我国SHIBOR市场利率波动的实证研究

以SHIBOR(上海银行间同业拆借利率)市场利率为数据样本,构建了带有混合高斯分布的Markov状态转换GARCH(1,1)模型,分析表明我国SHIBOR市场利率变动的时间序列具有尖峰、后尾和波动聚集等现象。对比普通的GARCH(1,1)模型、Markov状态转换GARCH(1,1)模型和带混合高斯分布的Markov状态转换GARCH(1,1)模型的贝叶斯后验参数估计值发现,第三种模型对我国SHIBOR市场利率波动的拟合效果最好,普通GARCH(1,1)模型的拟合效果最差。研究说明我国SHOBOR市场发展正在逐步健全起来。     

变结构门限t-GARCH模型及其伪持续性研究

为了反映金融时间序列的波动集聚性、非对称性、厚尾性以及在实证研究中表现出的伪持续性，本文结合门限GARCH模型以及变结构的方法提出了变结构门限t—GARCH模型。首先用Monte Carlo模拟的方法考虑了变结构GARCH模型中存在的伪持续性问题；其次针对金融时间序列非对称性、厚尾性以及强持续性的特点提出了变结构门限t—GARCH模型，总结了关于变结构点检验的几个主要方法；最后用该模型来拟合沪市和深市两个股市的周收益率序列，得到了比GARCH模型更好的拟合结果。     

基于ARIMA模型和GARCH模型的美元指数波动性分析

美元指数在金融危机前后出现了耐人寻味的变化,其波动影响着国际经济、政治格局。本文运用自回归单整移动平均时间序列(ARIMA模型)和广义自回归条件异方差时间序列(GARCH模型)的方法分析美元指数,采集大量历史样本数据,对其波动特性进行实证研究。运用ARIMA模型对未来短期美元指数走向进行预测,表明美元指数的波动有一定的规律。同时,对美元指数建立用于描述大量金融时间序列的GARCH(1,1)模型,通过模型的定阶、检验、预测发现GARCH模型有较好的预测较长期整体走势的能力。     

基于极值分布理论的VaR与ES度量

本文应用极值分布理论对金融收益序列的尾部进行估计,计算收益序列的在险价值VaR和预期不足ES来度量市场风险。通过伪最大似然估计方法估计的GARCH模型对收益数据进行拟合,应用极值理论中的GPD对新息分布的尾部建模,得到了基于尾部估计产生收益序列的VaR和ES值。采用上证指数日对数收益数据为样本,得到了度量条件极值和无条件极值下VaR和ES的结果。实证研究表明:在置信水平很高(如99%)的条件下,采用极值方法度量风险值效果更好。而置信水平在95%下,其他方法和极值方法结合效果会很好。用ES度量风险能够使我们了解不利情况发生时风险的可能情况。     

江苏劳动争议案件数量的灰色预测及启示

以2000～2009年江苏省劳动争议受理案件数据为依据,运用灰色系统理论,建立灰色GM(1,1)主模型和GM(1,1)残差模型,对江苏省劳动争议受理案件时间序列进行了拟合、分析与预测。通过实证分析表明,GM(1,1)残差模型的拟合程度较高,是一种有效的劳动争议数量预测算法,模型预测结果可为劳动关系管理和劳动争议处理提供有利的理论依据。     

改进灰色预测模型的研究—以安徽省物流量预测为例

以GM(1,1)模型为基础,提出对原始数据取自然对数以提高原始数据的光滑度,同时,与优化背景值相结合,对GM(1,1)模型进行改进。利用安徽物流量的实际数据,比较原始GM(1,1)模型、基于优化背景值的GM(1,1)模型和新的GM(1,1)模型各自的拟合精度,发现新的GM(1,1)模型精度最好,有一定的实用性。     

改进灰色预测模型的研究——以安徽省物流量预测为例

以GM(1,1)模型为基础,提出对原始数据取自然对数以提高原始数据的光滑度,同时,与优化背景值相结合,对GM(1,1)模型进行改进.利用安徽物流量的实际数据,比较原始GM(1,1)模型、基于优化背景值的GM(1,1)模型和新的GM(1,1)模型各自的拟合精度,发现新的GM(1,1)模型精度最好,有一定的实用性.     

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。     

深证成指周收益率波动及预测实证研究——基于ARCH模型

文章以1996—2012年深证成指(399001)周收盘价为对象,就我国股市波动情况进行实证研究。研究结果表明,我国深成指周收益率序列不存在自相关;对比GARCH(1,1)模型和GARCH-M(1,1)模型,不含常数项的GARCH-M(1,1)模型优于捕捉深成指周收益率的波动性。文章最后对其波动性进行了预测分析。     

基于GARCH模型的股票收益率分析及预测

GARCH模型是对金融数据波动性进行描述的有效方法,它是最常用、最便捷的异方差序列拟合模型。资产收益率是金融数据分析常用的指标,比价格序列更易处理且更有研究意义。本文采用R语言,对2009年1月6日—2019年5月20日沪深300指数的日收盘价进行预处理,将其转化为平稳的收益率序列,检验其ARCH效应,建立GARCH模型以及标准化残差分析,最后对收益率和股票价格进行预测,预测的结果能为投资者进行决策提供一定的参考。     

基于不同分布假设GARCH模型对上证指数波动性预测能力的比较研究

本文在四种不同的分布假设(Normal,Student-t,GED和SkewedStudent-t)下,对上证指数波动性进行了GARCH(1,1)模型预测能力实证比较研究,目的在于揭示分布假设对GARCH模型预测能力的影响。研究结果表明,使用厚尾分布假设(Student-t,GED)提高了模型的预测绩效。但引入偏斜student-t分布并未能进一步提高模型预测能力。     

Evaluating interval forecasts of high‐frequency financial data

A number of methods of evaluating the validity of interval forecasts of financial data are analysed, and illustrated using intraday FTSE100 index futures returns. Some existing interval forecast evaluation techniques, such as the Markov chain approach of Christoffersen ( 1998 ), are shown to be inappropriate in the presence of periodic heteroscedasticity. Instead, we consider a regression‐based test, and a modified version of Christoffersen's Markov chain test for independence, and analyse their properties when the financial time series exhibit periodic volatility. These approaches lead to different conclusions when interval forecasts of FTSE100 index futures returns generated by various GARCH(1,1) and periodic GARCH(1,1) models are evaluated. Copyright © 2003 John Wiley & Sons, Ltd.     

基于G(1,1)模型的2010年中国价格指数预测分析

近期,周小川表示,中国的通货膨胀已经显现。管理好通货膨胀成为今年宏观调控新重点。为研究2010年通货膨胀变化趋势,为宏观经济决策提供依据,文章以1994～2009年历年各月原材料、燃料、动力购进价格指数、工业品出厂价格指数、商品零售价格指数、居民消费价格指数为基础,运用GM(1,1)模型,对中国2010年上述四种价格指数进行了预测。     

Normal mixture GARCH(1,1): applications to exchange rate modelling

Some recent specifications for GARCH error processes explicitly assume a conditional variance that is generated by a mixture of normal components, albeit with some parameter restrictions. This paper analyses the general normal mixture GARCH(1,1) model which can capture time variation in both conditional skewness and kurtosis. A main focus of the paper is to provide evidence that, for modelling exchange rates, generalized two‐component normal mixture GARCH(1,1) models perform better than those with three or more components, and better than symmetric and skewed Student's t‐GARCH models. In addition to the extensive empirical results based on simulation and on historical data on three US dollar foreign exchange rates (British pound, euro and Japanese yen), we derive: expressions for the conditional and unconditional moments of all models; parameter conditions to ensure that the second and fourth conditional and unconditional moments are positive and finite; and analytic derivatives for the maximum likelihood estimation of the model parameters and standard errors of the estimates. Copyright © 2006 John Wiley & Sons, Ltd.     

An econometric analysis of asymmetric volatility: Theory and application to patents

The purpose in registering patents is to protect the intellectual property of the rightful owners. Deterministic and stochastic trends in registered patents can be used to describe a country's technological capabilities and act as a proxy for innovation. This paper presents an econometric analysis of the symmetric and asymmetric volatility of the patent share, which is based on the number of registered patents for the top 12 foreign patenting countries in the USA. International rankings based on the number of foreign US patents, patent intensity (or patents per capita), patent share, the rate of assigned patents for commercial exploitation, and average rank scores, are given for the top 12 foreign countries. Monthly time series data from the United States Patent and Trademark Office for January 1975 to December 1998 are used to estimate symmetric and asymmetric models of the time-varying volatility of the patent share, namely US patents registered by each of the top 12 foreign countries relative to total US patents. A weak sufficient condition for the consistency and asymptotic normality of the quasi-maximum likelihood estimator (QMLE) of the univariate GJR(1,1) model is established under non-normality of the conditional shocks. The empirical results provide a diagnostic validation of the regularity conditions underlying the GJR(1,1) model, specifically the log-moment condition for consistency and asymptotic normality of the QMLE, and the computationally more straightforward but stronger second and fourth moment conditions. Of the symmetric and asymmetric models estimated, AR(1)–EGARCH(1,1) is found to be suitable for most countries, while AR(1)–GARCH(1,1) and AR(1)–GJR(1,1) also provide useful insights. Non-nested procedures are developed to test AR(1)–GARCH(1,1) versus AR(1)–EGARCH(1,1), and AR(1)–GJR(1,1) versus AR(1)–EGARCH(1,1).     

Modelling Regime‐Specific Stock Price Volatility*

Single‐state generalized autoregressive conditional heteroscedasticity (GARCH) models identify only one mechanism governing the response of volatility to market shocks, and the conditional higher moments are constant, unless modelled explicitly. So they neither capture state‐dependent behaviour of volatility nor explain why the equity index skew persists into long‐dated options. Markov switching (MS) GARCH models specify several volatility states with endogenous conditional skewness and kurtosis; of these the simplest to estimate is normal mixture (NM) GARCH, which has constant state probabilities. We introduce a state‐dependent leverage effect to NM‐GARCH and thereby explain the observed characteristics of equity index returns and implied volatility skews, without resorting to time‐varying volatility risk premia. An empirical study on European equity indices identifies two‐state asymmetric NM‐GARCH as the best fit of the 15 models considered. During stable markets volatility behaviour is broadly similar across all indices, but the crash probability and the behaviour of returns and volatility during a crash depends on the index. The volatility mean‐reversion and leverage effects during crash markets are quite different from those in the stable regime.     

Non-stationarity in garch models: A bayesian analysis

First, the non-stationarity properties of the conditional variances in the GARCH(1, 1) model are analysed using the concept of infinite persistence of shocks. Given a time sequence of probabilities for increasing/decreasing conditional variances, a theoretical formula for quasi-strict non-stationarity is defined. The resulting conditions for the GARCH(1,1) model are shown to differ from the weak stationarity conditions mainly used in the literature. Bayesian statistical analysis using Monte Carlo integration is applied to analyse both stationarity concepts for the conditional variances of the US 3-month treasury bill rate. Interest rates are known for their weakly non-stationary conditional variances but, using a quasi-strict stationarity measure, it is shown that the conditional variances are likely to be stationary. Second, the level of the treasury bill rate is analysed for non-stationarity using Bayesian unit root methods. The disturbances of the GARCH model for the treasury bill rate are t-distributed. It is shown that the unit root parameter is negatively correlated with the degrees-of-freedom parameter. Imposing normally distributed disturbances leads therefore to underestimation of the non-stationarity in the level of the treasury bill rate.     

Temporal aggregation of multivariate GARCH processes

This paper derives results for the temporal aggregation of multivariate GARCH(1,1) processes in the general vector specification. It is shown that the class of weak multivariate GARCH(1,1) processes is closed under temporal aggregation. Fourth moment characteristics turn out to be crucial for the low frequency dynamics for both stock and flow variables. In some aspects, the aggregation characteristics of multivariate GARCH processes are shown to be different from those of vector autoregressive moving average processes. A numerical example illustrates some of the results.     

A semiparametric GARCH model for foreign exchange volatility

A semiparametric extension of the GJR model (Glosten et al., 1993. Journal of Finance 48, 1779–1801) is proposed for the volatility of foreign exchange returns. Under reasonable assumptions, asymptotic normal distributions are established for the estimators of the model, corroborated by simulation results. When applied to the Deutsche Mark/US Dollar and the Deutsche Mark/British Pound daily returns data, the semiparametric volatility model outperforms the GJR model as well as the more commonly used GARCH(1,1) model in terms of goodness-of-fit, and forecasting, by correcting overgrowth in volatility.     

GM(1,1)预测遗传算法及其应用

本文概括了对遗传算法进行优化的主要策略和采用GM(1,1)模型进行灰色预测的基本方法;提出了采用GM(1,1)模型对遗传进化过程中的染色体适应度值进行预测,以该预测值作为选择操作依据之一的遗传算法改进策略;通过一个资源分配问题的实例,验证了该改进策略的有效性。实验证明,使用该方法进行较少代数的遗传进化即可得到较为充分优化的解决方案。