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市场流动性与市场预期的动态相关结构研究——基于ARMA-GJR-GARCH-Copula模型分析
市场流动性 市场预期 时变信息熵 ARMA-GJR-GARCH-Copula 动态相关结构
2016/2/29
本文在兼顾"时间尺度"和"价格尺度"双重因素下构建了标准化的市场流动性测度,并利用时变信息熵方法提出了一类市场预期的新指标。将ARMA-GJR-GARCH模型与时变Copula模型相结合分析了市场流动性与市场预期之间的动态相关结构。利用2009年1月~2014年9月中国股市日度数据进行实证分析,结果表明:市场流动性和市场预期存在较明显的持续性和负向"杠杆效应",通过LL、AIC和BIC三种准则比较...
A closed-form estimator for the multivariate GARCH(1,1) model
Multivariate GARCH(1,1) VARMA Temporal Aggregation Es-timation
2013/4/27
We provide a closed-form estimator based on the VARMA representation for the unrestricted multivariate GARCH(1,1). We show that all parameters can be derived using basic linear algebra tools. We show ...
Weighted bootstrap in GARCH models
asymptotic distribution bootstrap confidence region,GARCH model quasi maximum likelihood
2012/11/22
GARCH models are useful tools in the investigation of phenomena, where volatility changes are prominent features, like most financial data. The parameter estimation via quasi maximum likelihood (QMLE)...
近年来，美国金融危机、欧债危机、地震等突发事件不断冲击着我国金融市场，各类资产价格频繁出现大幅跳动，收益风险短期内急剧扩大。鉴于此，本文构建了门限效应下状态变量依赖自回归强度跳跃-GARCH模型（简称TSD-ARJI-GARCH模型）来探讨股票资产价格随时间平滑波动和大幅度跳跃的双重特征。该模型扩展了现有可变强度跳跃-GARCH模型，克服了片面强调内生或外生因素的局限性，既允许跳跃强度受单个资产异...
二元GED-GARCH模型的利率与汇率波动溢出效应研究
利率 汇率 GEDGARCH 溢出效应
2012/9/7
分别运用二元NGARCH模型和二元GEDGARCH模型，对金融危机前后利率和汇率的波动溢出效应进行研究，通过自适应绝对偏差和自适应均方误差的平方根2种标准进行评价。研究认为，二元GEDGARCH预测效果更好，在金融危机前利率与汇率之间存在着由汇率到利率的溢出效应；在金融危机之后，利率与汇率具有双向的波动溢出效应。
GARCH族模型在金融风险的度量中有着广泛的应用。在考虑股市收益率和波动率序列双长记忆性的基础上,基于上证综合指数和深圳成份指数的日收盘价序列,从证券投资风险量化的角度,引入受险值VaR和相对正确符号指标PCS作为模型预测误差衡量指标,比较分析了双长记忆GARCH族模型在不同分布假设情况下的的拟合与预测精度。结果显示:偏t分布能较好描述沪深股市的厚尾特征;在较小的VaR水平下ARFIMA(2,d1...
本文对Van der Weide(2002)的广义正交GARCH模型进行扩展,提出反映金融资产收益波动性特征,具有"杠杆效应"的广义正交GARCH模型。由于这种扩展的广义正交GARCH模型在高维数据中面临参数估计困难,本文从交互信息理论视角研究模型的参数估计问题,在理论上证明基于交互信息最小化的多元GARCH模型参数估计与基于极大似然函数参数估计的联系和区别,并在提出的扩展广义正交GARCH模型框...
Non-Gaussian Quasi Maximum Likelihood Estimation of GARCH Models
Non-Gaussian Quasi Maximum Likelihood Estimation GARCH Models
2010/3/9
The non-Gaussian quasi maximum likelihood estimator is frequently used
in GARCH models with intension to improve the efficiency of the GARCH
parameters. However, the method is usually inconsistent u...
Dependence structure of stable R-GARCH processes
Dependence structure stable R-GARCH processes
2009/9/21
In this pper we invastipte properties of R-GARCH
procmses with ppositivr: steictcrly stab innovations. We derive the uzxconditiond
distributions and analyze the dependcect: structure;. This
at3alys...
DATA-DRIVEN SCORE TEST OF FIT FOR CONDITIONAL DISTRIBUTION IN THE GARCH(l,l) MODEL
GARCH(1,l) model noise distribution efficient score vector score test BIC Schwarz selection rule
2009/9/18
A data-driven score test for a conditional distribution
in the GARCH(1,l) model is proposed. Conditional distribution assumption
is verified by a score test, obtained from nesting the null
density ...
THE MODIFIED TEMPERED STABLE DISTRIBUTION, GARCH MODELS AND OPTION PRICING
Option pricing GARCH process tempered stable distribution volatility clustering
2009/9/18
We introduce a new variant of the tempered stable distribution,
named the modified tempered stable (MTS) distribution and we develop
a GARCH option pricing model with MTS innovations. This model
al...
Comparison of MCMC Methods for Estimating GARCH Models
Bayesian inference GARCH Gibbs sampler Markov chain Monte Carlo Metropolis-Hastings algorithm
2009/3/6
This paper reviews several MCMC methods for estimating the class of ARCH models, and compare performances of them. With respect to the mixing, efficiency and computational requirement of the MCMC, thi...
CHANGES OF STRUCTURE IN FINANCIAL TIME SERIES AND THE GARCH MODEL
integrated periodogram spectral distribution functional central limit theorem Kiefer-Muller process Brownian bridge sample autocorrelation change point GARCH process long range dependence IGARCH non-stationarity
2009/2/26
In this paper we propose a goodness of fit test that checks the resemblance of the spectral density of a GARCH process to that of the log-returns. The asymptotic behavior of the test statistics are gi...
Modelling Stock Returns with AR-GARCH Processes
autoregressive process GARCH and EGARCH models conditional heteroscedastic variance financial log returns
2009/2/23
Financial returns are often modelled as autoregressive time series with random disturbances having conditional heteroscedastic variances, especially with GARCH type processes. GARCH processes have bee...