TESTS FOR FRACTIONAL INTEGRATION:A MONTE CARLO INVESTIGATION

Abstract. The performance of the Geweke-Porter-Hudak (GPH) test, the modified rescaled range (MRR) test and two Lagrange multiplier (LM) type tests for fractional integration in small samples is examined using Monte Carlo methods. Both the GPH and MRR tests are found to be robust to moderate autoregressive moving-average components, autoregressive conditional heteroskedasticity effects and shifts in the variance. However, these two tests are sensitive to large autoregressive moving-average components and shifts in the mean. It is also found that the LM tests are sensitive to deviations from the null hypothesis. As an illustration, the GPH test is applied to two economic data series.     

TGARCH模型预测高炉铁水硅质量分数

为了更好地反映高炉铁水硅质量分数序列的高波动特性,利用门限广义自回归条件异方差（TGARCH）模型对硅质量分数序列进行预测.应用Portmantea Q检验、拉格朗日乘子检验以及非对称项系数显著性检验,验证了高炉铁水硅质量分数序列存在异方差性和非对称性.在此基础上将TGARCH模型应用于高炉铁水硅质量分数预测,采用极大似然估计法确定参数,建立TGARCH(1,1,1)预测模型,并采用命中率和误差率2种评价准则对预测结果进行分析.这种方法克服了以往模型没有考虑序列非对称性影响的缺陷,更加适合于高炉铁水硅质量分数的预测.将预测模型应用于包钢6号高炉,取得了较好的预测效果.     

Detection in sea clutter based on nonlinear ARCH model

In this paper, a complex nonlinear autoregressive conditional heteroscedasticity (CNARCH) model is proposed to model sea clutter. For heteroscedastic model, since the likelihood function is not obtained from explicit probability density function (PDF) expression, it is typically referred to as a quasi-likelihood function. The corresponding quasi-maximum likelihood estimation (QMLE) of the model parameters is derived. Furthermore, the corresponding detection algorithm is derived based on this model. We also conduct the simulations of both synthetic and practical data, demonstrate that the proposed model offers higher accuracy in detection, than the linear ARCH model, when used in the sea clutter.     

基于ARCH模型的工业经济预警系统

研究了工业经济预警问题,以四川省工业经济预警系统为例,阐述了基于ARCH预测技术的工业经济预警模型的原理,应用工业经济效益综合指数建立预警体系,并对四川省白酒酿制业的经济预警问题进行了实证研究。     

HYPOTHESIS TESTING FOR ARCH MODELS: A MULTIPLE QUANTILE REGRESSIONS APPROACH

We propose a quantile regression‐based test to detect the presence of autoregressive conditional heteroscedasticity by combining distributional information across multiple quantiles. A chi‐square‐type test statistic based on the weighted average of distinct regression quantile estimators is formed. Unlike the widely used likelihood‐based tests, the proposed test does not make any distributional assumptions on the underlying errors. Monte Carlo simulation studies show that the proposed test outperforms the likelihood‐based tests in several aspects.     

基于QPSO的上证指数ARCH模型

介绍一种利用量子行为粒子群算法(QPSO)建立上证指数收益的 ARCH模型,利用不同的算法精确地估计模型中的参数,验证QPSO算法的优越性。利用得到的估计模型对指数收益进行预测,得到大致跟随指数实际走势的预测值。试验结果表明,QPSO算法比粒子群算法、遗传算法能更好地解决此类问题。     

Bootstrapping a weighted linear estimator of the ARCH parameters

Abstract.  A standard assumption while deriving the asymptotic distribution of the quasi maximum likelihood estimator in ARCH models is that all ARCH parameters must be strictly positive. This assumption is also crucial in deriving the limit distribution of appropriate linear estimators (LE). We propose a weighted linear estimator (WLE) of the ARCH parameters in the classical ARCH model and show that its limit distribution is multivariate normal even when some of the ARCH coefficients are zero. The asymptotic dispersion matrix involves unknown quantities. We consider appropriate bootstrapped version of this WLE and prove that it is asymptotically valid in the sense that the bootstrapped distribution (given the data) is a consistent estimate (in probability) of the distribution of the WLE. Although we do not show theoretically that the bootstrap outperforms the normal approximation, our simulations demonstrate that it yields better approximations than the limiting normal.     

ARCH类模型及其在上证指数收益波动中的应用

介绍ARCH模型、GARCH模型和GARCH—M模型,分析ARCH类模型的特点。然后以上证综指日收益率作为研究对象,采用ARCH类模型结合Eviews统计软件对上证综指日收益率的时变性进行实证分析。实证结果表明,GARCH(1,1)模型能较好地拟合上海股市收益率的波动特征,如波动聚集性、长记忆性等;GARCH(1,1)-M模型也能很好地拟合股市中风险与收益率之间的关系。     

基于ARMA-GARCH模型的超短期风功率预测研究

风功率预测对提高电能质量和电力系统的安全运行具有重要意义。基于时间序列的方法,对内蒙古赤峰地区某风场的风功率数据进行了超短期预测,通过对数据平稳性检验的结果,建立了时间序列的ARMA模型,利用拉格朗日乘子检验的方法,检验ARMA模型具有ARCH效应,并建立适合的ARMA-GARCH模型。结论通过对比ARMA模型,ARMA-ARCH模型和ARMA-GARCH模型的风功率预测精度可知,在解决数据的残差序列异方差函数具有长期相关性时,ARMA-GARCH模型能够有效的提高预测精度。     

Modeling and forecasting crude oil markets using ARCH-type models

This study investigates the time-varying volatility of two major crude oil markets, the West Texas Intermediate (WTI) and Europe Brent. A flexible autoregressive conditional heteroskedasticity (ARCH) model is used to take into account the stylized volatility facts such as clustering volatility, asymmetric news impact and long memory volatility among others. The empirical results indicate that the intensity of long-persistence volatility in the WTI is greater than in the Brent. It is also found that for the WTI, the appreciation and depreciation shocks of the WTI have similar impact on the resulting volatility. However, a leverage effect is found in Brent. Although both the estimation and diagnostic evaluations are in favor of an asymmetric long memory ARCH model, only the WTI models provide superior in the out-of-sample forecasts. On the other hand, from the empirical out-of-sample forecasts, it appears that the simplest parsimonious generalized ARCH provides the best forecasted evaluations for the Brent crude oil data.