基于GARCH模型族的上证综指实证研究

早在20世纪60年代,Fama（1965）观察到投机性价格的变化和收益率的变化具有稳定时期和易变时期,大的报酬紧连着大的报酬,小的报酬紧连着小的报酬,称为波动集群性（Mandelbrot,1963;Fama,1965）。此后,国外开始对投机性价格波动特征进行了大量研究,其中Engle（1982）提出的自回归条件异方差性模型（即ARCH模型）很好地捕获条件异方差性以及尖峰厚尾性,拟合了波动率聚类现象。     

上海股市收益率GARCH模型族的实证研究

利用GARCH模型族,实证分析了上海股市收益率的波动特征,指出上海股市收益率不仅具有条件异方差性,而且具有“杠杆效应”。     

基于上证指数的中国股市ARCH效应分析

股票价格频繁的波动是股票市场最明显的特征之一。本文以上证指数每日收益率为研究对象,检验股票价格指数的波动是否具有条件异方差性,检验得到肯定回答后,通过ARCH族模型来研究股价指数收益率的波动性。     

沪深两市股票市场的Garch类模型分析

本文采用EGARCH(1,1)模型和Granger因果检验分别对我国沪、深两个股票交易市场2001年2月到2006年的3月期间的股票价格指数的日度数据进行了分析。EGARCH(1,1)模型表明这两个市场股指收益率存在条件异方差、持久的波动聚类现象和杠杆效应。Granger因果检验则说明两市场波动之间则存在双向的Granger因果关系,即两个市场之间表现出对称的波动溢出效应。     

人民币汇率波动规律研究——基于GARCH模型

利用GARCH类模型对2007年1月1日至2010年4月30日的人民币/美元的日汇率序列的波动特性进行了计量检验,检验结果表明在此期间汇率序列的波动存在显著的条件异方差性和波动性过程的非对称性,且还具有波动的集群性特征。     

基于ARCH类模型的国债市场波动率研究

本文运用自回归条件异方差(ARCH)类模型对我国国债市场(包括银行间国债市场和交易所国债市场)的波动率进行实证分析,结果显示:我国国债市场具有波动率集聚的特征,存在ARCH效应,不存在杠杆效应和高风险、高收益特征,同时国债市场的波动具有很强的持续性。     

深圳成分指数波动率的实证研究——GQARCH-M模型的应用

ARCH类模型是刻画序列波动集束(volatilityclustering)和异方差的有力工具,将深圳成分指数作为研究对象,运用GQARCH-M模型,并引入了对波动时段进行划分的概念,针对不同的波动时段,分析其波动率的非对称特点,得出在股市发展的前两个时段,利好消息比利空消息对股市造成的影响要大,并给出了合理的解释。此外,收益率和波动性在第二时段后具有显著的正相关关系,这也为我国股市逐步完善提供了实证依据。     

基于ARCH类模型的国债市场波动率研究

本文运用自回归条件异方差（ARCH）类模型对我国国债市场（包括银行间国债市场和交易所国债市场）的波动率进行实证分析，结果显示：我国国债市场具有波动率集聚的特征，存在ARCH效应，不存在杠杆效应和高风险、高收益特征，同时国债市场的波动具有很强的持续性。     

基于GARCH模型的股市收益与股指波动的实证研究

文章采用四个市场指数建立以来至2010年12月30日止,运用传统的最小二乘法和改进的自回归条件异方差模型( GARCH),从A股市场指数的波动性入手,研究四个市场收益率的特征,对指教序列的分布、序列的平稳性和异方差进行检验,从而对A股市场指教的波动有更深刻的认识和把握.     

基于GARCH族模型的中国股市通信板块收益率波动分析

文章运用GARCH族模型分析中国沪深股市通信板块指数日收益率的波动特性,发现信息通信板块收益率是一个平稳过程,其波动具有＂聚集＂和＂非对称效应＂的特点。而GARCH（2,1）模型比GARCH（1,1）模型更好地消除了收益率序列的异方差,其中非对称模型TARCH（2,1）模型的拟合效果最好;GARCH-M模型和非对称的CARCH（1,1）模型都不适用于描述其收益率的波动特征。     

金融危机时期公允价值计量对股票收益率和市场波动性的相关性研究

文章研究我国A股非金融类上市公司在金融危机时期,公允价值计量的资产和损益对股票收益率和市场波动性的影响。文章选取2007年第4季度-2009年第2季度7个季度的面板数据,运用固定效应模型进行回归分析,结果表明:公允价值计量的资产对股票收益率有显著影响,公允价值计量的损益的会计信息没有反映在股价上,对股票收益率无显著影响;公允价值计量的资产和损益都没有加剧市场波动,反而降低了市场波动,即公允价值在我国没有起到金融危机助推器的作用。     

Management Motivation and Market Assessment: Revaluations of Fixed Assets

The study examines Hong Kong managers' motivation for upward revaluation of fixed assets. The results show that revaluations are positively associated with the firms' future operating performance, suggesting that the managers' primary motivation for upward revaluation of fixed assets has been to signal fair value of assets to financial statements users. Another motivation for revaluations has been to improve the firm's borrowing capacity. The results also indicate a significantly positive association between revaluations and stock prices and returns, suggesting that the market's assessment aligns with the managers' revaluations.     

The VIX,the variance premium and stock market volatility

We decompose the squared VIX index, derived from US S&P500 options prices, into the conditional variance of stock returns and the equity variance premium. We evaluate a plethora of state-of-the-art volatility forecasting models to produce an accurate measure of the conditional variance. We then examine the predictive power of the VIX and its two components for stock market returns, economic activity and financial instability. The variance premium predicts stock returns while the conditional stock market variance predicts economic activity and has a relatively higher predictive power for financial instability than does the variance premium.     

Measuring financial risk and portfolio reversion with time changed tempered stable Lévy processes

Given that underlying assets in financial markets exhibit stylized facts such as leptokurtosis, asymmetry, clustering properties and heteroskedasticity effect, this paper applies the stochastic volatility models driven by tempered stable Lévy processes to construct time changed tempered stable Lévy processes (TSSV) for financial risk measurement and portfolio reversion. The TSSV model framework permits infinite activity jump behaviors of returns dynamics and time varying volatility consistently observed in financial markets by introducing time changing volatility into tempered stable processes which specially refer to normal tempered stable (NTS) distribution as well as classical tempered stable (CTS) distribution, capturing leptokurtosis, fat tailedness and asymmetry features of returns in addition to volatility clustering effect in stochastic volatility. Through employing the analytical characteristic function and fast Fourier transform (FFT) technique, the closed form formulas for probability density function (PDF) of returns, value at risk (VaR) and conditional value at risk (CVaR) can be derived. Finally, in order to forecast extreme events and volatile market, we perform empirical researches on Hangseng index to measure risks and construct portfolio based on risk adjusted reward risk stock selection criteria employing TSSV models, with the stochastic volatility normal tempered stable (NTSSV) model producing superior performances relative to others.     

A quantile-copula approach to dependence between financial assets

This article unveils the dependence structure between United States stock prices, crude oil prices, exchange rates, and U.S. interest rates. In particular, we employ linear and nonlinear estimation methods, such as quantile regression and the quantile-copula approach. Over the 1998–2017 period, we find that there is a positive relationship between the dollar value and the S&P 500 stock price, with the exception of the lower and upper tails of the stock return distribution. Further evidence is obtained on the dependence structure between other asset returns. The stock returns are negatively related to oil prices but positively to U.S. interest rates. Our results highlight the way that financial assets are linked, which have implications for risk management and monetary policy.     

Monetary Policy and the Stock Market: Theory and Empirical Evidence

This paper gives a comprehensive review of the literature on the interaction between real stock returns, inflation, and money growth, with a special emphasis on the role of monetary policy. This is an area of research that has interested monetary and financial economists for a long time. Monetary economists have been interested in the question whether money has any effect on real stock prices, while financial economists have investigated whether equity is a good hedge against inflation. Empirical studies show that money can be helpful in predicting future stock returns. Empirical evidence also suggest that equity is not a good hedge against inflation in the short run but may be so in the long run. The short-run negative relation between stock returns and inflation can easily be explained by theoretical models. If the central bank conducts a countercyclical monetary policy this will result in a negative relation between inflation and stock returns, while if it conducts a procyclical policy we could observe a positive relation. According to both theoretical and empirical studies investors receive an inflation risk premium for holding equity.     

Market Optimism and Merger Waves

We argue that stock and bond market booms and merger waves are both driven by increases in optimism in financial markets and discuss two behavioral hypotheses, the managerial discretion and overvaluation hypotheses that claim that merger waves are driven by market optimism. Empirical support for the managerial theory is provided by evidence that the amounts of assets acquired increase as optimism in financial markets increases and that the returns to acquiring companies are inversely related to market optimism at the time of mergers. Our measures of market optimism also explain managerial choices of finance for mergers. Copyright © 2012 John Wiley & Sons, Ltd.     

Do cryptocurrencies provide better hedging? Evidence from major equity markets during COVID-19 pandemic

Using the five-minute interval price data of two cryptocurrencies and eight stock market indices, we examine the risk spillover and hedging effectiveness between these two assets. Our approach provides a comparative assessment encompassing the pre-COVID-19 and COVID-19 sample periods. We employ copula models to assess the dependence and risk spillover from Bitcoin and Ethereum to stock market returns during both the pre-COVID-19 and COVID-19 periods. Notably, the COVID-19 pandemic has increased the risk spillover from Bitcoin and Ethereum to stock market returns. The findings vis-à-vis portfolio weights and hedge effectiveness highlight hedging gains; however, optimal investments in Bitcoin and Ethereum have reduced during the COVID-19 pandemic, while the cost of hedging has increased during this period. The findings also confirm that cryptocurrencies cannot provide incremental gains by hedging stock market risk during the COVID-19 pandemic.