Information Flows across Markets: Evidence from China–Backed Stocks Dual–Listed in Hong Kong and New York

Using a bivariate generalized autoregressive conditional heteroskedasticity (GARCH) model, we examine patterns of information flows for China–backed stocks that are cross–listed on exchanges in Hong Kong and New York. Results analyzing the dual–listed stocks indicate significant mutual feedback of information between domestic (Hong Kong) and offshore (New York) markets in terms of pricing and volatility. Stocks listed on the domestic market appear to play a more significant role of information transmission in the pricing process, whereas stocks listed on the offshore market play a bigger role in volatility spillover.     

The relevance of currency risk in the EMU

We investigate how the elimination of intra-European exchange risk may affect international financial markets using a conditional version of the International CAPM. We estimate the EMU and non-EMU components of aggregate currency risk and document significant exposures to both. The premium for EMU risk is positive and associated with exposure to the French, Italian and Spanish currencies. The premium for non-EMU risk is consistently negative and accounts for most of the aggregate currency premiums. In the 1990s, exposures to EMU risk declined significantly while exposures to non-EMU risk increased. Hence the adoption of the Euro is unlikely to have a large impact on aggregate currency risk premiums.     

On the forecastability of share prices on the Viennese stock exchange

In this paper the Viennese stock exchange data are analysed by using ARMA and GARCH technology. After using AIC and BIC for estimating the linear structure of the time series, to the resulting innovations a GARCH(1,1) model is fit. The resulting residuals are then tested for serial independence and constancy of its distribution to check whether the models are reasonable. Main result is that the residuals of this ARMA-GARCH(1,1)-model are reasonably iid (which is checked by BDS and classical independence tests) for index data and significantly less well-behaved for stock data. Second, there is considerable autocorrelation in the data (especially in the Viennese indices WBK and ATX) which can be exploited even with 1.25% transaction costs (which is checked by a posteriori analysis of a strategy which exploits an underlying time-varying AR(1) model), however, much higher profit can be made with 0.5% transaction costs. Furthermore, the same techniques are applied to US Standard & Poor 500 index and the results for both data sets are compared giving the result that the US-market looks much more mature than the Viennese one.Financial Support by the Institute for Advanced Studies, Vienna, and the Fonds zur Förderung der wissenschaftlichen Forschung, Vienna, Grant P 9176 is gratefully acknowledged. This paper is a slightly abbreviated version of the Research Report No. 135 by the same authors (see References), which contains many detailed plots of the results.     

服务出口与实际汇率动态关系分析:基于美国数据的考察

基于代表性家庭追求效用最大化,文中构建了一个由外国实际收入、汇率水平、货物出口以及经GARCH(1,1)模型估计所得条件方差作为汇率风险代理变量组成的服务出口方程.采用自回归分布滞后估计方程参数,并将汇率变动影响货物出口进而传导至服务出口的间接效应纳入分析框架.估计结果显示,汇率水平变动与服务出口之间存在负相关关系,而...     

基于百度指数的碳交易市场波动率的实证研究

碳交易市场波动率研究主要基于成交量数据对收益率GARCH效应的解释作用。在互联网时代可以有更新更为有效的方法来衡量碳交易市场的波动性。基于“碳交易”词条的百度指数,以湖北碳交易市场中的收益率为样本,本文通过对比使用引入成交量和搜索量的IGARCH（1,1）模型,实证研究发现传统的量价方程的确没有解释力,而百度指数可以对收益率的GARCH效应做出部分合理的解释,这可以在某种程度上反映湖北碳交易市场的交易信息流。     

Detecting nonlinearity in time series by model selection criteria

This article analyzes the use of model selection criteria for detecting nonlinearity in the residuals of a linear model. Model selection criteria are applied for finding the order of the best autoregressive model fitted to the squared residuals of the linear model. If the order selected is not zero, this is considered as an indication of nonlinear behavior. The BIC and AIC criteria are compared to some popular nonlinearity tests in three Monte Carlo experiments. We conclude that the BIC model selection criterion seems to offer a promising tool for detecting nonlinearity in time series. An example is shown to illustrate the performance of the tests considered and the relationship between nonlinearity and structural changes in time series.     

Forecasting the volatility of asset returns: The informational gains from option prices

A new class of forecasting models is proposed that extends the realized GARCH class of models through the inclusion of option prices to forecast the variance of asset returns. The VIX is used to approximate option prices, resulting in a set of cross-equation restrictions on the model’s parameters. The full model is characterized by a nonlinear system of three equations containing asset returns, the realized variance, and the VIX, with estimation of the parameters based on maximum likelihood methods. The forecasting properties of the new class of forecasting models, as well as a number of special cases, are investigated and applied to forecasting the daily S&P500 index realized variance using intra-day and daily data from September 2001 to November 2017. The forecasting results provide strong support for including the realized variance and the VIX to improve variance forecasts, with linear conditional variance models performing well for short-term one-day-ahead forecasts, whereas log-linear conditional variance models tend to perform better for intermediate five-day-ahead forecasts.     

GARCH族模型的预测能力比较:一种半参数方法

半参数GARCH模型无须设定条件分布的具体形式。本文首先将一种效率较高、易于实施的半参数方法——估计函数方法应用于10类常见的GARCH结构,并给出证据,显示该方法能显著提高GARCH族模型的波动率预测绩效。然后,应用估计函数方法,较为全面地比较各类GARCH结构的预测能力。为给出统计意义下的结果,并减少数据窥察问题,研究中分别使用OLS和SPA检验法进行绩效评价。结果发现,与其他GARCH类结构相比,EGARCH和APARCH模型能够较好地描述股市收益率的波动过程。     

Quantile cointegrating regression

Quantile regression has important applications in risk management, portfolio optimization, and asset pricing. The current paper studies estimation, inference and financial applications of quantile regression with cointegrated time series. In addition, a new cointegration model with quantile-varying coefficients is proposed. In the proposed model, the value of cointegrating coefficients may be affected by the shocks and thus may vary over the innovation quantile. The proposed model may be viewed as a stochastic cointegration model which includes the conventional cointegration model as a special case. It also provides a useful complement to cointegration models with (G)ARCH effects. Asymptotic properties of the proposed model and limiting distribution of the cointegrating regression quantiles are derived. In the presence of endogenous regressors, fully-modified quantile regression estimators and augmented quantile cointegrating regression are proposed to remove the second order bias and nuisance parameters. Regression Wald tests are constructed based on the fully modified quantile regression estimators. An empirical application to stock index data highlights the potential of the proposed method.     

成交量、持仓量对我国大豆类期货价差波动影响的实证分析

采用GARCH(1,1)模型就成交量、持仓量对大豆类期货价差波动率的影响进行实证分析,结果显示：当期成交量、持仓量对大豆期货价差波动的整体影响是显著的;滞后成交量、持仓量对大豆期货价差波动的整体影响也是显著的;当成交量、持仓量同时进入条件方差方程时,它们对大豆类期货价差波动的影响整体上也是显著的。这一结论揭示了我国大豆期货市场信息传递过程,验证了我国大豆期货市场的信息非有效性,对期货市场投资者以及期货市场监管者具有一定的借鉴意义。