基矛MRS—GARCH模型的我国沪深股市指数实证研究

针对传统GARCH模型不能解释股票市场变结构特征的缺陷，结合马尔可夫状态转换模型和标准GARCH模型，提出MRS-GARCH模型，并给出模型的参数估计方法。本文利用沪深300指数的数据对所给模型和方法进行了实证分析，证明MRS-GARCH模型与标准GARCH模型相比，能够更好地描述数据的特征，具有极好的拟合效果，验证了股票市场存在伪持续性的理论。     

中国股市收益波动特征分析

在以往文献中发现用传统的GARCH模型估计收益率序列通常表现出波动具有长记忆性特征和较高的方差持续性，这些特征可以由方差的结构性变点造成。本文采用Chow检验对上证综指收益率序列进行了方差结构性变点的检测，证实了这些结构性变点与影响中国股市收益结构的国内外重大的经济和政治事件相符合采用GARCH模型分段建模，发现了国内、国际的重大经济和政治事件对股市的影响作用。分段建模很好地刻画了我国股票市场的发展过程，各阶段的GARCH模型表明股票市场波动逐渐减缓，市场逐步成熟。     

金融时间序列中加性异常值的鉴别与校正

借鉴Franses and Ghijsel[1](1999)和Charles and Darne[2](2005)提出的鉴别和校正金融序列加性异常值的方法,以GARCH模型为例,对我国的上证综合指数和深圳成分指数进行了加性异常值的鉴定与校正,并对校正后的残差进行了正态检验。结果表明该方法效果显著,进行异常值校正后的GARCH(1,1),更好地拟合金融时间序列中的尖峰厚尾和波动丛聚性的特性,纠正了正态分布的GARCH(1,1)对时间序列拟合的偏误。     

基于MRS-GARCH模型的我国沪深股市指数实证研究

针对传统GARCH模型不能解释股票市场变结构特征的缺陷,结合马尔可夫状态转换模型和标准GARCH模型,提出MRS-GARCH模型,并给出模型的参数估计方法。本文利用沪深300指数的数据对所给模型和方法进行了实证分析,证明MRS-GARCH模型与标准GARCH模型相比,能够更好地描述数据的特征,具有极好的拟合效果,验证了股票市场存在伪持续性的理论。     

基于ARCH族模型的中国外汇市场汇率波动性分析

本文通过对美元/人民币和欧元/人民币的日收益的实证分析,验证了金融时间序列波动的集聚性。然后,在序列满足高阶ARCH效应的基础上,通过对两种汇率收益在GARCH、TARCH和EARCH三种模型下拟合结果的比较,可以发现两者外部冲击的影响、记忆性、好消息和坏消息的影响持续性及杠杆效应程度等方面有差异性也有相似性。最后,根据观察所得,提出相应的政策建议。     

一类变系数动态方程的误差修正研究

建立在时间序列上的模型变量及其参数往往是不变的，本文从经济发展的内在规律和长期趋势出发，对样本观测值序列的若干个阶段进行模型拟合分析，设计变系数动态方程，并对其误差进行拟合修正，为构建一类变系数动态模型提供参考。     

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。     

上证综指基于ARCH模型的VaR风险价值测度分析

现代金融理论中,波动性是金融时间序列最重要的特征之一,因此模拟和预测股票市场的波动性已经成为众多理论和实证研究的重要领域。文章通过选取上证综合指数2008年至2012年的日对数收益率,运用ARCH簇分别在正态分布、t分布和GED分布条件下测度上证综合指数的风险价值VaR,发现上证指数在t分布和GED分布条件下拟合程度较好,存在收益率的厚尾性和波动集聚性特征。     

中国金融发展与贸易开放的变结构协整分析

本文运用变结构协整技术分析了我国金融发展与贸易开放之间的长期均衡及短期动态关系.采用循序检验法确定结构突变点,发现在1994年和2003年金融发展与贸易开放之间的协整关系发生突变；对1978～2007年的数据进行变结构协整检验,得到两段变结构关系,并建立了误差修正模型.结果表明,即便是在相关序列存在结构性突变的情况下,...     

利用GARCH-M类模型和极值理论对上证综指的研究

金融时间序列具有分布的厚尾性、波动的集聚性等特征,传统的方法难以准确的度量其风险。文中运用一种新的估计VaR和ES的方法,即采取两阶段法。首先用GARCH-M类模型(GARCH-M、EGARCH-M和TGARCH-M)拟和原始收益率数据,得到残差序列;第二步用极值分析的方法分析的尾部,最后得到收益率序列的动态VaR和ES。最后对三个模型的计算结果进行比较。     

Limitations of portfolio diversification through fat tails of the return Distributions: Some empirical evidence

This study investigates the level of risk due to fat tails of the return distribution and the changes of tail fatness (TF) through portfolio diversification. TF is not eliminated through portfolio diversification, and, interestingly, the positive tail has declining fatness until a certain level is reached, while the negative tail has rising fatness. This indicates that fat tails are highly relevant to common factors on systematic risk and that the relevance of common factors is higher for the negative tail compared to the positive tail. In the portfolio diversification effect, the declining fatness of the positive tail further reduces risk, but the rising fatness of the negative tail does not contribute to this effect. The asymmetry between the fatness of the positive and negative tails in the return distribution corresponds to the asymmetry of the trade-off relationship between loss avoidance and profit sacrifice that is expected as a consequence of portfolio diversification. Investors use portfolio diversification to reduce their risk of suffering high losses, but following this strategy means sacrificing high-profit potential. Our study provides empirical confirmation for the practical limitation of portfolio diversification and explains why investors with diversified portfolios suffer high losses from market crashes. An examination of the Northeast Asian stock markets of China, Japan, Korea, and Taiwan show identical results.     

The Performance of Structural Change Tests

This paper provides analytical and Monte Carlo studies of the effect of different types of structural change (residual variance, process mean and process persistence) on the performance of the Chow/Wald stability test. We focus on the first-order autoregressive model, which has been used to estimate and assess changes in inflation persistence. Our results show that the autoregressive model is a difficult subject for the Chow test.     

Recent Theoretical Results for Time Series Models with GARCH Errors

This paper provides a review of some recent theoretical results for time series models with GARCH errors, and is directed towards practitioners. Starting with the simple ARCH model and proceeding to the GARCH model, some results for stationary and nonstationary ARMA–GARCH are summarized. Various new ARCH–type models, including double threshold ARCH and GARCH, ARFIMA–GARCH, CHARMA and vector ARMA–GARCH, are also reviewed.     

Stock returns and volatility: Evidence from the Athens Stock market index

This paper investigates the volatility of the Athens Stock excess stock returns over the period 1990–1999 through the comparison of various conditional hetero-skedasticity models. The empirical results indicate that there is significant evidence for asymmetry in stock returns, which is captured by a quadratic GARCH specification model, while there is strong persistence of shocks into volatility.     

Estimation and tests for power-transformed and threshold GARCH models

Consider a class of power-transformed and threshold GARCH(p,q)$(p,q)$ (PTTGRACH(p,q)$(p,q)$) model, which is a natural generalization of power-transformed and threshold GARCH(1,1) model in Hwang and Basawa [2004. Stationarity and moment structure for Box–Cox transformed threshold GARCH(1,1) processes. Statistics & Probability Letters 68, 209–220.] and includes the standard GARCH model and many other models as special cases. We first establish the asymptotic normality for quasi-maximum likelihood estimators (QMLE) of the parameters under the condition that the error distribution has finite fourth moment. For the case of heavy-tailed errors, we propose a least absolute deviations estimation (LADE) for PTTGARCH(p,q)$(p,q)$ model, and prove that the LADE is asymptotically normally distributed under very weak moment conditions. This paves the way for a statistical inference based on asymptotic normality for heavy-tailed PTTGARCH(p,q)$(p,q)$ models. As a consequence, we can construct the Wald test for GARCH structure and discuss the order selection problem in heavy-tailed cases. Numerical results show that LADE is more accurate than QMLE for heavy-tailed errors. Furthermore, the theory is applied to the daily returns of the Hong Kong Hang Seng Index, which suggests that asymmetry and nonlinearity could be present in the financial time series and the PTTGARCH model is capable of capturing these characteristics. As for the probabilistic structure of PTTGARCH(p,q)$(p,q)$ model, we give in the appendix a necessary and sufficient condition for the existence of a strictly stationary solution of the model, the existence of the moments and the tail behavior of the strictly stationary solution.     

GARCH models,tail indexes and error distributions: An empirical investigation

We perform a large simulation study to examine the extent to which various generalized autoregressive conditional heteroskedasticity (GARCH) models capture extreme events in stock market returns. We estimate Hill's tail indexes for individual S&P 500 stock market returns and compare these to the tail indexes produced by simulating GARCH models. Our results suggest that actual and simulated values differ greatly for GARCH models with normal conditional distributions, which underestimate the tail risk. By contrast, the GARCH models with Student's t conditional distributions capture the tail shape more accurately, with GARCH and GJR-GARCH being the top performers.     

Dynamic volatility modelling of Bitcoin using time-varying transition probability Markov-switching GARCH model

Bitcoin (BTC), as the dominant cryptocurrency, has attracted tremendous attention lately due to its excessive volatility. This paper proposes the time-varying transition probability Markov-switching GARCH (TV-MSGARCH) models incorporated with BTC daily trading volume and daily Google searches singly and jointly as exogenous variables to model the volatility dynamics of BTC return series. Extensive comparisons are carried out to evaluate the modelling performances of the proposed model with the benchmark models such as GARCH, GJRGARCH, threshold GARCH, constant transition probability MSGARCH and MSGJRGARCH. Results reveal that the TV-MSGARCH models with skewed and fat-tailed distribution predominate other models for the in-sample model fitting based on Akaike information criterion and other benchmark criteria. Furthermore, it is found that the TV-MSGARCH model with BTC daily trading volume and student-t error distribution offers the best out-of-sample forecast evaluated based on the mean square error loss function using Hansen’s model confidence set. Filardo’s weighted transition probabilities are also computed and the results show the existence of time-varying effect on transition probabilities. Lastly, different levels of long and short positions of value-at-risk and the expected shortfall forecasts based on MSGARCH, MSGJRGARCH and TV-MSGARCH models are also examined.     

金融时序数据建模的模型设定问题分析

W．J．Granger与D．F．Hendry（2004）关于建模思路的对话引起了国际计量经济学界关于模型设定问题的争论，本文就这一问题分析讨论了在金融时序数据实证研究中得以广泛应用的ARCH／GARCH模型的设定问题，认为在金融时序数据的建模中，ARMA族模型不宜作为数据生成过程的模型设定，其统计性质也不能直接扩展到ARMA-GARCH族数据生成过程。虽然ARCH／GARCH族模型作为金融时序数据的生成过程有着良好的统计性质，但不宜单纯采用一般到特殊的建模思路，而应是一般到特殊和特殊到一般两种建模思路的结合。ARCH／GARCH族模型的设定应当包含事前检验、事后检验等设定检验步骤。     

转型时期制度变迁对我国经济增长影响的实证分析

我国现在进行的改革开放必然会引起制度的变迁,而从经济学理论上讲,制度的变迁又会对经济的发展产生巨大影响。文中制度这一因素引入经济增长模型,并量化测度了制度变迁,并进行实证分析。结果表明,改革开放30多年来我国的制度变迁对经济的增长具有巨大的促进作用。而目前,我国的体制改革尚未完成,仍有较大的制度变迁空间,所以制度创新与变革仍然是我国未来较长一段时期内经济增长的重要动力源泉。     

Comovements and asymmetric tail dependence in state housing prices in the USA: A nonparametric approach

We reexamine the methods used in estimating comovements among US regional home prices and find that there are insufficient moments to ensure a normal limit necessary for employing the quasi‐maximum likelihood estimator. Hence we propose applying the self‐weighted quasi‐maximum exponential likelihood estimator and a bootstrap method to test and account for the asymmetry of comovements as well as different magnitudes across state pairs. Our results reveal interstate asymmetric tail dependence based on observed house price indices rather than residuals from fitting autoregressive–generalized autoregressive conditional heteroskedasticity (AR‐GARCH) models.