波动性比率与涨跌幅限制实证研究

从交易机制的角度解释了股市开盘收益率的波动大于收盘收益率的波动的原因。在考虑了涨跌幅限制的存在对股票价格行为的影响后，建立了一个简单的计量模型，提出涨跌停频率的大小与波动率的正相关关系及涨跌停日的日内收益率与次日隔夜收益率的正相关关系的假设，并就中国股市的情况进行了实证分析，得出了与假设一致的结论。这里的研究为“方差比率之谜”提供了新的解释思路。     

中国股市"已实现"波动率的周期性研究

论述证券市场“已实现”波动率的理论,利用深圳成分指数和上海综合指数的5 m in高频数据,对沪深两市的波动周期做了实证分析,通过Fourier谱分析,比较了沪深两市的波动周期,揭示了我国股市的周期波动性特征.目前,我国股票市场的周期性研究多集中在市场指数和收益率的低频数据周期分析,本文的特点是利用高频数据对波动率这一重要参数的周期性进行分析.     

银行间债券市场回购利率的波动性分析

引入了状态转移TGARCH模型(称为SW-TGARCH模型),并对我国银行间债券市场回购利率进行分析.与ARCH类模型相比,SW-TGARCH模型能够更好地描述其波动性,并解决了GARCH模型高持续性及状态转移问题.实证分析表明,造成我国银行间债券市场回购利率波动性状态转移的最主要原因是政策因素.     

波动率持续性基于信息到达过程的成交量解释

综合利用对数周期图方法、Granger因果检验、以及扩展广义自回归条件异方差模型 ,对随机抽取的 10只上证 180成份股进行了实证研究 .得出以下结论 :收益波动率和成交量有相同的长记忆特征 ;信息到达过程是波动率行为的原因之一 ,成交量和波动率展现出的相互因果关系可能是它们同受信息到达过程影响的结果 ;成交量变量能够在有限程度内解释部分波动率的持续性 .     

含有随机波动的风险敏感性控制的最优投资模型

该文主要讨论了由一个无风险资产和N个风险资产组成的投资组合，在波动率受经济因子影响以及不考虑交易费用和消费的情况下，利用风险敏感性动态规划控制在有限期限和无限期限内实现投资利润的最大化．     

空盘量变动对我国期货市场期货价格收益波动性的影响

空盘量是除交易之外刻画期货市场交易活跃程度的另一个重要指标。将空盘量分解为可预期和不可预期两部分,研究空盘量的变动对期货价格收益波动性的影响。实证结果表明,空盘量对期货价格收益的波动性具有负向影响,即总体而言,空盘量的增加对期货价格收益波动性的影响小于空盘量的减少对期货价格收益波动性的影响;并且,不可预期空盘量对期货价格收益的影响比可预期空盘量对价格收益的影响大许多。同时,从信息经济学的角度对实证结果分别进行了解释。     

基于波动率测量误差的波动率预测模型

本文研究了一种基于波动率测量误差的波动率预测模型,并做了非线性扩展,期望改进预测效果.考虑到文献中关于波动率可能长记忆性和非线性并存的观点,本文以具有长记忆特征的HAR(heterogeneous autoregressive)模型为基础,加入波动率测量误差后模型持续性有所提高,结合非线性的时变参数模型则达到结构变化和减弱异方差的效果.本文用2652天的沪深300高频数据计算的已实现极差波动率来验证模型效果.固定参数下,在HAR型模型中加入测量误差作为调节变量可以较显著地改善样本外预测效果.时变参数下,加入测量误差的HARQ型模型预测效果大多优于对应的HAR型模型.时变参数模型总体上可以改善固定参数模型的预测效果,尤其在预测期较长的情况下改善均是显著的.     

人民币汇率市场联动特征分析

融合Copula理论和GARCH有偏T模型,构建了汇率波动的t-Copula-DCC-GARCH-skewT模型,对中、美、欧3大经济体货币汇率USD-CNY和EUR-USD进行实证研究.该模型能够捕捉到人民币汇率之间的动态相关性.相关曲线展现了3大经济体在汇率制度上的阶段性联动特征,表明中、美、欧等经济体货币汇率联动性有利于人民币国际化的汇率制度建设以及世界经济的共同治理和系统性风险防范.     

Forecasting the Daily Time‐Varying Beta of European Banks During the Crisis Period: Comparison Between GARCH Models and the Kalman Filter

This intention of this paper is to empirically forecast the daily betas of a few European banks by means of four generalized autoregressive conditional heteroscedasticity (GARCH) models and the Kalman filter method during the pre‐global financial crisis period and the crisis period. The four GARCH models employed are BEKK GARCH, DCC GARCH, DCC‐MIDAS GARCH and Gaussian‐copula GARCH. The data consist of daily stock prices from 2001 to 2013 from two large banks each from Austria, Belgium, Greece, Holland, Ireland, Italy, Portugal and Spain. We apply the rolling forecasting method and the model confidence sets (MCS) to compare the daily forecasting ability of the five models during one month of the pre‐crisis (January 2007) and the crisis (January 2013) periods. Based on the MCS results, the BEKK proves the best model in the January 2007 period, and the Kalman filter overly outperforms the other models during the January 2013 period. Results have implications regarding the choice of model during different periods by practitioners and academics. Copyright © 2016 John Wiley & Sons, Ltd.     

On the Modelling and Forecasting of Multivariate Realized Volatility: Generalized Heterogeneous Autoregressive (GHAR) Model

Recent multivariate extensions of the popular heterogeneous autoregressive model (HAR) for realized volatility leave substantial information unmodelled in residuals. We propose to employ a system of seemingly unrelated regressions to model and forecast a realized covariance matrix to capture this information. We find that the newly proposed generalized heterogeneous autoregressive (GHAR) model outperforms competing approaches in terms of economic gains, providing better mean–variance trade‐off, while, in terms of statistical precision, GHAR is not substantially dominated by any other model. Our results provide a comprehensive comparison of the performance when realized covariance, subsampled realized covariance and multivariate realized kernel estimators are used. We study the contribution of the estimators across different sampling frequencies, and show that the multivariate realized kernel and subsampled realized covariance estimators deliver further gains compared to realized covariance estimated on a 5‐minute frequency. In order to show economic and statistical gains, a portfolio of various sizes is used. Copyright © 2016 John Wiley & Sons, Ltd.