基于动态VaR模型的我国银行间拆借市场风险度量研究

本文介绍了一种基于GARCH和非参数法的动态VaR模型——L_VaR模型,用来度量市场风险与流动性风险两者综合风险的大小。并通过采样我国银行间隔夜拆借的高频交易数据,以及SAS软件的数据处理分析发现,GARCH（1,1）模型能较好地拟合隔夜拆借利率的波动情况,而非参数估计法（Boot- strap）能较准确地估计拆借市场流动性的波动水平。实证结果表明。基于动态VaR模型对于市场风险与流动性风险两者综合风险的短期预测效果较为理想。     

基于VaR-GARCH模型的中国商品期货动态保证金水平的设定

本文通过对中国大连商品交易所以及上海期货交易所黄豆、铜两种主要期货品种收益率的研究,论证了其时间序列存在ARCH效应;借助VaR风险价值法,运用GARCH模型,建立VaR-GARCH模型,计算出两合约每个交易日VaR风险预测值;计算黄豆0809合约和铜0809合约日涨跌停的绝对值,将其与VaR预测值进行比较,设定保证金水平,确定保证金率并与交易所规定的保证金率进行比较,为交易所制定更合理的期货保证金率提供依据。     

股市收益波动的非对称性研究

随着我国股票市场的逐步发展及证券市场监管体制的逐步完善,股票市场收益波动所呈现的非对称性效应是否会发生逆转引人关注。本文运用不同的GARCH类条件异方差模型(EGARCH、TARCH、PARCH以及非对称CARCH模型),选取1998年1月5日至2006年11月6日上证综指的日收盘价格指数数据,分析和验证了股票收益波动的非对称性效应。结果表明,上海股市股票收益波动仍然具有非对称性,即利空消息对收益波动的冲击效应大于同等程度的利好消息,从而验证了非对称效应的不可逆转性。     

GARCH模型下的美式期权模拟定价——来自中国权证市场的经验

分别运用GBM模型和GARCH模型对我国市场上的股票价格进行参数估计,并使用最小二乘蒙特卡罗模拟方法对我国具有百慕大性质的权证进行蒙特卡罗模拟定价,发现GARCH模型的定价效果明显优于GBM假设下的定价,虚值程度越高的权证,定价误差越大。定价误差的对数与上证综合指数的对数之间存在明显的协整关系,权证没有卖空机制,使得套利无法实现,投机气氛较浓,是我国权证的市场价格明显高估的重要原因。实证对GARCH条件下的定价误差更加具有解释力。     

基于GARCH模型对人民币汇率波动的实证研究

本文建立了人民币汇率波动的GARCH族模型,实证检验了汇率制度改革以来人民币汇率波动的特征。结果显示,2005年7月21日至今,人民币的汇率收益具有显著的左厚尾特征;汇率的波动并不服从正态分布,具有集聚性;并且人民币的波动具有记忆性,随时间变化不会衰减;通过TGARCH模型的实证结果显示,人民币的汇率波动存在一定的杠杆效应,人民币汇率还不具备浮动汇率的特征。根据分析,本文认为杠杆效应的存在源自于汇率升值的单向预期,给出以下建议：通过有节奏的汇率市场化改革,以及改善国际收支双顺差,减少对升值的单向预期;央行对汇率的波动适当控制;培育人民币汇率衍生市场,增加进出口贸易企业规避汇率风险的金融产品;增加对附加值高的出口企业非汇率贸易政策支持。     

汇率波动与贸易流量:来自中国的经验数据

以GARCH模型（generalized autoregressive conditional heteroscedasticity model）导出的实际有效汇率指数的条件方差代表汇率变动，尝试用实际有效汇率指数的变化研究汇率波动对我国贸易流量的动态影响，并应用协整理论和向量误差修正模型（VEC），就汇率波动对进出口贸易流量的影响进行实证分析。实证研究结果表明：长期中，持续的汇率波动对中国进口具有积极作用，而对出口则有显著的负面影响，说明汇率风险的加大会影响风险收益的增加，并通过国际贸易商品价格的变动，影响正常的国际贸易；短期内，进出口贸易流量受汇率波动的影响较小，出口主要受国外收入与实际汇率水平影响，进口主要受国内收入影响。     

我国沪市股价波动性的实证分析

本文以上证综指日收益率作为研究对象,从实证的角度采用GARCH模型族研究了1999年至2005年间我国股价波动的统计特征.实证结果表明:我国股价波动具有尖峰厚尾特征、异方差性和波动的非对称性;验证了高风险对应于高收益的投资组合理论.     

M1 demand and volatility

The reasons for the instability within the M1 market during the 1970s and 1980s have been attributed to an actual breakdown in the M1 market, multicollinearity, or an inappropriate specification. The present paper offers a more general reason: increased volatility within the money demand determinants produced an econometric environment which made estimation of the relationship problematic. In order to investigate this possibility, the traditional demand vector is re-estimated with additional generalized autoregressive conditional heteroskedasticity representation for income, inflation, and interest rate volatility as conditioning variables. The rolling regression results highlight a significant stability within the M1 demand vector and its long- and short-run parameters. I wish to thank, without implicating, two anonymous referees for helpful comments and suggestions     

Volatility and market structure

This study examines volatility within three related intra-day series – transaction returns, quote midpoint returns, and limit order book midpoint returns – for a set of NYSE-listed stocks. We document statistically significant GARCH effects both overall and surrounding earnings announcements in all three series for the majority of stocks in the sample. We then compare the extent of volatility clustering among the series. In addition, the relation between volatility and market structure is examined via a set of cross-sectional regressions, and relations among the series over time are studied in a vector autoregressive framework.     

The effect of time-varying covariances on asset risk premia

In this article we examine an intertemporal capital asset pricing model (CAPM) that allows for time-varying conditional covariances that are assumed to follow a multivariate integrated generalized autoregressive conditional heteroscedastic (IGARCH) process. The resulting pricing equation includes idiosyncratic risk premia in addition to the usual market beta. Empirical analysis based on ten size and ten industry portfolios reveals significant idiosyncratic premia for most portfolios. Overall, we reject the static CAPM in favor of the intertemporal CAPM.