证券市场行业间的信息流传导效应——基于沪市A股市场MGARCH波动模型的实证研究

同一时点证券市场行业间的收益率波动传导会对投资者的证券组合产生重要影响,因此,本文选用1997年至2008年沪市A股行业的日收益率数据,利用MGARCH模型对中国证券市场行业指数间的波动传导关系进行了实证研究。研究结果显示:股票市场行业间日收盘指数收益率波动存在显著的行业传导效应,而且传导可能会存在持续性。投资者可以依据股票市场行业间的波动规律,实施短期的行业间套期保值策略。     

基于CARR模型和GARCH模型的股市波动性预测研究

金融资产波动性建模和预测是金融理论与实践中的一个重要课题,已经有了许多建模与预测方法。本文利用我国股市的高频数据进行实证研究,分别运用CARR模型和GARCH模型进行波动性预测,进而对两个模型的预测能力进行对比,结果表明:CARR模型在波动性预测方面比GARCH模型的效果更好。     

企业债券市场波动性研究——基于ARMA-GARCH模型的实证分析

基于交易所企债指数,利用ARMA-GARCH模型分析市场波动性。结果表明ARMA(1,1)-GARCH(1,1)模型可以合理解释企业债券市场波动存在的持续性、丛集性和杠杆效应。场外冲击对条件方差的影响具有持久性,反向冲击比等量的正向冲击会导致更高的下一期的条件方差。EGARCH(1,1)-M模型回归结果表明,市场存在正的风险溢价,但是预期条件波动对预期收益仅有微弱补偿。     

基于非线性混合模型对股市价格预测兼对比分析

为更精确地预测未来走势,提出一种新的预测模型：首先采用HP滤波把时间序列分解为趋势序列和周期序列,然后根据序列建模要求,对趋势序列采用自回归模型（AR）进行拟合和预测,对周期序列采用GARCH模型族进行拟合和预测,最后将趋势序列预测值和周期序列预测值相加,与原序列进行比较,进而选择模型精确度较高的模型.通过实证对比其他模型分析,该模型预测效果较好,可以为金融预测提供帮助.     

标准仓单质押组合的价格风险——基于中国农产品期货规范市场的实证研究

利用基于Copula函数的AR(p)-GARCH(p,q)模型计算的VaR能够对农产品标准仓单的价格风险进行准确度量。对大连商品交易所的典型期货交易品种——黄大豆一号、豆油、豆粕的期货合约日结算价进行了实证研究。研究结果显示:从对价格风险预测的盯市频率来看,时变VaR优于静态VaR,因此重视农产品价格风险的频次预测应替代传统风险判断的单次监测;从对风险因子间相依性结构的刻画来看,基于t-Copula函数计算的VaR优于基于正态Copula函数计算的VaR,因此质押物价格波动间的相关系数是度量组合风险时必须考虑的重要变量。     

基于Swarm的股票市场建模与GARCH效应研究

波动性是金融研究的基础议题之一,文章在多主体模型的基础上对它进行了研究。首先,在Lux模型基础上增加观望者类型,建立改进模型,并在Swarm平台上进行仿真,产生与真实股价走势相似的价格时间序列,随后研究了模型收益率的GARCH效应,发现当市场处于周期形态时,收益GARCH效应显著,当市场处于泡沫均衡态时,收益GARCH效应并不显著。     

A conditionally heteroskedastic independent factor model with an application to financial stock returns

We propose a new conditionally heteroskedastic factor model, the GICA-GARCH model, which combines independent component analysis (ICA) and multivariate GARCH (MGARCH) models. This model assumes that the data are generated by a set of underlying independent components (ICs) that capture the co-movements among the observations, which are assumed to be conditionally heteroskedastic. The GICA-GARCH model separates the estimation of the ICs from their fitting with a univariate ARMA-GARCH model. Here, we will use two ICA approaches to find the ICs: the first estimates the components, maximizing their non-Gaussianity, while the second exploits the temporal structure of the data. After estimating and identifying the common ICs, we fit a univariate GARCH model to each of them in order to estimate their univariate conditional variances. The GICA-GARCH model then provides a new framework for modelling the multivariate conditional heteroskedasticity in which we can explain and forecast the conditional covariances of the observations by modelling the univariate conditional variances of a few common ICs. We report some simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of financial data. Finally, we present an empirical application to the Madrid stock market, where we evaluate the forecasting performances of the GICA-GARCH and two additional factor GARCH models: the orthogonal GARCH and the conditionally uncorrelated components GARCH.     

RISK–RETURN TRADE‐OFF AND BEHAVIOUR OF VOLATILITY ON THE SOUTH AFRICAN STOCK MARKET: EVIDENCE FROM BOTH AGGREGATE AND DISAGGREGATE DATA

The study analyses the nature and behaviour of volatility, the risk–return relationship and the long‐term trend of volatility on the South African equity markets using aggregate level, industrial level and sectoral level daily data for the period 1995‐2009. By employing dummy variables for the Asian and the sub‐prime financial crises and the 11 September political shock, the study further examines whether the long‐term trend of volatility structurally breaks during financial crises and major political shocks. Three time‐varying generalised autoregressive conditional heteroskedasticity models were employed: one of them symmetric, and the other two asymmetric. Each of these models was estimated based on three error distributional assumptions. The findings of the study are as follows: First, volatility is largely persistent and asymmetric. Second, risk at both aggregate and disaggregate level is generally not a priced factor on the South Africa (SA) stock market. Third, the threshold autoregressive conditional heteroscedasticity (TARCH) model under the generalised error distribution is the most appropriate model for conditional volatility of the SA stock market. Fourth, volatility generally increases over time, and its trend structurally breaks during financial crises and major global shocks. The policy and investment implications of the findings are outlined.     

成交量、持仓量对我国大豆类期货价差波动影响的实证分析简

采用GARCH（1,1）模型就成交量、持仓量对大豆类期货价差波动率的影响进行实证分析,结果显示：当期成交量、持仓量对大豆期货价差波动的整体影响是显著的;滞后成交量、持仓量对大豆期货价差波动的整体影响也是显著的;当成交量、持仓量同时进入条件方差方程时,它们对大豆类期货价差波动的影响整体上也是显著的。这一结论揭示了我国大豆期货市场信息传递过程,验证了我国大豆期货市场的信息非有效性,对期货市场投资者以及期货市场监管者具有一定的借鉴意义。     

基于 GARCH 类模型的黑龙江省现代化大农业资金供需预测分析

从黑龙江省现代化大农业发展的资金需求总量、子行业资金需求现状以及资金供给总量、资金供给结构现状进行分析,基于GARCH类模型预测了2013-2017年黑龙江省现代化大农业的资金需求总量和供给总量,分析了资金的缺口,得出加强财政资金对农业投资的引领作用、加强农业投资资金的整合、推进竞争性的农村金融市场发展、增强农业企业自主融资能力,从根本上和实际上解决黑龙江省现代农业投资的资金供需矛盾。