基于四次幂差修正HAR模型的股指期货波动率预测

股指期货波动率建模与预测是揭示其波动运行规律和市场风险是重要途径。本文基于跳跃、好坏波动率与符号跳跃建立四组HAR模型,提出单级纠偏HARQ类模型和多级纠偏HARQF类模型,实证研究揭示股指期货波动运行规律,并采用MCS检验来评估模型优劣。HAR建模考察连续与跳跃波动、好与坏波动率的两种已实现波动分解。为了降低波动率估计偏差,基于最小化MSE准则确定最优抽样频率,利用已实现核修正的ADS检测法识别跳跃,采用已实现核估计修正好坏波动率与符号跳跃。基于沪深300股指期货的实证研究表明：连续波动比跳跃波动对未来已实现波动贡献更大;好坏波动率具有不对称波动冲击,而符号跳跃对未来波动具有负向冲击;好坏波动率分解优于连续与跳跃波动分解;中位数已实现四次幂差能够显著提升HAR类模型的样本内外预测能力;与样本内预测相反,样本外预测中单级纠偏HARQ类模型优于多级纠偏HARQF类模型;MCS检验得出HARQ-RV-SJ模型表现最佳。研究结论与启示对认识股指期货波动规律和市场风险具有意义。     

嵌入GARCH波动率估计的B1ack-Litterman投资组合模型

在B1ack-Litterman投资组合模型中,为了更有效地估计风险资产的期望收益和波动率,引入了投资者的主观观点,这种处理确实能提高均值-方差投资组合模型的性能。但是在实践中,如何度量投资者观点成了另一个难题。为了克服这一困难,我们将GARCH波动率估计嵌入到B1ack-Litterman模型中,通过使用GARCH模型的预测能力来替代投资者主观观点,从而获得一个新的投资决策模型。作为应用,分别考虑了国内外真实市场数据测试情形,通过实证结果发现,嵌入了GARCH波动率估计后,Black-Litterman模型的性能可进一步得到很好提高,样本外平均收益率、波动率和夏普比等指标,均要好于一些传统模型。     

机构投资者、高管变更与股价波动

基于2004年—2012年的中国上市公司数据,从高管变更的视角研究了机构投资者的公司治理效应,并尝试从投资者行为的角度解析高管变更事件对公司股价波动的影响机理.实证结果表明:第一,对于那些业绩亏损的公司,机构投资者的撤离能够形成较强的外部压力,迫使当任高管离职.而对于那些业绩仍然维持盈利的公司,机构投资者的施压作用则不明显.总体上,业绩好坏始终是公司高管被迫离任的主要原因,而机构投资者则是通过"用脚投票"的方式起到了推波助澜的作用.第二,高管强制变更会被机构投资者解读为负面信号,新高管的上任并不会挽回机构投资者的持股信心.第三,噪音交易者(中小个体投资者)倾向于将亏损公司的高管变更事件解读为利好消息,其踊跃买入行为则构成了公司股价在高管强制变更后发生波动加剧现象的直接原因.     

情绪指数与市场收益: 纳入中国波指( iVX) 的分析

本文在 Baker 和 Wurgler( 2006, 2007) 研究框架的基础上, 将中国波动率指数( iVX) 作为一个新的情绪代理变量,结合传统的封闭式基金折价率、股票换手率和 IPOs 的数量等变量,运用主成分分析法构建了中国 A 股市场的情绪指数, 并分析了情绪指数与市场收益之间的依赖关系和预测效果． 研究发现,情绪指数与市场收益呈负向关系． 然而,其当期依赖关系并不显著,而情绪指数对其后第三周的市场收益有较显著的负向预测关系． 中国波指的加入能够明显提高这种预测效果; 相反,IPOs 的数量则并不是一个有效的情绪代理变量． 此外, 采用前两个主成分的加权并不比仅采用第一主成分构建情绪指数在市场收益预测方面表现得更好, 甚至表现得更差． 最后,分析了情绪效应的不对称性,发现正情绪指数对未来收益的影响要远远大于负情绪指数．     

The local fractional bootstrap

We introduce a bootstrap procedure for high‐frequency statistics of Brownian semistationary processes. More specifically, we focus on a hypothesis test on the roughness of sample paths of Brownian semistationary processes, which uses an estimator based on a ratio of realized power variations. Our new resampling method, the local fractional bootstrap, relies on simulating an auxiliary fractional Brownian motion that mimics the fine properties of high‐frequency differences of the Brownian semistationary process under the null hypothesis. We prove the first‐order validity of the bootstrap method, and in simulations, we observe that the bootstrap‐based hypothesis test provides considerable finite‐sample improvements over an existing test that is based on a central limit theorem. This is important when studying the roughness properties of time series data. We illustrate this by applying the bootstrap method to two empirical data sets: We assess the roughness of a time series of high‐frequency asset prices and we test the validity of Kolmogorov's scaling law in atmospheric turbulence data.     

Testing explosive bubbles with time-varying volatility

This article considers the problem of testing for an explosive bubble in financial data in the presence of time-varying volatility. We propose a weighted least squares-based variant of the Phillips et al.) test for explosive autoregressive behavior. We find that such an approach has appealing asymptotic power properties, with the potential to deliver substantially greater power than the established OLS-based approach for many volatility and bubble settings. Given that the OLS-based test can outperform the weighted least squares-based test for other volatility and bubble specifications, we also suggest a union of rejections procedure that succeeds in capturing the better power available from the two constituent tests for a given alternative. Our approach involves a nonparametric kernel-based volatility function estimator for computation of the weighted least squares-based statistic, together with the use of a wild bootstrap procedure applied jointly to both individual tests, delivering a powerful testing procedure that is asymptotically size-robust to a wide range of time-varying volatility specifications.     

The generalized Gudermannian distribution: inference and volatility modelling

In this paper, we introduce a new distribution, called generalized Gudermannian (GG) distribution, and its skew extension for GARCH models in modelling daily Value-at-Risk (VaR). Basic structural properties of the proposed distribution are obtained including probability density and cumulative distribution functions, moments, and stochastic representation. The maximum likelihood method is used to estimate unknown parameters of the proposed model and finite sample performance of maximum likelihood estimates are evaluated by means of Monte-Carlo simulation study. The real data application on Nikkei 225 index is given to demonstrate the performance of GARCH model specified under skew extension of GG innovation distribution against normal, Student's-t, skew normal and generalized error and skew generalized error distributions in terms of the accuracy of VaR forecasts. The empirical results show that the GARCH model with GG innovation distribution produces the most accurate VaR forecasts for all confidence levels.     

Remittance volatility and financial sector development in sub-Saharan African countries

In this study we examine the relationship between remittances, remittance volatility and financial sector development in sub-Saharan Africa using a two-step system GMM estimator over the period 2002–2014. Separately focussing on banking sector- and stock market development, our study distinguishes between the effect of remittances and remittance volatility on financial sector depth and financial sector efficiency. The results indicate remittances act as a substitute for the formal banking system in sub-Saharan African countries. We further provide evidence that remittance volatility is detrimental to both banking sector depth and efficiency. No evidence is found that remittance volatility is related to stock market development. A policy implication from our study is that sub-Saharan African countries should have measures in place to monitor the predictability of remittances while the cost of remittance transfer needs to be investigated.     

Background risk and nonprofit endowment portfolio volatility

This article focuses on endowed operating public charities that receive income not only from sources such as donations, grants, and service fees but also from endowment portfolios. Using the Form 990 data between 2009 and 2016, this study examines if the risk from nonendowment income sources, namely background risk, is relevant to endowment portfolio volatility, and if there are any differences across four types of nonprofits where endowment assets are the most concentrated, including museums, universities and colleges, K‐12 schools, and hospitals. The results show that the association between background risk and endowment portfolio volatility is significant and negative for universities; however, it is either nonsignificant or significantly positive for other types of organizations. This study extends research on university endowments to other types of endowed nonprofits. The findings imply different endowment objectives and reflect different asset allocation strategies across types of organizations.     

国际间资本市场联动效应的理论解读与实证研究

通过基于投资套利视角的理论模型阐述了资本市场之间产生联动溢出效应的作用机制,利用考虑股市涨跌非对称效应的均值波动模型考察了中美两国股票市场之间的联动效应,包括均值溢出效应、ARCH/GARCH型波动溢出效应和股市涨跌产生的非对称溢出效应。结果显示,中美股票市场在均值层面不存在显著的溢出关系,但在波动层面则具有多重显著的溢出关系。同时,中美两国股票市场上升或下降对中国股票市场的波动性和中美股票市场联动关系也有着不同影响。