility spillover effects among the China-backed securities. Our study indicates that the red chips appear to spread information to other China-backed markets ‘directly’ or ‘indirectly’. The results imply that the red chip market processes information faster than the other markets.     

Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China's Stock Markets

This paper examines empirical contemporaneous and causal relationships between trading volume, stock returns and return volatility in China's four stock exchanges and across these markets. We find that trading volume does not Granger-cause stock market returns on each of the markets. As for the cross-market causal relationship in China's stock markets, there is evidence of a feedback relationship in returns between Shanghai A and Shenzhen B stocks, and between Shanghai B and Shenzhen B stocks. Shanghai B return helps predict the return of Shenzhen A stocks. Shanghai A volume Granger-causes return of Shenzhen B. Shenzhen B volume helps predict the return of Shanghai B stocks. This paper also investigates the causal relationship among these three variables between China's stock markets and the US stock market and between China and Hong Kong. We find that US return helps predict returns of Shanghai A and Shanghai B stocks. US and Hong Kong volumes do not Granger-cause either return or volatility in China's stock markets. In short, information contained in returns, volatility, and volume from financial markets in the US and Hong Kong has very weak predictive power for Chinese financial market variables.     

Office Property Returns in Shanghai,Guangzhou, and Shenzhen

Commercial property development in China has been a growth industry in recent years. This article examines the returns on office property in the three major cities of Shanghai, Guangzhou, and Shenzhen in the period 1991 to 1997. Analyses based on the Security Market Line (SML) show that property investments in the office sector in Shanghai and Guangzhou have excess returns and that Shanghai office property tends to dominate the optimal portfolios due to its superior risk-adjusted returns. While equal returns in Shanghai, Guangzhou, and Shenzhen cannot be rejected, the Shanghai office property is subject to the least systematic risk, compared with all other cities. Hong Kong office property is included only in the less risky optimal portfolios. In addition, our results indicate that there is little correlation between the office property returns in Hong Kong and the office property in Guangzhou and Shenzhen. Guangzhou and Shenzhen office markets, which are geographically relatively close to Hong Kong, tend to be more volatile than the Shanghai office market. However, owing to Shenzhens proximity to Hong Kong, there is significant correlation between the returns of the office property in Shenzhen and the office property in Hong Kong.     

Determinants of returns and volatility of Chinese ADRs at NYSE

This paper examines the determinants of returns and of volatility of the Chinese ADRs as listed at NYSE. Using an autoregressive conditional heteroskedasticity (ARCH) model and data from 16 April 1998 through 30 September 2004, we find that Hong Kong stock market (underlying market), US stock market (host market), and local (Shanghai A and B) markets all are important determinants of returns of the Chinese ADRs. However, the underlying Hong Kong market has the most significant impact on mean returns of the ADRs. In terms of the determinants of the conditional volatility of the ADRs returns, only shocks to the underlying markets are significant. These results are consistent with [Kim, M., Szakmary, A.C., Mathur, I., 2000. Price transmission dynamics between ADRs and their underlying foreign securities. Journal of Banking and Finance 24, 1359–1382] who find that the most influential factor in pricing the ADRs in Japan, UK, Sweden, The Netherlands and Australia is their underlying shares. Implications of the results for investors are discussed.     

Causality and cointegration of stock markets among the United States,Japan and the South China Growth Triangle

This paper explores the causality and cointegration relationships among the stock markets of the United States, Japan and the South China Growth Triangle (SCGT) region. Applying the recently advanced unit root and cointegration techniques that allow for structural breaks over the sample period (October 2, 1992 to June 30, 1997), we find that there exists no cointegration among these markets except for that between Shanghai and Shenzhen. By invoking the Granger causality test and considering the non-synchronous trading problem, we will show that stock price changes in the US have more impact on SCGT markets than do those of Japan. More specifically, price changes in the US can be used to predict those of the Hong Kong and Taiwan markets on next day. Similarly, price changes on the Hong Kong stock market lead the Taiwan market by 1 day. Furthermore, the stock returns of the US and Hong Kong markets are found to be contemporaneous. Finally, there is a significant feedback relationship between the Shanghai and the Shenzhen Stock Exchanges.     

US ADR and Hong Kong H-share discounts of Shanghai-listed firms

This paper examines the differential between the share prices of Chinese securities traded on their home market of Shanghai versus prices observed offshore in New York and Hong Kong. The discounts attached to Chinese securities, whether trading as ADRs on the NYSE or as H-shares on the Hong Kong market, appear to have been significantly influenced by changes in both exchange rate expectations and investor sentiment during 1998–2006. Expected exchange rate changes alone account for approximately 40% of the total variation in each case. This is combined with large cross-sectional variation, however, reflecting additional significant market-wide and company-specific sentiment effects.     

新闻驱动的量化投资

本文讨论了在香港股市中利用新闻软信息进行量化处理并进行统计套利的量化投资策略,提出了测量新闻消息作者情绪的统计学指数,以及测量新闻消息与证券的相关的统计学指数,以及新闻消息对相关证券影响方向（正、负面或者中性）的指数。本文证明在香港股票市场上存在着利用这些指数进行统计套利的机会。     

上海和香港两地股市联动性研究--基于GARCH模型的分析

本文基于经济基础假说和市场传染假说两大基础理论,将股票收益率分解为开盘收益率和收盘收益率,运用GARCH．M模型研究了上海股市和香港股市之间的联动关系。结果显示,两大股市存在相互影响的联动关系,但是上海对香港股市的影响要强于香港对上海股市的影响,反映出两地之间的紧密经济关系及大陆对香港地区经济影响日益增强的现实。