Multivariate GARCH Models with Correlation Clustering

A new clustered correlation multivariate generalized autoregressive conditional heteroskedasticity (CC‐MGARCH) model that allows conditional correlations to form clusters is proposed. This model generalizes the time‐varying correlation structure of Tse and Tsui (2002, Journal of Business and Economic Statistics 20 : 351–361) by classifying the correlations among the series into groups. To estimate the proposed model, Markov chain Monte Carlo methods are adopted. Two efficient sampling schemes for drawing discrete indicators are also developed. Simulations show that these efficient sampling schemes can lead to substantial savings in computation time in Monte Carlo procedures involving discrete indicators. Empirical examples using stock market and exchange rate data are presented in which two‐cluster and three‐cluster models are selected using posterior probabilities. This implies that the conditional correlation equation is likely to be governed by more than one set of decaying parameters. Copyright © 2011 John Wiley & Sons, Ltd.     

汇率波动对中国出口增加值的影响分析

在全球价值链迅速发展和人民币汇率改革不断推进的背景下,研究汇率波动对出口中国内增加值的影响具有重要的现实意义.本文首先利用计量经济模型估计汇率波动对出口和部门进口的影响,进而利用反映加工贸易的非竞争型投入产出模型测算出口中国内增加值所受的影响.结果表明,汇率波动对出口增加值的影响比对出口额的影响更大,除了影响出口中的直接增加值之外,汇率波动还会影响进口品和国内品之间的替代,进而影响国内品中间投入结构和出口中的间接增加值.分贸易方式来看,汇率波动对一般贸易出口的影响更大,加工贸易在有限程度上缓和了汇率波动对总出口增加值的影响.对部门出口增加值而言,进口需求价格弹性较大的部门所受影响较大,加工出口比重高的部门所受影响较小.     

The Information Content of Intraday Implied Volatility for Volatility Forecasting

This study examines the intraday S&P 500 implied volatility index (VIX) to determine when the index contains the most information for volatility forecasting. The findings indicate that, in general, VIX levels around noon are most informative for predicting realized volatility. We posit that the VIX performs better during this time period because trading motivation around noon is less complex, and therefore trades contain more information on the market expectation of future volatility. Further investigation on the 2008 financial crisis period suggests that market participants become more cautious, and thus the forecasting performance is sustained until the market's close. Copyright © 2015 John Wiley & Sons, Ltd.     

Realized Volatility Forecast of Stock Index Under Structural Breaks

We investigate the realized volatility forecast of stock indices under the structural breaks. We utilize a pure multiple mean break model to identify the possibility of structural breaks in the daily realized volatility series by employing the intraday high‐frequency data of the Shanghai Stock Exchange Composite Index and the five sectoral stock indices in Chinese stock markets for the period 4 January 2000 to 30 December 2011. We then conduct both in‐sample tests and out‐of‐sample forecasts to examine the effects of structural breaks on the performance of ARFIMAX‐FIGARCH models for the realized volatility forecast by utilizing a variety of estimation window sizes designed to accommodate potential structural breaks. The results of the in‐sample tests show that there are multiple breaks in all realized volatility series. The results of the out‐of‐sample point forecasts indicate that the combination forecasts with time‐varying weights across individual forecast models estimated with different estimation windows perform well. In particular, nonlinear combination forecasts with the weights chosen based on a non‐parametric kernel regression and linear combination forecasts with the weights chosen based on the non‐negative restricted least squares and Schwarz information criterion appear to be the most accurate methods in point forecasting for realized volatility under structural breaks. We also conduct an interval forecast of the realized volatility for the combination approaches, and find that the interval forecast for nonlinear combination approaches with the weights chosen according to a non‐parametric kernel regression performs best among the competing models. Copyright © 2014 John Wiley & Sons, Ltd.     

The Forecasting Efficacy of Risk‐Neutral Moments for Crude Oil Volatility

This paper examines the information content of implied volatility for crude oil options as it relates to future realized volatility. Using data for the period 1996 to 2011 we find that implied volatility is an effective predictor of the month‐ahead realized volatility. We show that implied volatility subsumes the information content of contemporaneous volatility, and it contains incremental information on future volatility after controlling for contemporaneous volatility. Furthermore, incorporating risk‐neutral skewness, and especially kurtosis, improves the forecasting of realized volatility. Overall, the association between implied volatility and month‐ahead realized volatility is consistent with evidence documented for other asset classes, leading us to conclude that implied volatility serves as a reasonable proxy for expected volatility. Copyright © 2015 John Wiley & Sons, Ltd.     

Does Information Help Intra‐Day Volatility Forecasts?

While much research related to forecasting return volatility does so in a univariate setting, this paper includes proxies for information flows to forecast intra‐day volatility for the IBEX 35 futures market. The belief is that volume or the number of transactions conveys important information about the market that may be useful in forecasting. Our results suggest that augmenting a variety of GARCH‐type models with these proxies lead to improved forecasts across a range of intra‐day frequencies. Furthermore, our results present an interesting picture whereby the PARCH model generally performs well at the highest frequencies and shorter forecasting horizons, whereas the component model performs well at lower frequencies and longer forecast horizons. Both models attempt to capture long memory; the PARCH model allows for exponential decay in the autocorrelation function, while the component model captures trend volatility, which dominates over a longer horizon. These characteristics are likely to explain the success of each model. Copyright © 2013 John Wiley & Sons, Ltd.     

高频数据下基于LSTM的协方差矩阵预测模型

协方差矩阵的建模与预测,对于金融风险管理、投资组合管理等至关重要。 针对时间序列模型 对高维变量预测精度较低的问题,利用长短记忆神经网络模型(LSTM),提出了基于深度学习的高频数据已 实现协方差矩阵预测模型。 利用金融高频数据得到已实现协方差矩阵,对其进行 DRD 分解,针对相关系数 矩阵 R 进行向量化处理,利用向量异质自回归模型(HAR)预测已实现相关系数矩阵 R;针对已实现波动率 矩阵 D,利用半协方差(semi covariance)思想,结合 LSTM 模型,得到已实现波动率矩阵 D 的深度学习预测模 型,构建了 LSTM-SDRD-HAR 已实现协方差矩阵动态预测模型。 LSTM 模型和 HAR 模型能捕捉实际数据 的长期记忆性,半协方差有利于捕捉金融数据的杠杆性。 实证分析表明:相较于传统向量 HAR 已实现协方 差矩阵预测模型,LSTM-SDRD-HAR 预测已实现协方差矩阵更为准确,基于 LSTM-SDRD-HAR 预测已实现 协方差矩阵构造的有效前沿组合投资效果更佳。     

铸锭扩径对TC4锻坯成分的均匀性及波动性的影响

选择5个批次的TC4扩径铸锭，分别沿锻坯的长度及宽度方向对其主要成分分布的均匀性及波动性进行探究。结果表明：TC4锻坯沿两个方向的主要元素分布基本都居于预期目标中部，但沿长度方向的主要元素分布均匀性不及沿宽度方向的。Fe、Al、O、V 4种元素的均匀性依次递减，其中O的分布存在头部超预期目标上限的情况。利用方差分别计算沿两个方向的主要元素波动比，发现其按照Al、V、Fe、O的顺序递减，且前两者的波动比在同一数量级(10^-2)并远大于后两者的(10^-4)。沿宽度方向的主要元素的波动比要略低于沿长度方向的，其中O沿两个方向的波动比最小，稳定性最好。     

基于非对称VECM BEKK GARCH模型的国内外原油价格关联性分析

 构建非对称VECM BEKK GARCH模型，分别选取大庆原油和WTI（美国西德克萨斯原油）作为国内外原油现货价格的代表，研究分析2000年1月至2020年8月间国内外原油价格间的关联性。研究结果表明:WTI原油价格对大庆原油价格存在显著的均值溢出和波动溢出效应，而大庆原油价格对WTI原油价格只存在显著的波动溢出效应；二者间存在双向的非对称性的波动溢出效应，即大庆原油价格会随着WTI原油价格的变动而呈现出时变性和持续性变化的特点，而WTI原油价格变动呈现出持续性时，大庆原油价格也会随之产生变化，总体上，负向冲击使大庆原油价格波动的幅度大于WTI原油价格波动的幅度。     

政府收支对我国经济波动的平抑效果

 采用构建的向量自回归模型（VAR模型）和向量误差修正模型（VEC模型），基于我国1985—2019年度经济发展相关数据的研究发现，政府支出冲击能在一定程度上提高实际产出和私人消费水平，对经济具有显著的促进作用；政府支出冲击会对私人投资造成短期挤出效应和长期挤入效应；从长期来看，政府收入冲击会导致实际产出水平的下降，政府收入冲击会造成私人消费和私人投资的剧烈波动。增强政策的正向效应，需要明确宏观调控中财政政策的目标，重视政府支出的度以及政府收入对私人消费和私人投资的影响，严格把控财政政策效应的滞后期。