On the asymmetric impact of macro–variables on volatility

We extend the GARCH–MIDAS model to take into account possible different impacts from positive and negative macroeconomic variations on financial market volatility: a Monte Carlo simulation which shows good properties of the estimator with realistic sample sizes. The empirical application is performed on the daily S&P500 volatility dynamics with the U.S. monthly industrial production and national activity index as additional (signed) determinants. We estimate the Relative Marginal Effect of macro variable movements on volatility at different lags. In the out-of-sample analysis, our proposed GARCH–MIDAS model not only statistically outperforms the competing specifications (GARCH, GJR-GARCH and GARCH–MIDAS models), but shows significant utility gains for a mean-variance investor under different risk aversion parameters. Attention to robustness is given by choosing different samples and estimating the model in an international context (six different stock markets).     

Bayesian time series analysis of periodic behaviour and spectral structure

Analysis, model selection and forecasting in univariate time series models can be routinely carried out for models in which the model order is relatively small. Under an ARMA assumption, classical estimation, model selection and forecasting can be routinely implemented with the Box–Jenkins time domain representation. However, this approach becomes at best prohibitive and at worst impossible when the model order is high. In particular, the standard assumption of stationarity imposes constraints on the parameter space that are increasingly complex. One solution within the pure AR domain is the latent root factorization in which the characteristic polynomial of the AR model is factorized in the complex domain, and where inference questions of interest and their solution are expressed in terms of the implied (reciprocal) complex roots; by allowing for unit roots, this factorization can identify any sustained periodic components. In this paper, as an alternative to identifying periodic behaviour, we concentrate on frequency domain inference and parameterize the spectrum in terms of the reciprocal roots, and, in addition, incorporate Gegenbauer components. We discuss a Bayesian solution to the various inference problems associated with model selection involving a Markov chain Monte Carlo (MCMC) analysis. One key development presented is a new approach to forecasting that utilizes a Metropolis step to obtain predictions in the time domain even though inference is being carried out in the frequency domain. This approach provides a more complete Bayesian solution to forecasting for ARMA models than the traditional approach that truncates the infinite AR representation, and extends naturally to Gegenbauer ARMA and fractionally differenced models.     

U.S. Virgin Islands National Park:

No abstract available for this article.     

Output Expectations and Forecasting of UK Manufacturing

This paper analyses both quarterly data from the Confederation of British Industry (CBI) Survey on respondents’ expectations of recent and forthcoming manufacturing output and monthly Office of National Statistics (ONS) figures on actual manufacturing output within the UK. Quarterly output expectations of the CBI manufacturers are explained from the monthly ONS observations using a bounded rationality approach. The logistic formulation models the diffusion process across respondents. There is a backward-looking CBI Survey perspective, explained by past ONS observations, and a forward-looking perspective, explained from future ONS statistics. Also, the forecasting of monthly manufacturing output from earlier values, along with the quarterly CBI Survey information, is examined and tested against the alternative Pesaran/Thomas method. The study provides econometric evidence for the validity of the logistic model and shows that bounded rationality can explain the formation of predictions among business managers in the UK manufacturing sector. The emerging consensus from the literature, supported by this paper, is that the logistic format is a superior approximation to the true data generating process compared with the earlier standard Anderson/Pesaran/Thomas approach. An adjustment to the Survey is used, which achieves perfect symmetry with up and down versions of the data. The benefits of this adjustment are tested in the forecasting section.

干旱区内陆河流域未利用地开发生态风险评价及预测*——以开都河流域为例

[目的]探析开都河流域在未利用地开发过程中生态风险指数的变化特征,为西北干旱区内陆河流域土地利用结构调整与生态保护修复协调发展提供建议。[方法]文章采用PSR模型构建基于14个指标框架的流域未利用地开发生态风险评价指标体系;通过测度综合生态风险指数法进行时空视角的特征变化与格局划分评价;并运用灰色预测模型前瞻性模糊预测该区域未来4年的生态风险变化态势。[结果]2009—2016年开都河流域未利用地开发生态风险整体呈波动上升趋势,生态风险程度由较低下降至低生态风险水平,随后上升至一般程度。这是因为土地开发利用对生态环境造成压力,但在政府相应生态保护政策的出台落实下又逐步缓解,生态系统结构和功能好转明显,抵御风险能力得以提升。预测结果显示2017—2020年开都河流域生态风险将由一般生态风险程度上升至较高程度,因此需要采取适当的管理措施来消减生态风险发生的可能性。[结论]开都河流域作为沙漠中典型的绿洲生态系统,生态环境较为脆弱,通过未利用地的差别化开发、鼓励零星分散的开发模式以及细分不同地类开发的生态补偿设置等方式路径,以期缓解降低干旱区内陆河流域未利用地开发带来的生态风险。