证券市场的混沌现象分析

通过对沪深两市股指收益率序列中Lyapunov指数和吸引子的分数维的计算分析,证实了我国证券市场中股价指数波动具有明显的混沌特性。由于混沌本身具有对初值的极其敏感性,以及其局部的随机性与全局的决定性的特征,股价变化的背后存在着某种决定性的支配规则。对此,传统的非线性理论无法准确进行描述,需要运用混沌理论来描述证券市场价格波动,探索影响证券市场的内在机理,并预测证券市场价格波动的未来走向。     

技术交易规则预测能力与收益率动态过程——基于Bootstrap方法的实证研究

本文对我国股票市场技术交易规则预测能力进行了实证检验,发现移动平均规则所产生的买入区间收益率更大而波动率却更小,卖出区间的收益率为负而波动率却更大。运用自举(Bootstrap)方法检验发现,四种常用的收益率线性模型均不能解释买卖出区间收益率与波动率所表现出的非对称现象,尤其无法解释卖出区间收益率为负的现象。为此,本文通过人工神经网络方法,将条件异方差结构引入到现有的收益率非线性模型,发现该模型能更好地解释买卖出区间收益率与波动率模式,表明收益率动态过程中存在非线性特征。     

经济周期与证券市场波动关联性——基于向量SWARCH模型的新证据

本文认为关于经济周期与证券市场波动关联性研究结论的分歧源自仅注重样本区间内整体关联性的检验,忽视了分析经济增长不同阶段与证券市场波动的特定关联性。基于向量SWARCH模型,本文实证检验了我国GDP增长率与证券收益率间的关联性,结论表明,虽然“整体关联性”检验不支持经济周期与市场波动间存在显著相关性的结论,但“状态相关系数”却显示两者间的关联性具有“区制转移”特征,并体现了对前者依赖的“门限效应”和“非对称效应”。     

新企业会计准则中公允价值变动损益对新能源企业股价的影响分析

发展新能源产业是应对环境恶化的重要举措之一。本文选取我国上市公司中395家新能源企业在2007-2009年间的季报数据,运用面板数据分析方法,检验了公允价值变动损益与这类概念股市价的收益率及其波动率之间的相关性,结果表明新会计准则实施以来公允价值变动损益与股价收益率显著正相关,而于收益率的波动显著负相关,进一步表明公允价值变动损益具有信息含量,且并不会增大资本市场的波动风险。     

我国股票市场有效性的研究

本文从有效市场假设出发,重点研究我国股票市场的有效程度。本文作者通过抽样统计,对我国股票市场的有效程度进行了实证分析。先运用序列相关检验的自回归模型对市场指数收益率数据进行检验,得出了我国股票市场处于弱式有效水平的结论;然后引入事件研究法,通过对超常收益率的测算检验市场是否达到半强式有效,进而阐述如下观点:我国股票市场目前已达到弱式有效水平,但并不具有半强型有效市场的特点。     

对中国股市有效性及波动性的实证检验

利用最近几年的数据和EVIEWS软件分析了中国证券市场的有效性及波动性,使用经典线性回归方程做了时间序列回归和横截面回归。并且依次放松假设,采用WHITE检验和Glejser检验分析了股票收益率的异方差问题;采用DW检验及Breusch-Godfrey检验分析了股票收益率的自相关问题等;还采用DF检验了时间序列的平稳性。接着对股市的弱式有效假说予以检验,最后使用ARCH、GARCH模型对我国股市做波动性检验。     

基于GARCH模型的股市收益与股指波动的实证研究

文章采用四个市场指数建立以来至2010年12月30日止,运用传统的最小二乘法和改进的自回归条件异方差模型( GARCH),从A股市场指数的波动性入手,研究四个市场收益率的特征,对指教序列的分布、序列的平稳性和异方差进行检验,从而对A股市场指教的波动有更深刻的认识和把握.     

中国股票市场波动的统计特征分析

本文从股票价格行为与收益率的变化来描述中国股市波动。首先分析了股价波动状况；接着运用均值、标准差、偏度及峰度等描述性统计变量对股票收益率波动的基本统计特征进行分析；最后检验了收益率序列的自相关性、平稳性与正态性。     

上海期货交易所的涨跌停板制度对波动性的影响

本文通过对上海期货交易所的三个品种的涨跌停板制度进行检验,检验方法为:从收益率所拟和的ARMA模型中滤出残差,进行波动率的GARCH模型回归。波动率模型中加入了哑元变量来体现涨停板对后一日波动的影响。实证结果显示,铜、铝、天然橡胶的涨跌停板本应显著地使收益率的波动率减小的作用未检验出,相反却得到涨停板使三个品种显著波动率增大的检验结果。是否需要扩大涨跌停板,提高市场效率?检验结果带给我们如何使涨跌停板制度趋于合理化的思考。     

上海证券市场混沌特征分析

文章利用Dayid Ruelle提出的相空间重构技术和Wolf算法计算出了上证综合指数日收益率的Lyapunov指数,利用P-Grassberger和I·Procaccla提出的时间序列关联维数的G-P算法计算出上证综指日收益率序列的关联维数。得出上海证券市场具有明显的非线性混沌特征的结论。     

Time series properties of ARCH processes with persistent covariates

We investigate the time series properties of a volatility model, whose conditional variance is specified as in ARCH with an additional persistent covariate. The included covariate is assumed to be an integrated or nearly integrated process, with its effect on volatility given by a wide class of nonlinear volatility functions. In the paper, such a model is shown to generate many important characteristics that are commonly observed in financial time series. In particular, the model yields persistence in volatility, and also well predicts leptokurtosis. This is true for any type of volatility functions considered in the paper, as long as the covariate is integrated or nearly integrated. Stationary covariates cannot produce important characteristics observed in many financial time series. We present two empirical applications of the model, which show that the default premium (the yield spread between Baa and Aaa corporate bonds) affects stock return volatility and the interest rate differential between two countries accounts for exchange rate return volatility. The forecast evaluation shows that the model generally outperforms GARCH and FIGARCH at relatively lower frequencies.     

Sources of volatility persistence: A case study of the U.K. pound/U.S. dollar exchange rate returns

This paper utilizes a new approach to examine the inherent nonlinear dynamics of the exchange rate returns volatility. Specifically, we utilize a regime switching threshold (i) generalized autoregressive conditional heteroskedasticity (RS-TGARCH) and (ii) a fractional generalized autoregressive conditional heteroskedasticity (RS-TFIGARCH) model. The RS-TGARCH model is found to be adequate in analyzing the first two moments of the U.K. pound/U.S. dollar monthly exchange rate returns series. The RS-TFIGARCH is found to be adequate for the daily returns series. The volatility persistence and leverage effects associated with exchange rate returns series are jointly tested by means of a Wald Chi-square test.     

Forecasting cryptocurrency volatility

This paper studies the behavior of cryptocurrencies’ financial time series, of which Bitcoin is the most prominent example. The dynamics of these series are quite complex, displaying extreme observations, asymmetries, and several nonlinear characteristics that are difficult to model and forecast. We develop a new dynamic model that is able to account for long memory and asymmetries in the volatility process, as well as for the presence of time-varying skewness and kurtosis. The empirical application, carried out on 606 cryptocurrencies, indicates that a robust filter for the volatility of cryptocurrencies is strongly required. Forecasting results show that the inclusion of time-varying skewness systematically improves volatility, density, and quantile predictions at different horizons.     

Testing for linear and nonlinear granger causality in the stock price-volume relation: Korean evidence

In this paper, linear and nonlinear Granger causality tests are used to examine the dynamic relationship between daily Korean stock returns and trading volume. We find evidence of significant bidirectional linear and nonlinear causality between these two series. ARCH-ype models are used to examine whether the nonlinear causal relations can be explained by stock returns and volume serving as proxies for information flow in the stochastic process generating volume and stock returns respectively. After controlling for volatility persistent in both series and filtering for linear dependence, we find evidence of nonlinear bidirectional causality between stock returns and volume series. The finding of strong bidirectional stock price-volume causal relationships implies that knowledge of current trading volume improves the ability to forecast stock prices. This evidence is not supportive of the efficient market hypothesis. Another finding is that the nonlinear relationship is sensitive to institutional, organizational, and structural factors. The results of this study should be useful to regulators, practitioners and derivative market participants whose success precariously depends on the ability to forecast stock price movements.     

Impact of economic growth volatility on income inequality: ASEAN experience

The study attempts to examine the symmetric and the asymmetric impact of volatility of economic growth on the inequality of income in the major ASEAN economies over the period 1980–2015. Financial development, trade openness as a proxy of globalization, inflation, human capital formation, and fiscal policy are utilized as major control variables. The paper tries to explore the causal association between inequality of income distribution and economic growth volatility, exploring simultaneously the long-run association and the short-run dynamics in the time series structure. The study applied Clemente–Montanes–Reyes unit root test to identify the structural break in the time series. Further, the cointegrating relationship of the time series observations was explored by applying the ARDL (linear) bounds test approach along with the nonlinear ARDL for making fruitful comparisons in the long-run relationship among the variables. The countries chosen are Malaysia, Indonesia, Thailand, Singapore and The Philippines. The empirical findings strongly suggest a long-run cointegrating relationship between income inequality and growth volatility with a positive and statistically significant impact. Also, the causality analysis was explored using the Toda and Yamamoto (1995) method of Granger causality. The causality test shows that there exists bidirectional causality from inequality transmission to economic growth volatility. The implications that are developed from this study helps us to understand the various policy reforms in the ASEAN region, that are more transparent and can make these economies less susceptible to risks.     

Short-term spot rate models with nonparametric deterministic drift

Most studies assume stationarity when testing continuous-time interest-rate models. However, consistent with Bierens [Bierens, H. (1997). Testing the unit root with drift hypothesis against nonlinear trend stationary, with an application to the US price level and interest rate. Journal of Econometrics, 81, 29–64; Bierens, H. (2000). Nonparametric nonlinear co-trending analysis, with an application to interest and inflation in the United States. Journal of Business and Economics Statistics, 18, 323–337], our nonparametric test results support nonlinear trend stationarity. To accommodate nonstationarity, we detrend the interest-rate series and re-examine a variety of continuous-time models. The goodness-of-fit improves significantly for those models with drift-induced mean reversion and worsens for those with high volatility elasticity. The inclusion of a nonparametric trend component in the drift significantly reduces the level effect on the interest-rate volatility. These results suggest that the misspecification of the constant elasticity model should be attributed to the nonlinear trend component of the short-term interest-rate process.     

On economic uncertainty,stock market predictability and nonlinear spillover effects

This paper uses a k-th order nonparametric Granger causality test to analyze whether firm-level, economic policy and macroeconomic uncertainty indicators predict movements in real stock returns and their volatility. Linear Granger causality tests show that whilst economic policy and macroeconomic uncertainty indices can predict stock returns, firm-level uncertainty measures possess no predictability. However, given the existence of structural breaks and inherent nonlinearities in the series, we employ a nonparametric causality methodology, as linear modeling leads to misspecifications thus the results cannot be considered reliable. The nonparametric test reveals that in fact no predictability can be observed for the various measures of uncertainty i.e., firm-level, macroeconomic and economic policy uncertainty, vis-à-vis real stock returns. In turn, a profound causal predictability is demonstrated for the volatility series, with the exception of firm-level uncertainty. Overall our results not only emphasize the role of economic and firm-level uncertainty measures in predicting the volatility of stock returns, but also presage against using linear models which are likely to suffer from misspecification in the presence of parameter instability and nonlinear spillover effects.     

Understanding the conditional out-of-sample predictive impact of the price of crude oil on aggregate equity return volatility

Using monthly data from 1973 through 2020, we explore whether it is possible to improve the accuracy of one-month ahead log-aggregate equity return realized volatility point forecasts by conditioning on various nonlinear crude oil price measures widely relied on in the literature. When evaluating the evidence of unconditional relative equal predictive ability as specified in Diebold and Mariano (1995), we observe that similar to well-known economic variables, such as the dividend yield, the default yield spread and the rate of inflation, we rarely observe evidence of statistical gains in relative point forecast accuracy in favor of the crude oil price-based models. However, when evaluating the evidence of conditionalrelative equal predictive ability as specified in Giacomini and White (2006), we observe that contrary to well-known economic predictors, certain nonlinear crude oil price variables, such as the one-year net crude oil price increase suggested in Hamilton (1996) offer sizable point forecast accuracy gains relative to the benchmark. These statistical gains can also be translated into economic gains.     

A General Approach to Testing Volatility Models in Time Series

Volatility models have been playing important roles in economics and finance. Using a generalized spectral second order derivative approach, we propose a new class of generally applicable omnibus tests for the adequacy of linear and nonlinear volatility models. Our tests have a convenient asymptotic null N(0,1) distribution, and can detect a wide range of misspecifications for volatility dynamics, including both neglected linear and nonlinear volatility dynamics. Distinct from the existing diagnostic tests for volatility models, our tests are robust to time-varying higher order moments of unknown form (e.g., time-varying skewness and kurtosis). They check a large number of lags and are therefore expected to be powerful against neglected volatility dynamics that occurs at higher order lags or display long memory properties. Despite using a large number of lags, our tests do not suffer much from the loss of a large number of degrees of freedom, because our approach naturally discounts higher order lags, which is consistent with the stylized fact that economic or financial markets are affected more by the recent past events than by the remote past events. No specific estimation method is required, and parameter estimation uncertainty has no impact on the convenient limit N(0,1) distribution of the test statistics. Moreover, there is no need to formulate an alternative volatility model, and only estimated standardized residuals are needed to implement our tests. We do not have to calculate tedious and model-specific score functions or derivatives of volatility models with respect to estimated parameters, which are required in some existing popular diagnostic tests for volatility models. We examine the finite sample performance of the proposed tests. It is documented that the new tests are rather powerful in detecting neglected nonlinear volatility dynamics which the existing tests can easily miss. They are useful diagnostic tools for practitioners when modelling volatility dynamics.